bioinformatics thesis ideas

BSc and MSc Thesis Subjects of the Bioinformatics Group

On this page you can find an overview of the BSc and MSc thesis topics that are offered by our group. The procedure to find the right thesis project for you is described below.

MSc thesis: In the Bioinformatics group, we offer a wide range of MSc thesis projects, from applied bioinformatics to computational method development. Here is a list of available MSc thesis projects . Besides the fact that these topics can be pursued for a MSc thesis, they can also be pursued as part of a Research Practice .

BSc thesis: As a BSc student you will work as an apprentice alongside one of the PhD students or postdocs in the group. You will work on your own research project, closely guided by your supervisor. You will be expected to work with several tools and/or databases, be creative and potentially overcome technical challenges. Below you will find short descriptions of the research projects of our PhDs and Postdocs. In addition you can take a look at the list of MSc thesis projects above.

Procedure for WUR students:

• Request an intake meeting with one of our thesis coordinators by filling out the MSc intake form or BSc intake form and sending it to [email protected]
• Contact project supervisors to discuss specific projects that fit your background and interest
• Upon a match, take care of the required thesis administration together with your supervisor(s) and enroll in the thesis BrightSpace site to find more information on a thesis in the Bioinformatics group

Procedure for non-WUR students or students in other non-standard situations: We have limited space for interns from other institutes. If you are interested, please email our thesis coordinators at [email protected]; please attach your CV and indicate what are your main research interests.

BSc thesis topics

Integrative omics for the discovery of biosynthetic pathways in plants, molecular function prediction of natural products, linking the metabolome and genome, linking metagenomics and metatranscriptomics to study the endophytic root microbiome, exploiting variation in lettuce and its wild relatives.

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General Information There are plenty of opportunities for Bioinformatics research projects at UCLA. This program is designed to help interested students find research projects related to Bioinformatics across campus. Typically, these projects are for credit; in exceptional circumstances they may offer funding. Participation in research projects can both significantly improve your chances of admittance into top graduate programs and make you a much more competitive employment candidate. Even better, it gives you something to talk about during an interview. Feel free to contact us even if you do not know exactly whether or not you want to work on a research project or know the field you wish to research in. Please remember that every undergraduate and masters student is welcome to participate in research, regardless of your background or year in the program. Undergraduates are STRONGLY encouraged to participate in research as early as possible in their careers. Ideally, you should start a research project during your sophomore year, but it is never too late or to early to start! Undergraduate students may receive up to 8 units credit toward the minor with enrollment in Computer Science 194/199 or Bioinformatics 194/199.

General Procedure If you are reasonably sure which project you would like to work on, use the contact information listed under the project to contact the person responsible for the project directly to set up a meeting. If you are not sure, but you are even slightly interested in research, feel free to email us or drop in to help chose an appropriate project. Most students take a project for course credit, although funding may be available in some cases. You can contact Eleazar Eskin (eeskin [at] cs [dot] ucla [dot] edu) if you have any questions.

Research Projects Below is a list of research projects that are accepting undergraduate researchers.

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• Proquest Dissertations and Theses Search US theses and dissertations. Accessed through OxLip+, search for 'dissertations and theses'.
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Theses and Dissertations On-line

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A number of recent theses and dissertations prepared at Oxford are available to download from the Oxford Research Archive (ORA) . The British Library provides access to UK theses through its EThOS service . Already digitised UK theses can be downloaded freely as PDF files. Requests can be made to digitise older theses, but there is a cost of around £40 and waiting time of 30 days for digitisation. The British Library no longer provides theses on microfilm.

Finding Oxford Theses

SOLO allows you to search for Theses in the Oxford collections.

1. Navigate to the  SOLO  homepage.

2. Click on the ' Advanced Search ' button

3. Click the ' Resource Type ' menu and choose the ' Theses ' option.

4. Type in the title or author of the thesis you are looking for and click the ' Search ' button.

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Diploma / Master / Bachelor theses and Projects

In the following we list available, currently processed, and finished theses and student projects. When looking for a topic, please check not only the available topics but also the processed and recently finished topics. There might be unannounced but available follow up theses or projects that are not yet announced. So if you find a topic interesting, please contact the corresponding supervisor for further information. Bioinformatics is a highly specialized application area of computer science and biology and to successfully solve research questions in this field, you require a lot of interdisciplinary knowledge. Therefore, to do a Master thesis with us, we have the minimum requirement that you have attended one of our teaching courses . We may also ask you to present an introductory talk about your chosen topic (given material provided by us) before we can accept you. This does not apply to Bachelor theses or projects.

Open Topics

Approximative iterative prediction of complex non-nested rna structures.

The structure of RNA molecules is typically studied in a simplified graph model that represents the formed intra-molecular interactions, i.e. base pairings. Due to computational complexity, such RNA structure models are typically restricted to nested base pairing models that can be visualized by a non-crossing planar graph. Such models were shown to cover the majority of structure defining base pairs and are thus often sufficient to do biologically relevant studies. Nevertheless, there is a large class of RNAs where the final structure is defined by the formation of non-nested base pairs, i.e. base pairs that have to be represented by crossing lines within a planar graph. Algorithms that consider such pairings often have a time complexity of O(n^5) or more depending on the imposed restrictions in which context crossing base pairs are considered. Thus, they are not feasible to be applied to long RNA molecules or in large scale studies. Within this project we want to tackle this problem with an iterative scheme of structure prediction. That is we will apply structure and interaction prediction approaches to predict nested and crossing structure elements in an hierarchical approach. While this will not necessarily identify the optimal crossing structure, it provides a most general model of crossing structure formation utilizing the speed of nested structure prediction approaches.

Port a raw read pipeline for microbiome data analysis to Galaxy

Microbiome is the collection of all microbes, such as bacteria, fungi, viruses, along with their genes, which live inside and outside our bodies in all environments surrounding us. To investigate microbiomes, researchers use sequencing data and microbiome analyses. These analyses rely on sequencing data to investigate microbiomes. Such analysis relies on sophisticated computational approaches: assembly, binning, taxonomic classification, functional profiling etc. Analyzing microbiome data makes it possible to answer the two main questions for most microbiome analysis. Who (microorganisms) are there: by extracting the community from the microbiome reads What are they doing (and how): by extracting the gene/pathway abundance profile from the metagenomics reads and transcript abundance profiles from the metatranscriptomics reads and combining them These analyses rely on bioinformatics tools and also databases. Few workflows to process this data are available and most are not openly available, not transparent, or not easy to use by researchers. To tackle this problem, the Freiburg Galaxy team together with the microGalaxy community use Galaxy to build workflows to analyze microbiome sequencing data.

Project context: MGnify offers an automated pipeline for the analysis and archiving of microbiome data to help determine the taxonomic diversity and functional & metabolic potential of environmental samples. The pipeline even if documented is not really usable outside their resources. We would like to offer this pipeline for Galaxy users. This project aims to port the raw reads part of the pipeline into Galaxy. More information about the project can be found here: https://github.com/usegalaxy-eu/project-ideas/issues/31

CRISPR accessory proteins

The CRISPR-Cas system is an adaptive immune system in many archaea and bacteria, which provides resistance against invading genetic elements. The three major components of CRISPR-Cas systems are CRISPR-array, leader sequence and Cas genes. A recent study[1] demonstrated that there are proteins adjacent to the Cas proteins that help the CRISPR-Cas to switch targeting and degrading. This work aims to cluster/classify all the accessory proteins based on the associated Cas proteins. To do this, you will use the method from [1] to identify and analysis clusters. Project Outline - Start scanning all archaeal and bacterial genomes that have a CRISPR-Cas system. - Extract the up-and-downstream flanking genes of each CRISPR-Cas system. - Classify the genes according to different conditions and find clusters concerning locations and functions. [1] https://www.tandfonline.com/doi/full/10.1080/15476286.2018.1483685

Implementing new features for RNA-RNA interaction prediction

Our group develops the tool IntaRNA , which is one of the state-of-the-art programs for RNA-RNA interaction prediction. We are continously extending the tool (c++11, boost, autotools, openmp) that is hosted on Github BackofenLab/IntaRNA . Within the development process, we offer various student projects covering different aspects of the project. For a list of open topics, please refer to "student project" marked issues @github . If you are interested, please contact Martin Raden . Most topics can be adapted to be suitable for a student project, bachelor, or master thesis.

Docker based RNA-analysis workbench

You are interested in bleeding edge Linux-Kernel-Technologdy and virtualization? You want to help to distribute software packages in a OS-independend way? Than you can help us to solve the deployment problems of scientific software in a general way. That project will use Docker [1], an open source project that automates the deployment of applications, to produce self-contained images (containers). These containers are OS independent, versioned (like a git-history) and easy to use, which enables reproducibility of research results and easy deployment of entire software stacks. Prerequisites: Linux/Unix, Bash, autotools [1] https://www.docker.io Team-Project: can be combined with the "Graph visualization framework" and the "Galaxy Tool integration" project

Galaxy Tool integration

Galaxy is an open, web-based platform for data intensive research. The University of Freiburg is running a Galaxy server to serve all different needs of our researchers. In addition to the common Next-Gerneration-Sequencing Tools, we offer Tools for cheminformatics [1], proteomics and RNA bioinformatics. To integrate an apllication into Galaxy, a thin wrapper between the Galaxy API and the targeted application needs to be written. Here usability is key. Good wrappers are easy to use and abstracting complicated application details. As part of our Galaxy project we are permanently seeking for motivated tool-wrappers that are enthusiastic about usability, want to work with a vibrant community to make Bio- & Cheminformatik Tools accessible for more researchers. The overall aim is to put the developed wrapper in the Galaxy Tool Shed [2], a Galaxy Appstore, where everyone can get there favorite application with a few mouse clicks. Prerequisites: XML, Bash, autotools, Python [1] https://github.com/bgruening/galaxytools/tree/master/chemicaltoolbox [2] https://wiki.galaxyproject.org/Tool%20Shed Team-Project: can be combined with the "Graph visualization framework" and the "Docker" project

Interactive molecule design based on graph grammars

Further topics from the galaxy team.

The Freiburg Galaxy team is hosting further project ideas in its own GitHub repo. You want to work on NGS, big-data analysis, Cloud- or HPC-computing or develop complex front- end backends have a look at the topics in the link below: https://github.com/bgruening/project-ideas/issues

Further topics concerning CRISPR research

Further topics ....

Further Topics are available on request. If you have a suggestion for a topic you are interested in, do not hesitate to contact us. Otherwise, the completed theses may lead you.

Topics in Progress

Automated web crawling for publications using compensatory mutation experiments.

Compensatory mutation experiments provide the most reliable proof of specific inter-molecular base pairs formed by RNA-RNA interactions. They provide proof that specific base pairs are part of mechanism that is based on the formation of an RNA-RNA interaction. Such experiments are expensive both w.r.t. time and resources and thus often part of the methodology of research projects that try to unwind specific molecular biological mechanisms. Thus, the experiments are often "only" one step in a longer list of experiments to gather proof for a projects hypothesis and therefore only described within the main text of the manuscript. Since publication search engines typically only parse and index title and abstract information of published articles, identifying publications that involve compensatory mutations is not easily done. In order to better understand the details of RNA-RNA interactions, e.g. to improve prediction algorithms or to design new ones, compensatory mutation experiment data would be most beneficial. This projects aims at the development of a web crawling tool to systematically identify publications that provide such experimental details.

Design of a data base and respective user front ends to collect and browse compensatory mutation experiments

Compensatory mutation experiments provide the most reliable proof of specific inter-molecular base pairs formed by RNA-RNA interactions. They provide proof that specific base pairs are part of mechanism that is based on the formation of an RNA-RNA interaction. Such experiments are expensive both w.r.t. time and resources and thus often part of the methodology of research projects that try to unwind specific molecular biological mechanisms. Experimental details are often only presented in form of illustrative images or use non-uniform textual encodings. Thus, the extraction of such information is typically manually done. In order to better understand the details of RNA-RNA interactions, e.g. to improve prediction algorithms or to design new ones, compensatory mutation experiment data would be most beneficial. This projects aims at the development of a data base scheme suited to store experimental details and respective meta data. To help in the manual encoding and reviewing of compensatory mutations, an interactive user front end is to be developed.

Visualizing the effect of homo-dimerization on RNA-RNA interaction formation

The interactions formation between RNAs is key to many regulatory processes in life. Such RNA-RNA interactions (RRIs) are typically formed between regions of the molecules that are not involved in (intra-molecular) structure formation of the molecules themselves. Thus, in order to predict RRIs the structure of the interacting RNAs has to be taken into account. This is well modeled and done using accessibility-based RRI prediction tools like our in-house tool IntaRNA. Some regulatory molecules are produced in large amounts in order to fulfill their regulatory function via RRI formation. In such a scenario, it is quite likely that the molecules not only interact with their regulatory targets but also with molecules of their own type, which is called homo-dimerization. Dimerization can have multiple effects, e.g. (i) it might reduce the regulatory effect since many RNAs are bound and not available for interaction with the target molecule or (ii) it might change the structure of the dimerizing RNAs and thus "unlock" regions for RRI formation with the target that are otherwise blocked by intra-molecular structure. This project aims at studying such effects of homo-dimerization. To this end, a workflow is to be implemented that combines RRI prediction and constraint RNA structure prediction to model the effects of homo-dimerization. Furthermore, respective visualizations are to be developed and integrated into the workflow to simplify the study and interpretation of such effects.

Genomic long range RNA-RNA interactions in flaviviruses

For the replication of flaviviruses, the formation of a specific long-range RNA-RNA interaction of the trailing untranslated regions of the virus genomes is crucial. This project aims at the prediction, comparison and modeling of these interactions using state-of-the-art tools for RNA-RNA interaction prediction and RNA alignment to identify common and species-specific details of these interactions.

Clustering SARS-CoV-2 spike protein sequences using autoencoder neural network

The aim of this project is to create a low-dimensional representation of SARS-CoV-2 spike protein sequences using an autoencoder neural network. Then, the low dimensional representation of sequences should be clustered using popular clustering algorithms such as TSNE and UMAP to explore if the original differences in sequences belonging to different clades (categories of sequences) are also maintained in lower dimensions. Related reading

Learn and predict nucleotide evolution in SARS-COV2 sequences using generative adversarial neural network

SARS-COV2 sequences mutate to multiple variants categorized into lineages and clades, some of which alter the pathogenicity of the virus making it more virulent. Using generative adversarial neural networks, artificial sequences can be generated using the knowledge of the evolution of SARS-COV2 sequences in the past. Ideally, the neural network should learn the 'edit' mechanism of the sequences that evolved in the past and should generate sequences based on the learned knowledge. The generated sequences should be compared with the true sequences to see how good the neural network performs.

Closed Topics

Evaluating classification methods based on microbial community composition.

Microbiome is the collection of all microbes, such as bacteria, fungi, viruses, along with their genes, which live inside and outside our bodies in all environments surrounding us. To investigate microbiomes, researchers use sequencing data and microbiome analyses. These analyses rely on sequencing data to investigate microbiomes. Such analysis relies on sophisticated computational approaches: assembly, binning, taxonomic classification, functional profiling etc. Analyzing microbiome data makes it possible to answer the two main questions for most microbiome analyses. Who (microorganisms) are there: by extracting the community from the microbiome reads What are they doing (and how): by extracting the gene/pathway abundance profile from the metagenomics reads and transcript abundance profiles from the metatranscriptomics reads and combining them. The MGnify Pipeline (https://www.ebi.ac.uk/metagenomics/pipelines/5) provides a standardized way to process metagenomic data and store the results on a public database.

Project context: The MGnify database can be accessed via an API, that allows the retrieval of microbiome abundance data of various origins. Example notebooks that use the API are already included in galaxy. The projects aim to use this abundance data and investigate potential applications in machine learning and comparative metagenomics. Therefore, different normalization approaches need to be applied, that normalize the data in regard to experiment-specific parameters, such as sequencing depth and sample size. The normalized data should be used to investigate the potential to classify samples from different biomes as well as different host phenotypes. The workflows should be implemented and documented in galaxy.

Integration of multi-modal omics analysis framework into Galaxy

Single-cell multimodal omics allows simultaneous profiling of different types information such as gene expression, DNA methylation, chromatin accessibility and surface protein levels of each individual cells. Such data enables cell characterization based on complex gene regulatory networks. Analysis of such datasets requires immense knowledge in programming languages such as R, python and statistics. To provide experimentalists with complex multimodal analysis workflows, this project aims to integrate computational workflows in Galaxy. We chose to integrate muon based workflows for such data analysis. The muon framework shares datatypes and features with an already Galaxy integrated framework called Scanpy. The objectives of this project are integration of muon multimodal analysis workflows into Galaxy and development of Galaxy training material based on the integrated workflows.

Creation of a tutorial for metagenomics data analysis

Emerging and powerful technologies like DNA sequencing are getting cheaper and therefore more accessible for many applications, e.g. in microbiome. This produces more data to analyze by scientists. Platforms like Galaxy help scientists to analyze their own (complex) data in a user friendly way. But they need to learn how to do that. The Galaxy Training Network (GTN) created an open-source e-learning infrastructure to provide a collection of tutorials developed and maintained by the worldwide Galaxy community ( https://training.galaxyproject.org ). Related to microbiome data analysis, the GTN currenlty offers 8 tutorials, built around a research story ( https://training.galaxyproject.org/training-material/topics/metagenomics/ ). The microGalaxy community aims to expand that catalog for whole-genome microbiome data analysis. The aim is this project is to create a tutorial using data from the Human Microbiome Project, tools an tutorials developed by the Hüttenhover lab to update the general overview tutorial.

Port an amplicon pipeline for microbiome data analysis to Galaxy

Project context: MGnify offers an automated pipeline for the analysis and archiving of microbiome data to help determine the taxonomic diversity and functional & metabolic potential of environmental samples. The pipeline even if documented is not really usable outside their resources. We would like to offer this pipeline for Galaxy users. This project aims to port the amplicon part of the pipeline into Galaxy. More information about the project can be found here: https://github.com/usegalaxy-eu/project-ideas/issues/31

Development of a Galaxy pipeline for detection of SARS-CoV-2 variants in wastewater samples

Nearly two years after the first report of SARS-CoV-2 in Wuhan, China, the COVID-19 pandemic has affected more than 485 million people. Wastewater surveillance has attracted extensive public attention during the SARS-CoV-2 pandemic, as a passive monitoring system to complement clinical and genomic surveillance activities. Several methods and protocols are already in place that effectively facilitate the detection and quantification of viral RNA in wastewater samples, and concentrations in wastewater have been shown to correlate with trends in reported cases. The Galaxy community has put a lot of efforts for continuous analysis of intra-host variation in SARS-CoV-2 ( https://galaxyproject.org/projects/covid19/ ), including development of workflows. The aim of this thesis are to: (i) Evaluate existing workflows for wastewater data analaysis; (ii) Expand and adaptat existing Galaxy workflows; (iii) Extensive test of workflows on mock and real data; (iv) Connect with existing data sources.

Machine-learning-based improvement of genome-wide target prediction of sRNAs

Identifying putative regulatory target regions of bacterial small (s)RNAs is still a challenging problem due to the high false positive rate of predictive methods. One way to greatly reduce false positives is to combine genome-wide predictions of related organisms, which is the core feature of the CopraRNA approach. This project aims at the identification and benchmarking of fast, simple but still sufficiently reliable target prediction workflows based on machine learning techniques to speedup CopraRNA.

Graph neural network based model for cancer driver prediction

• Python programming experience, Machine Learning

Gene prioritization based on pheotypes

Graph neural network-based method for single-cell rna-seq denoising, development of an automated scoring system for shared galaxy histories.

Due to the pandemic situation the interaction with the public face-to-face is not feasible. Therefore, we with the Street Science Community started the development of an online data analysis game ( http://streetscience.community/DNAnalyzer/ ). Within the game users will learn about the microbiome, DNA, sequencing and how to perform a data analysis. Galaxy provides the perfect platform to learn and later perform data analyses. To get scores for there data analysis gamer will share there histories. Within this project a tool will be developed where two shared galaxy history are compared and a score for the submitted history will be calculated. Further information about the topic can be found here: https://github.com/usegalaxy-eu/project-ideas/issues/28

Implementation of the infrastructure for an online & interactive game on DNA data analysis

Due to the pandemic situation the interaction with the public face-to-face is not feasible. Therefore the street science community started the development of an online data analysis game ( http://streetscience.community/DNAnalyzer/ ). Within the game users will learn about the microbiome, DNA, sequencing and how to perform a data analysis. Galaxy provides the perfect platform to learn and later perform data analyses. However, the gamer will register on a separate website connected with Galaxy and additionally tracks the successes and results of each gamer. The aim of this project is to implement a small webserver to register participants, display the videos, questions, puzzle, collect and display the score of the participants, and connect with the automated scoring system developed in an other master project. Further information about the topic can be found here: https://github.com/usegalaxy-eu/project-ideas/issues/22

Tool Resource Prediction for Genomic Datasets

The amount of CPU, RAM, and processing time for a tool to complete is dependent on the size of the input dataset and the complexity of the tool. By emulating these processing requirements with a benchmarking stress-testing tool such as stress-ng, we wish to accurately measure the footprint of the top set of tools on the UseGalaxy.eu workbench with repeated benchmarks, and try to predict their future footprint based on input data size and other extractable content, using machine-learning.

Integrating Multi-Omics Data and Pathway Structure with Explainable Graph Neural Network for Precision Medicine

Cancer is a disease that has afflicted the human species for ages, with each tumor possessing its own set of unique characteristics. As a result, people with comparable phenotypes respond to similar therapy in different ways. Largely unsolved, this area has started evolving over the past few decades owing to the availability of multi-omics data and large-scale data of cancer cell lines with different drugs approved for clinical trials. Consequently, a new area termed personalized tumor therapy has emerged. The goal of this research is to propose a novel method that aims at predicting drug response for cancer cell lines.

Analyzing miRNA processing patterns from single-cell small RNA sequening

Studying mRNA expression at single-cell resolution is a well established research area. There exists numerous experimental and computational methods to sequence and analyze the single-cell transcriptomcs. But all of them were designed and optimized to work with protein-coding genes only. Currently, there are only a very few experimental protocols to sequence small non-coding RNAs at single-cell level. It was shown that the existing computational methods that are used for single-cell mRNA-Seq can be used to cluster mature miRNAs and miRNAs also show cell-type specific expressions. In this project we aim to investigate whether miRNAs processing is cell-type specific. To achieve this, we use apply existing computational methods that were developed for bulk miRNA-Seq data to cluster individual cells based on miRNA processing patterns.

Analysis of CRISPR-Cas System in Marine Metagenomics

The 2020 Nobel Prize in Chemistry to Emmanuelle Charpentier and Jennifer A. Doudna for the discovery and development of CRISPR/Cas9 system highlight the importance of CRISPR-Cas systems. CRISPR-Cas system is an adaptive immune system found in prokaryotic lifeforms and is very diverse in nature. Cas proteins evolve rapidly. Here, we aim to analyse metagenomic data found in the marine ecosystem for the CRISPR-Cas proteins. The main focus is on class-2 type-V system, as the effector protein Cas12 from this system is a promising gene editing candidate. We used three databases for the analysis: Tara Oceans database with 2,631 draft metagenomes, MarRef dataset with 970 assembled metagenome, and IMG/VR dataset with above 90 percent completeness. We built four pipelines comprising different methods and tools for the whole analysis: pipeline 1 for detecting CRISPR-Cas systems and Cas12 proteins, pipeline 2 for transposons, pipeline 3 for repeats and their secondary structures, and pipeline 4 for the spacers and protospacer adjacent motifs (PAMs). We observed that the two tools (CRISPRCasIdentifier and CRISPRCasTyper) used for detecting CRISPR-Cas systems produce very different results, indicating the requirement for building a more accurate and robust tool for the identification of CRISPR-Cas systems. For different variants of Cas12 proteins, we detected different transposable elements. From the analysis of detected repeats, we identified 13 different secondary structures for the repeats found in type V systems and many having a conserved GAAAC or GAA sequence at the 3� terminus. During the spacer analysis, we detected different PAMs. Along with 5� T-rich PAMs, we also detected 5� A-rich PAMs along the upstream of detected spacer sequences. Our work shows that there is still a lot not known about Cas12 proteins, and further in-depth analysis can lead to a better understanding of Cas12 proteins and CRISPR-Cas systems.

Peak Calling und Workflow-Implementierung f�r das single cell Assay for Transposase-Accessible Chromatin Verfahren durch Sequenzierung

In der vorliegenden Bachelorarbeit wird das Verfahren scATAC-Seq und seine biologischen Hintergründe vorgestellt, welches offene Regionen im Chromatin des Genoms einzelner Zellen findet. Des Weiteren wird untersucht, wie die Daten von scATAC-Seq am besten verarbeitet werden, so dass mäglichst viele, hoch qualitative Informationen zu den offenen Chromatinregionen erhalten werden kännen. Dafür werden die Daten speziell vorverarbeitet, anschlie�end werden die Zellen teilweise gruppiert und schlussendlich die Peaks durch Peak Calling bestimmt. Im Anschluss werden die Peaks der einzelnen Zell-Gruppen wieder zusammengefügt, um sie schlussendlich zu vergleichen und auf verschiedene Qualitätskriterien zu überprüfen. In dieser Arbeit werden vier verschiedene Methoden vorgestellt, um diesen Ablauf, mit kleineren Änderungen, durchzuführen. Dazu werden ungefähr 3000, durch scATAC-Seq gewonnene, menschliche Zellen durch die verschiedenen Methoden bearbeitet und untersucht. Anschlie�end werden die Ergebnisse verglichen. Die Resultate zeigen Potential zur Feststellung von diesen Arten der Verarbeitung der Daten. Dabei kann in dieser Arbeit aber nicht eine Methode klar empfohlen werden, da es tiefere Untersuchung der gewonnenen Peaks benätigt, um ein abschlie�endes Urteil über die Qualität der Ergebnisse zu erhalten.

How genomes are shaped by direct and indirect selection pressure: a study in in silico experimental evolution

What are the different pressures that can shape genomes in evolution? The aim of this thesis is to focus particularly on the case of reductive genome evolution, i.e. the reduction of genome size over time as observed in some marine cyanobacteria. To address this topic, is used silico artificial evolution, a method in which genomes of virtual organisms evolve via computer simulations, and particularly the Aevol model. Several experiments have been conducted to test the effect of several parameters (population size, mutation rate, and selection strength) on the genome structures and other selection measures (e.g. fitness, robustness).

Drug repurposing and adverse event prediction through EHR knowledge graph completion

Drug repurposing is the process of discovering new indications of existing, approved drugs while the latter comprises identifying probable harmful effects of known or novel drugs. It is normally done by in vivo and vitro methods which are of high costs, slow results, and limited sample size besides some ethical issues. Therefore, effective computational methods are needed. In this project, we investigate EHR data and create a machine learning model using the relational graph attention network to predict the potential links between entities of interest link drugs, diagnoses, etc.

TAD detection in Hi-C data

Within the 3D space of a cell, DNA forms a structure resembling a ball of wool. The points of contacts of the DNA with itself, called DNA interactions, have "threads" within this "ball of wool" that form smaller loop-like structures called DNA loops. At a close genomic distance, these loops are called topological associated domains (TADs). A z-score based detection algorithm currently exists to detect these TADs, but lacks the ability to detect overlapping TADs and hierarchical structures. In this master project a new approach based on neural networks should be investigated and implemented.

CRISPR-Cas9 Off-Target Prediction Methods

• Python programming experience and Machine learning

Assigning tissues of action to genomic loci associated with kidney function

• R-Programming experience

Website for visualization and publishing of single-cell RNA-sequencing (scRNA-seq) datasets

Rri prediction ranking.

While IntaRNA is a state-of-the-art method to predict RNA-RNA interaction, it is not clear if this prediction will happen in nature. We are building a support vector machine model which should validate the in silico interaction on its occurrence in vivo and can therefore be use to post filter interaction predictions. RNA-RNA predictions can experimental be verified by mutation experiments. Based on this experimentally verified interactions we are building we are developing a positive and negative trainings set. This dataset is already discussed in CopomuS by Raden et al.

Machine Learning for Gene Discovery

Current approaches to finding new genes have a high false positive rate. Help us develop a tool to filter candidates in this straightforward Machine Learning project. You will expand on our Scikit-Learn python code and work with state of the art bioinformatics tools. The project covers feature extraction, filtering and classification on an annotated dataset of alignment files.

Binding Affinity Prediction of Protein-Ligand Complexes

This project predicts the binding affinities between the potential drugs (ligands) and the target proteins responsible for diseases or conditions.It uses the data of protein-ligand complexes stored in the PDBBind database to train a machine learning model.From every complex, features related to proteins are extracted by using the pocket-finding software fpocket. Four ML models were studied in this project - Simple Linear Regression, Random Forest Regression, Support Vector Regression, and Rotation Forest Regression.

Multi Protein-Ligand Interaction Prediction using Machine

In this thesis, a voxelization procedure was developed and applied to targets (or proteins) in the PCBA (PubChem BioAssay) dataset to create a three-dimensional image of the protein-ligand binding site. These voxelization data were used to train a neural network, more specifically a CNN autoencoder to featurize the binding site by keeping only the most relevant information. This information was then combined with ligand features (which have been calculated using the RDKit descriptor tool from the RDKit library) and finally using machine learning techniques, protein-ligand binding affinity was predicted for each protein-ligand pair.

BioBlend to Galaxy API extension and OpenAPI specification

BioBlend is a Python library to enable simple interaction with Galaxy via the command line or scripts.Galaxy is a data analysis platform for accessible, reproducible and transparent computational research. It includes a web interface through which users can design and perform tasks in a visual and interactive manner. The Galaxy server also exposes this functionality through its REST-based Application Programming Interface (API). In this project several important new features were introduced into BioBlend and the Galaxy API and a tutorial written for future developers.

Predicting Hi-C contact matrices using machine learning approaches

In recent years, many studies have shown that the three-dimensional conformation of genomes is a key factor for understanding several important mechanisms on the molecular biological level. However, the Hi-C experiments typically conducted to measure this 3D-structure are still expensive, so that computational methods for predicting the spatial chromatin organization from existing data have recently become subject to research. In this thesis, two machine learning approaches are investigated with regard to their usability for predicting chromosome conformation in form of Hi-C contact matrices from ChIP-seq data. Here, the first method adapts and extends an existing dense neural network architecture for Hi-C matrix predictions, while the novel second method, Hi-cGAN, leverages techniques from image synthesis, especially conditional generative adversarial networks (cGANs). While the dense neural network approach can neither produce satisfactory predictions for the Hi-C matrices of human cell lines GM12878 and K562, nor for Drosophila Melanogaster embry- onic cells in the chosen setting, Hi-cGAN yields encouraging outcomes in all three cases.

Within the 3D space of a cell, DNA forms a structure resembling a ball of wool. The points of contacts of the DNA with itself, called DNA interactions, have "threads" within this "ball of wool" that form smaller loop-like structures called DNA loops. At a close genomic distance, these loops are called topological associated domains (TADs). A z-score based detection algorithm currently exists to detect these TADs, but lacks the ability to detect overlapping TADs and hierarchical structures. In this master project a new approach based on machine learning classifiers should be investigated and implemented.

Hi-C interaction matrix prediction based on protein location

In the 3D space of a cell the DNA forms a structure that looks like a ball of wool. Obviously, many points of contacts of the DNA wire with itself, called DNA interactions, exists in this "ball of wool" and form a structure including DNA loops. These loops contribute to the stability of the DNA and do play an important role in gene regulation. Current research shows that proteins bind on the DNA at these loop locations and contribute to the formation of loops and therefore for the whole structure. The structure of the DNA can be read out with a technique called Hi-C and the resulting data is represented as an interaction matrix in the computer. However, Hi-C is an expensive technique and for many cell types no data is existing while at the same time the technique to read out the position of proteins on the DNA (ChIP-Seq) is quite cheap and a lot of data is online available. The goal of this master project is to use a random forest approach to predict Hi-C interaction matrices by learning the location of proteins. Based on the results of the master project from Andre Bajorat, possible optimizations for this model are investigated.

Hierarchical TAD detection in Hi-C data

Within the 3D space of a cell, DNA forms a structure resembling a ball of wool. The points of contacts of the DNA with itself, called DNA interactions, have "threads" within this "ball of wool" that form smaller loop-like structures called DNA loops. At a close genomic distance, these loops are called topological associated domains (TADs). A z-score based detection algorithm currently exists to detect these TADs, but lacks the ability to detect overlapping TADs and hierarchical structures. In this Bachelor thesis a method to detect these was developed and implemented.

Creating a linkage analysis workflow in Galaxy

Classical linkage analysis is the method of looking for genes that are inherited together in a family tree, which has been now superseded by variant analysis in the era of high-throughput sequencing, but is still relevant in rare disease studies. The Galaxy project is a free and open-source web-based platform for bioinformatic research, and offers users an interactive drag-and-drop avenue to perform their analyses. This project would involve wrapping tools into Galaxy, and chaining them together in a workflow for public user access. Optionally, training material can be written to guide users through the analysis. Applicants need only to know basic HTML/XML and Markdown.

Integrating a haplotype analysis visualization into Galaxy

The study of haplotypes is relevant to pedigree analysis, which looks for mutations inherited from founders that manifest only after many generations due to the semi-random/coalescent nature of inheritance. This project will be wrapping an existing haplotype visualization tool into Galaxy, an open source web-based bioinformatic analysis environment, in order to reach a greater number of users. Applicants must know basic Javascript and HTML/XML.

Multi-site RNA-RNA interaction prediction

Accessibility-based RNA-RNA interaction prediction methods are typically modelling a single block of consecutive inter-molecular base pairs. Thus, interaction pattern that consists of multiple concurrently formed blocks can not be predicted. Within this project, we are developing and testing possibilities to efficiently predict concurrent blocks of interaction within an accessibility-based prediction model. The approach will be based on IntaRNA , which is one of the state-of-the-art programs for RNA-RNA interaction prediction. The respective extensions of the IntaRNA package will be integrated into the main package for external use and further development.

Graph neural network-based method for disease gene prioritization

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by a large number of candidate genes and by the heterogeneity of the available information. Therefore, computational methods for the prioritization of candidate genes are needed to deal with these problems. A number of methods have been proposed and have shown potential results. However, there is still a need to develop more accurate disease gene prioritization methods. The aim of this project is to develop a graph neural network-based method for disease gene prioritization. This choice is supported by (1) graphs are a common and natural way to represent the gene relations, and (2) Neural network for graphs are now state-of-the-art in graph (graph node) classification problem.

A deep learning model to detect triple helices in genomics data

Triple helix formation has been known to interfere in the gene expression process by often modifying the transcription of targeted genes. Therefore, understanding how and where triple helices form is crucial to better understand gene expression. To identify regions where triple helices formed, wet-lab experiments and some computational methods are performed. However, non-existing methods are based on machine learning. Here we would like to propose a deep learning-based method to detect triple helices in genomic.

CRISPR/Cas9 is a unique and robust gene-editing method that has the ability to accurately edit target genes in a wide variety of organisms. However, experimental results indicate that the binding and cleavage of off-target sequences are a major concern for the application of CRISPR/Cas9 and the sgRNAs should be designed in such a way that the impact of off-targets is minimized. Several computational methods have been proposed as a substitute for expensive lab experiments to predict off-targets. Yet, powerful approaches need to be devised to make precise predictions. Here we aim at proposing a Graph Convolutional Network model to predict off-targets of CRISPR/Cas9. The proposed model is expected to overcome following typical challenges: data imbalance, robustness, prediction crossing different cell-types.

Ranking of mutations in RNA-RNA interactions

Point mutations are a common way to verify RNA-RNA interactions. So far, the selection of the position and the introduced mutation is done manually based on expert knowledge of the experimenter. Within this project, we are developing and testing possibilities to automatically evaluate and rank candidate mutations concerning their potential for interaction validation. The approach is based on IntaRNA , which is one of the state-of-the-art programs for RNA-RNA interaction prediction. The respective extensions of the IntaRNA package are integrated into the main package for external use and further development.

Benchmarking Big-Data Workflows Across European Academic Clouds to Evaluate Cloud Bursting Strategies

The Galaxy-Project, a web platform for big-data biomedical research, needs a lot of computational resources and cloud bursting, e.g. sending excess workloads to the cloud, may be a solution in high-demand situations. But how do the various academic clouds, spread across Europe, perform? May one be better suited than the other for a specific workload? Does physical distance and connectivity between data centers play a big enough role? What about the underlying infrastructure? Do they make a difference, even if the actual instance size is the same? In this work, where I benchmarked various academic clouds in Europe, I want to answer these questions and even offer a framework for future benchmarks, as the need for benchmarking more clouds in the future arise.

Base-pair probabilities for accessibility-based RNA-RNA interaction prediction

Computing base pair probabilities of RNA-RNA interactions allows for a number of useful applications, such as the creation of dot plots, which allow for easy and fast comparison between different base pairing patterns. A number of tools exist that already incorporate base pair probability calculation, such as RNAcofold and NUPACK. However these tools are limited to a specific algorithm for the optimal interaction computation that might lack in precision or computational efficiency depending on the application. IntaRNA on the other hand is a highly exible RNA-RNA interaction prediction tool that implements a large number of different prediction algorithms, including very efficient seed-constraint methods. This thesis explores the benefits and difficulties of introducing the computation of base pair probabilities into a number of IntaRNA predictors, including seed-based predictors. For this reason IntaRNA was extended with the ability to compute base pair probabilities, depending on the chosen prediction model. The output is provided as a dot plot to allow for easy investigation. Finally, a number of applications are presented that benet from base pair probabilities, including the comparison between verified and non-verified RNA-RNA interactions and the detection of multi-site RNA interactions. Based on these results, potential improvements for IntaRNA's prediction model are discussed, including different approaches for the accessibility computation and the incorporation of sequence conservation into the prediction estimation.

In the 3D space of a cell the DNA forms a structure that looks like a ball of wool. Obviously, many points of contacts of the DNA wire with itself, called DNA interactions, exists in this "ball of wool" and form a structure including DNA loops. These loops contribute to the stability of the DNA and do play an important role in gene regulation. Current research shows that proteins bind on the DNA at these loop locations and contribute to the formation of loops and therefore for the whole structure. The structure of the DNA can be read out with a technique called Hi-C and the resulting data is represented as an interaction matrix in the computer. However, Hi-C is an expensive technique and for many cell types no data is existing while at the same time the technique to read out the position of proteins on the DNA (ChIP-Seq) is quite cheap and a lot of data is online available. The goal of this master project is to use machine learning and neural network regression models/approaches to predict Hi-C interaction matrices by learning the location of proteins.

Hi-C interaction matrix correction

In the 3D space of a cell the DNA forms a structure that looks like a ball of wool. Obviously, many points of contacts of the DNA wire with itself, called DNA interactions, exists in this "ball of wool" and form a structure including DNA loops. However, many o f these contacts are random contacts or measurement errors and need to be corrected. A Python implementation is existing but comes to its limits for high resolution data caused by high memory usage. This master project should try out if a more efficient algorithm is existing and if an implementation in C++ is possible with less resources.

Statistical significance for RNA alignment predictions and an evaluation schema for multiple sequence alignments in local mode

To evaluate the predicted alignment of the RNA sequence-structure alignment tool LocARNA, so far the alignment score of the has been provided. The score is the optimal value of the objective function from the LocARNA optimization problem. However the scores are not very informative for the end-users, e.g. how well the predicted alignment is significant and likely to occur by chance. It would be desirable to have a statistical measure that not only rank the quality of a given alignment but also make it possible to compare the prediction to other alignment tools and the reference alignment. In this thesis an empirical p-value for LocARNA will be developed. Furthermore, to evaluate a multiple sequence alignment results a suitable scoring schema for multiple sequence alignments will be investigated.

p-value statistics of IntaRNA predictions

The RNA-RNA interaction prediction tool IntaRNA provides sophisticated and highly accurate results in terms of free energy minimization. Since it is non-trivial for users to interprete the provided free energy terms, this project investigates ways how energy statistics and respective p-values can be provided.

RNA-RNA interaction prediction via seed extension

Our group develops the tool IntaRNA , which is one of the state-of-the-art programs for RNA-RNA interaction prediction. We are continously extending the tool (C++, c++11, boost, doxygen, autotools, openmp) that is hosted on Github BackofenLab/IntaRNA . This project aims at the implementation and testing of seed-extension strategies to speedup and improve IntaRNA's predictions. The developed extensions to IntaRNA will be integrated into the main package for external use and further development.

Implementing bioinformatics algorithms for teaching

Within the last years, we have created interactive implementations of various algorithms discussed in our lectures. These are freely available at the Freiburg RNA tools - Teaching section of our public webserver. The algorithms are implemented in Javascript and are accompanied with according visualizations to better understand each approach.

Identifying and analysis of new anti-CRISPR proteins

CRISPR-Cas system of archaea and bacteria provides resistance against viruses and phages. Since phages have a constant battle against prokaryotic; recent discoveries show that have described phage genes that inhibit the CRISPR-Cas function. These are, however, likely to be quite diverse in function as they can interfere with the CRISPR-Cas response at different stages. This work aims to develop a new method of identifying a new family for anti-CRISPR proteins based on homology search.

Identification of CRISPR arrays using machine learning approach

Archaea and Bacteria are known to acquire immunity against viruses and plasmids through a widely conserved RNA-based gene silencing pathway. This mechanism involves non-coding RNA that originates from Clustered Regularly Interspaced Short Palindromic Repeats, and CRISPR-associated proteins (CRISPR-Cas system). CRISPRs consist of identical repeats that are between 20 to 47 base pairs in length, separated from each other by unique spacer sequences of similar length (27 to 72 base pairs). Most CRISPR arrays are flanked on the upstream (5') side by a leader sequence of 60 to 500 base pairs. These leaders often contain low complexity sequences and are rarely conserved between more distantly related species. Finally, there are the Cas genes, which are usually located directly up- or downstream of CRISPR array, however, they can also be found in very different locations. These genes encode protein complexes which work together with CRISPR arrays to confer the host cell with an adaptive immune system to fight invading viruses and plasmids. This work aims to develop a new tool to detect a CRISPR-Array using machine learning approaches.

Crossdating of wood samples using MICA-aligned density profiles

Our group develops the tool MICA , which enables Multiple Interval-based Curve Alignment of arbitrary curve/profile data. It is currently applied to derive meaningful consensus data of experimentally measured wood density samples. Within this project, we will use MICA density profile alignments to evaluate their potential for crossdating, i.e. the time annotation of wood samples. Given the increased information compared to standard methods based on ring widths, the approach should yield high precision even for small wood samples (few rings).

Modular benchmark pilot framework for evaluating RNA alignment tools

To benchmark the quality of RNA alignment algorithms, it is important to validate their performance and compare with similar tools. For this purpose a benchmark-pilot framework to automatically benchmark RNA alignment algorithms such as LocARNA and SPARSE will be developed. The aim is to have a modular and easily extendable framework to evaluate various range of tool for different computation platforms from PCs to High Performance Computing grid systems. The task of this project is focused on development of the benchmark-pilot code in python using SnakeMake workflow manager, to replace the previously deployed system.

RNA-RNA interaction prediction for long molecules

Our group develops the tool IntaRNA (see PhD-thesis A. Richter for details), which is one of the state-of-the-art programs for RNA-RNA interaction prediction. We are currently reimplementing and extending the tool (C++, c++11, boost, doxygen, autotools, openmp) that is hosted on Github BackofenLab/IntaRNA . This project aims at the implementation and testing of strategies to enable predictions for very long input molecules, for which the standard approach might break due to extreme memory consumption. The idea is to apply a window-based segmentation, which requires a special result handling to avoid duplications in the output. The developed extensions to IntaRNA will be integrated into the main package for external use and further development.

Constrained RNA-RNA interaction prediction

Our group develops the tool IntaRNA (see PhD-thesis A. Richter for details), which is one of the state-of-the-art programs for RNA-RNA interaction prediction. We are currently reimplementing and extending the tool (C++, c++11, boost, doxygen, autotools, openmp) that is hosted on Github BackofenLab/IntaRNA . This project aims at the implementation and testing of new prediction modi, which incorporate additional constraints to further improve prediction quality. To this end, an IntaRNA benchmark set and according protocol is compiled that is used in the course of the thesis to evaluate the newly integrated features. Furthermore, statistics on known interaction and single-molecule structures will provide the parameters for the new constraints. The developed extensions to IntaRNA will be integrated into the main package for external use and further development.

Within the last years, we have created interactive implementations of various algorithms discussed in our lectures. These are freely available at the Freiburg RNA tools - Teaching section of our public webserver. The algorithms are implemented in Javascript and are accompanied with according visualizations to better understand each approach. In the course of this project we are focusing on sequence alignment algorithms as taught in our Bioinformatics-1 and -2 lecture.

Prediction of non-consecutive RNA-RNA interactions

• exam in "RNA bioinformatics" lecture
• C++ experiences

Integration of BioJS into Galaxy

Galaxy is an open, web-based platform for data-intensive research. The University of Freiburg is running a Galaxy server to serve all different needs of our researchers. Visualization is a key aspect in the understanding of data analysis for medical and biological research. The Javascript library BioJS provides powerful visualization of multiple biological data. The overall aim is to integrate specific BioJS modules into Galaxy via its plugin architecture.

Large-scale clustering of non-coding RNAs in the Galaxy framework

Clustering of putative RNAs is currently the major approach for functional annotation of putative ncRNAs detected in genome-wide screens. GraphClust is one of the few approaches that can cluster hundreds of thousands putative ncRNAs as it is based on an alignment-free approach using an advanced graph kernel. The candidate clusters are iteratively retrieved and refined using RNA alignment tools. However the clustering pipeline requires in-depth knowledge as several tools have to be installed and configured. The goal of this project is an extension of the GraphClust tool using Galaxy framework that makes it possible to (a) perform the clustering of RNAs via a web interface, (b) run the computations on various operating systems and computation frameworks, (c) freely customize and extend the generic pipeline for specific needs. The project involves also attempts to apply the Galaxy workflow on a metatranscriptome dataset.

Characterization of ribosomal footprints with use of graph kernel based approaches

Ribosome profiling is an emerging technique that with use of deep sequencing methods, gives new insight to translation of proteins from single codon to genome scale. In comparison to former available methods microarrays and RNA-seq, Ribo-seq solely considers active mRNAs at translation phase in a cell which prepare information for protein synthesis. This novel charac- teristic of Ribo-seq provides new data with focus on translation level. The obtained patterns of ribosomal footprints may reveal new aspects in trans- lation field. The aim of this work is to classify Ribo-seq profiles according to different conditions and find clusters with respect to Ribo-seq profiles. This is done by a tool named BlockClust, which is based on a graph kernel method called Neighborhood fast graph kernel (NSPDK). BlockClust en- codes expression profiles data to graphs format and employ NSPDK method for achieving a high performance. Although BlockClust previously applied for clustering non-coding RNAs from their RNA-seq expression profiles, it can also be adapted to use for clustering and classification tasks on other types of data e.g. Ribosome profiling. Therefore, we have adapted Block- Clust by defining new attributes for finding patterns in Ribo-seq data and adding them to the former available set of attributes. Moreover, we per- formed an optimization by using different parameter sets. Furthermore, we showed that it is possible to employ BlockClust on Ribosome profiles. We achieved a good performance in classification of these profiles.

Approximate nearest neighbor query methods for large scale structured datasets

The task of efficiently finding the most similar representatives in a large set of entities is at the core of many problems in a variety of applications, ranging from chemoinformatics to recommendations systems; when the objects of interest are structured entities the problem becomes harder. In these cases structured instances are explicitly converted in sparse vectors that live in very high dimensional spaces (even millions of features). Exact algorithms have unfortunately a computational complexity that scales quadratically with the number of instances times the representation length of each instance, hence these approaches cannot be used when we have a large number of structured instances. A possible solution is to accept approximate results to gain efficiency. The candidate will extended one such approximate technique (the MinHash approximate nearest neighbor scheme) to efficiently solve the neighbor query in sub-linear time. The overall goals of the thesis were to provide an efficient and simple to use implementation for approximate nearest neighbor queries for large collections of high dimensional sparse vectors.

Learning to design RNA polymers with graph kernels

Graph data structures allow us to model complex entities in a natural and expressive way. In the machine learning literature, several types of discriminative systems that can deal with graphs in input are known (e.g. recursive neural networks, graph kernels, graphical models, etc), however, there are few generative approaches that can sample structures belonging to a desired distribution or class. The task of generating samples from a given distribution when this is accessible only via a finite number of examples is well developed when the domain of interest can be embedded in a vector space. The extension of these approaches to structured domains (i.e. where instances are strings, trees, graphs or hyper graphs) is however substantially less developed. One approach for learning constructive systems is based on a variant of the Metropolis Hastings (MH) algorithm guided by an efficient graph grammar, which, crucially, can be efficiently induced from an example set. Such a neighborhood graph based grammar is suitable when the feasibility constraints are local in nature. RNA polymers, which form structures comprising hundreds of nodes (nucleotides), exhibit however dependencies between distant portions of the structure. In order to extend the constructive system to the RNA domain, Mr. Mautner has introduced a multi level strategy based on a notion of graph minors, i.e. graphs obtained by edge contraction operations. An edge contraction is an operation which removes an edge from a graph while simultaneously merging the two vertices that it previously joined. By carefully defining a domain dependent contraction strategy, Mr. Maunter was able to operate on smaller graphs for which local rules are sufficient to capture the feasibility constraints.

Reinforcement learning techniques in RNA inverse folding

A non-coding RNA molecule functionality depends on its structure, which in turn, is determined by the specific arrangement of its nucleotides. The inverse folding of an RNA refers to the problem of designing an RNA sequence which will fold into a desired structure. This is a computationally complex problem. Algorithms which solve this problem take different approaches, but they share the following attitude: They start from an initial sequence or population and try to move it towards a desired product by performing normal or optimized search methods. RNA inverse folding programs are given different constraints such as GC-content ranges or basepair or nucleotide configurations. The output is normally one or more sequences which fold to the target structure. This work introduces a basic system that given a set of sample RNA secondary structures, produces models which generate structures similar to the sample set. The objectives and constraints are automatically extracted from samples. For doing this, a system is designed which generates models by performing learning on families of RNA sequences. This system consists of two subsystems: one responsible for decomposing secondary structures of sample RNAs into structural features and building a structural features corpus. It also extracts neighborhood connectivity models of structural features in the form of N-grams. The other subsystem is a reinforcement learning framework which uses the corpus and connectivity rules to produce models for generating structures which are similar to the samples. Results in this work show that the current system is able to produce models from RNA families which have a symmetric shape. To make the system capable of dealing with a broader range of RNA families and producing structures with functionalities identical to the sample structures, a refined feature extraction module has been added to the system. This module extracts the GC-content, size and local information of structural features and builds a refined feature corpus. This can provide the basis for a new set of experiments and a start point for producing models with practical applications.

Explorative Enumeration of large energy landscapes

• C++ implementation of the explorative energy landscape enumeration using the Vienna RNA package library.
• Parallelization, benchmarking and implementation tuning.
• Application of the developed program to large RNA molecules.
• Creation of a complete pipeline to study kinetics of RNA molecules including visualization.

Investigating LocARNA parameter search space by using automatic configuration methods

In recent years many novel RNA species have been discovered by new sequencing techniques. The correct classification of these RNAs into new and existing families heavily relies on accurate sequence-structure alignment tools, which makes it desirable to constantly improve their alignment quality. Therefore, having a high-performing RNA alignment tool is of fundamental importance in the field of computational biology. LocARNA implements an efficient heuristic version of Sankoff's accurate but computationally expensive algorithm for simultaneous sequence and structure alignment. The use of heuristics makes the algorithm applicable in practice, but also forces the inclusion of many additional parameters. Since the performance of an algorithm depends on the parameter setting, it is desirable to optimize these settings in order to improve alignment results. One way to find optimal parameter configurations is to use an automtic algorithm configuration technique. In this work the state of the art algorithm configuration tool SMAC is applied to improve LocARNA 's default parameter settings. The optimization focuses on fundamental parameters of the LocARNA algorithm. Both global and local alignment cases are covered, although for the local case this marks the first in-depth optimization attempt. Hence this work also introduces a complete local alignment parameter optimization pipeline for LocARNA. As a result, improved default parameter settings as well as different input scenario settings for both the global and local alignment cases are proposed. Notably, the average alignment quality of the local case on an extension of the Bralibase dataset was improved up to 26%. In conclusion, the presented work not only managed to optimize LocARNA 's local alignment but also provides a solid foundation for further works on parameter optimization using the implemented pipeline.

Graph-based clustering of CRISPR-Cas systems

• Find the best way to encode the CRISPR-Cas system as a graph that represents nature as realistically as possible.
• Use EDeN to perform unsupervised clustering of all available CRISPR-Cas systems in bacteria and archaea.
• Compare results to previous classification systems.

Learning to Construct Graphs with Real Vector Attributes Using Graph Kernels

Graph data structures allow us to model complex entities in a natural and expressive way. In the machine learning literature, several types of discriminative systems that can deal with graphs in input are known (e.g. recursive neural networks, graph kernels, graphical models, etc), however, there are few generative approaches that can sample structures belonging to a desired distribution or class. The task of generating samples from a given distribution when this is accessible only via a finite number of examples is well developed when the domain of interest can be embedded in a vector space. The extension of these approaches to structured domains (i.e. where instances are strings, trees, graphs or hyper-graphs) is however substantially less developed. While specialized applications exist, e.g. sampling phylogenetic trees, sampling dependency graphs for structural learning in graphical models, or sampling large Web like networks, data driven approaches that can deal with general types of graphs, are still in their infancy. Important applications of a successful generative graph system include the de-novo generation of molecular graphs for drugs and RNA biopolymers with user defined properties derived from prototypical natural examples. In these cases the spatial information of the atom arrangement becomes important for the determination of the associated physicochemical properties. There is therefore the necessity to upgrade these generative graph systems to deal with graphs that can encode spatial information in the form of multiple real valued attributes (e.g. 3D coordinates, distances, angles). In the Thesis the candidate will address the constructive learning problem using a variant of the Metropolis Hastings (MH) algorithm tailored for structural data types. She will upgraded the efficient graph grammar approach of a pre-existing code base to deal with graphs with real valued attributes.

A graph kernel approach to the identification and characterisation of structured noncoding RNAs using multiple sequence alignment information

Structured noncoding RNAs perform many functions that are essential for protein synthesis, RNA processing, and gene regulation. Structured RNAs can be detected by comparative genomics, in which homologous sequences are identified and inspected for mutations that conserve RNA secondary structure. To detect novel RNA classes in bacteria and archaea, a variety of bioinformatics strategies have been used, e.g. looking in upstream regions of protein coding genes for cis-regulatory RNAs. To identify ncRNAs independently from protein coding genes, Z. Weinberg has proposed a computational pipeline based on an initial BLAST clustering further refined by looking into secondary structures with CMfinder. The identified structures are then used in homology searches to find homologues that allow CMfinder to further refine its structural alignment. The resulting alignments are scored and then analysed manually to identify the most promising candidates and to infer possible biologic roles.

Interactive de novo molecular design

Synthesis of small molecules that improve on the curative properties of existing drugs or that are effective in previously untreatable illnesses is a very hard task, a task on which pharmaceutical companies are investing enormous amounts of resources. Computational methods become therefore an interesting solution when they can effectively replace the time consuming and expensive design, synthesis and test phases. Since de novo molecule-design systems have to explore a virtually infinite search space, exhaustive searching is infeasible, and they typically resort to local optimisation strategies. To date, one of the most critical aspects is the reliability of the evaluation function invoked to judge the quality of molecules that can be (and generally are) very different from those used in the function induction phase. One possible approach to overcome this difficulty is to integrate the expert knowledge of (medicinal) chemists in the evaluation loop. Doing so in an efficient way is not a trivial task, since one has to 1) minimise the number of times the system resorts to the expensive human oracle, and 2) use a form of interaction suitable for humans.

CRISPRloci visualization

• Find the best way to modify/customize the CGView tool in order to work for our purpose (Java).
• Integrate into CRISPRloci web server (JSP,Html,Java).

RNA energy landscapes with pseudoknot structures

Most studies of RNA kinetics use nested structure models to enable at least moderate sequence lengths. Nevertheless, there is evidence that pseudoknot structures are important for the function of some RNA molecules. Thus, ommitting them in kinetics fosters wrong results. This project will compare kinetics based on energy landscape with and without pseudoknot structures. Furthermore, new strategies have to be explored in order to face the vast increase of the landscape size to enable reasonable studies.

• C++ implementation of the explorative energy landscape enumeration strategies presented in our article in concert with the identified strategies by Bettina Hübner using the available algorithm implementations from the Energy Landscape Library (ELL) .

Similarity notions for RNA kinetics comparison

For larger RNA molecules it is often not computationally feasible to enumerate their whole energy landscape. Thus only partial fews of the landscapes are used to compute the kinetics of the respective molecule. Within this project, different strategies are explored to measure the similarity of kinetics, i.e. to evaluate how well the coarse grained model reflects the kinetics based on the complete energy landscape information.

Generating a local ncRNA benchmark set to evaluate local RNA alignment tools

Multiple local alignment of RNA sequences is by now still a challenging problem as parameters for already existing tools are not optimized yet for the local alignment case. The first step to solve this problem is the generation of a local benchmark set to be able to evaluate existing local RNA alignment tools. The main part of this work is the implementation of a pipeline to append genomic context of a given length to an already existing (global) benchmark set. A simple evaluation of LocARNA on the local ncRNA benchmark set and a random test set will be performed.

Differential Benchmarking of CopraRNA - Finding the optimal input for a specific question

• Generate an extensive dataset for differential benchmarking. (also non enteric bacteria)
• Write scripts that automatically run and evaluate the CopraRNA runs.
• Draw conclusions and develop guidelines for input organism selection.

Java GUI for Multiple Interval-based Curve Alignments (MICA)

• The MICA reimplementation of the core algorithm in Java.
• Development of a Graphical User Interface for MICA in Java.
• Application of the new tool on tree growth data and other data from literature, evaluating the new implementation.

Improving miRNA target prediction in humans using a highly descriptive graph-based, machine-learning model

• Compile training and test datasets of miRNA-mRNA interactions.
• Generate highly sensitive candidate interaction sites.
• Integrate all possible features into a novel graph model.
• Train and test machine learning model using different settings and parameters and use model to filter candidates.
• Compare results to existing tools.

Pruning strategies for large energy landscapes

The energy landscape framework enables the study of the folding kinetics of molecules. For instance the structure formation process of single RNA molecules or the interaction formation of two RNAs. To this end, transition probabilities of one structure to possible successive structures have to be identified. Unfortunately, there is an exponential growth of possible structures a molecule can adopt and accordingly an exponential growth of the energy landscape. One approach to face this problem is to group structures into "macro-states" and to consider only transitions between such structure ensembles. But their number is often still too large to enable kinetics computations. Within this project, different approaches to prune the macro-state energy landscape represenation are tested in order to reduce the according transition encoding to a feasible size open for kinetics computations. The pruning strategies are subject to quantitative and qualitative evaluations concerning reduced computational requirements and preserved kinetics quality.

RNA Barcodes for High-Throughput Sequencing Experiments

CLIP-seq is a method for genome-wide screening of interactions between RNAs and RNA-binding proteins. iCLIP is an extension of CLIP-seq that allows locating RNA-protein interactions with nuceleotide precision. iCLIP employs random sequence tags in to enable calculation of the number of binding events from PCR amplified source material. Errors introduced into these sequence tags during amplification or sequencing can lead to serious overestimation of binding events. This thesis examins the suitability of RNA barcodes developed for multiplex sequencing assays to prevent or mitigate this effect.

Graph-kernel based aromaticity prediction

• Data collection and preparation for training and testing of the SVMs.
• Evaluation of the NSPDK prediction using the available tools from the GGL- and NSPDK-package.

Atom mapping of chemical reactions via Constraint Programming

• C++ implementation of the CP-based atom mapping approach for even ITS rings presented in our article using the Gecode library.
• Extension of the CP-approach to odd rings.
• Evaluation of the approach using atom mappings of known chemical reactions.

Cluster based prediction of SH2 domain-peptide interactions using Graph Kernel

• Data colloection from several high-throughput experiments (e.g. microarray) and compile them to prepare the training and test sets.
• Optimise hyper-parameters for the NSPDK kernel.
• Use Support Vector Machine (SVM) based on NSPDK kernel for the classification.

Large Scale Activity Profile Induction for Small Molecules

• efficiency in the train and in the test phase: some bioassays with hundreds of thousands instances are available; in the test phase 30M compounds have to be screened;
• accuracy: the predicted activity profile has to be sufficiently close to the true activity profile to provide a reliable localization of compounds in activity space;
• semi-supervised mode of training should be possible: since many bioassays contain information only for few tens to hundreds compounds it is necessary to make the best use of the vast amount of unsupervised information available;

In this thesis the candidate will use a graph kernel (NSPDK) to train a linear max margin model via fast stochastic gradient descent technique. The candidate will set up the necessary infrastructure to perform and monitor the in-silico predictions and develop novel techniques for large scale semi-supervised problems in the chemoinformatics field.

Analysis of CLIP-seq and PARCLIP data for Argonaute to identify miRNA target sites

• Collect PARCLIP and HITS-CLIP data for mammals and identify the corresponding mRNA sequences to the CLIP sequences.
• Develop quality measures to map microRNA to each CLIP sequence.
• Explore general properties and uniqe characteristics of collected data. How do these datasets correspond to data found in microRNA databases?
• Optimise IntaRNA parameters to identify correct target sites so that the predictions are very sensitive.

Learning binding preferences of RNA-binding proteins using in vitro affinities and in vivo binding sites

Structural elements in long non-coding rnas.

Non-coding RNAs (ncRNAs) form a heterogeneous class of transcripts with little or no protein-coding capacity. Recently, it turned out that these molecules have a plethora of key regulatory roles in eukaryotic cells. NcRNAs directly act at the RNA level without ever being translated to protein. According to their length, one basically distinguishes small ( 200bp) ncRNAs. The function of a small RNA is typically determined by its secondary structure fold rather than underlying primary sequence. There are several ncRNA classes among small ncRNAs with well defined and well understood secondary structure motifs, examples include micro RNAs (usually forming stem-loop structures) or transfer RNAs (which exhibit the prominent cloverleaf motif). In contrast, it is unclear to which extent long non-coding RNAs contain and are determined by regions of conserved secondary structure. The aim of this work is to analyse secondary structures of long ncRNAs on a genome-wide scale with state-of-the-art bioinformatic techniques, to possibly identify and further characterise common structural elements shared by these transcripts. This may yield novel insights to the computational de novo prediction of long ncRNAs in recently sequenced eukayotic genomes, one of the open problems in current RNA bioinformatics.

De Novo Molecular Design Using Graph Kernels

Large scale multiple genome alignment via an efficient kernel method.

In order to make use of the large amount of genomic information that the sequencing experiments are making available, efficient algorithmic procedures are needed. One of the most fundamental type of processing for genomic data is that of genome alignment, whereby regions belonging to several related genomes are put in bi-univocal correspondence. As a result of the alignment procedure, information of biological relevance can be derived, such as the evolutionary conservation rate of given regions. The sequences in these regions are believed to be important and to correspond to functional biological entities like proteins and non-coding RNA. Correct alignments allow, in other terms, the (semi-)automatic discovery of biological objects (either belonging to known classes, or even to yet unknown classes). However, current genomic alignment techniques 1) are suitable for relatively closely related species, and 2) can process a relatively small number of genomes. In order to allow alignments for thousands of genomes, novel efficient techniques are needed. The choice of computational models suitable for this task has to take into consideration several requirements, such as a) efficiency, b) accuracy and c) flexibility.

Intersections of genomic intervals using interval trees

Testing to find overlaps between genomic features is an important task in genomics research. We know this feature as intersection. In this project I implement a fast and exible method to find intersections between two sets of genomic intervals by using interval trees. The implementation(unionBed) uses sets of features in BED format as input data and find overlaps between them. Then the unionBed results data is used to analyse three different secondary structure prediction hypotheses for co-transcriptional RNA folding and to compare them to each other.

hIntaRNA - Comparative prediction of sRNA targets in prokaryotes

The prediction of targets of bacterial sRNAs is a very challenging task, addressed by several approaches. The experimental testing and verification of sRNA targets is very costly and labour-intensive. Therefore, the reliable algorithmic prediction of putative sRNA targets could vastly reduce the amount of wet lab work. However, due to very short and often imperfect complementarity between the sRNA and its target the prediction is not a trivial task. The IntaRNA algorithm is one approach, which frequently, however, does not yield satisfying results yet and therefore demands improvement. It has been stated "that it is difficult to make significant target predictions when searching sequences from a single organism, and that targets should be predicted in a comparative analysis of multiple organisms". Eventhough this was stated for eukaryotes, the basic idea of this thesis also holds for bacteria. The task of improving the IntaRNA algorithm's prediction quality utilizes exactly this concept, also incorporating the individual phylogenetic distances between the organisms analyzed. For instance, there is compelling evidence, that the MicA and RybB sRNAs in E. coli and Salmonella each have homologous targets in both organisms, thus indicating a conservation on the regulatory level. Here, the implementation of the idea that overlapping target predictions for distinct organisms yield stronger evidence of correct functional prediction is presented.

Secondary structure motif determination in ncRNA via graph kernel based computational models

A partition function variant of rna base pair maximization in adp.

The goal of the project is to lay the foundations of computing RNA base pair probabilities, as done by the Mc Caskill algorithm, in the framework of Algebraic Dynamic Programming (ADP). In order to concentrate on the essential aspects of this problem, we simplify the scoring model of the algorithm to a Nussinov-style base pair maximization. The main challenge is to compute the outside part, which has no natural correspondence in the grammar parsing framework underlying ADP.

Generic JSP-based web frontend creation

Web frontends of terminal-based bioinformatics tools are important to ease their use for non-computer scientists and to enable ad hoc usage. The project aims at the development of a highly generic web frontend framework generalizing the currently available JSP-based frameworks of the CPSP-web-tools and Freiburg-RNA-tools . Main goals are to simplify the setup of new frontends for arbitrary terminal tools and to develop a robust generic framework. The integration is exemplified by creating a frontend for the recently developed program CARNA .

Alignmentverbesserungen mit Hilfe von Consensus-Dotplots

RNA-alignments are essential for identifying and characterising structured non-coding RNA. RNA-alignments are different to DNA or protein alignments in the fact that they not only align according to sequence similarity, but also take the base-pairing patterns of secondary structures into account. A common procedure to characterise the structure of non-coding RNAs is to predict the consensus structure of elements of the same family. The problem with this, is that any errors in the alignment are reflected directly in the quality of the predicted consensus structure. Therefore, it is of high importance to get the correct alignment of RNA families. The largest database of such family alignments is contained in Rfam. A common error in these alignments is that a small subset has been misaligned with respect to the structure, which results in some stems slightly offset to either the left or right in comparison to the others. The goal of this thesis is to develop a method to automatically detect and re-align these misaligned stems and to thus deliver a quick method to improve these common errors in the Rfam database. Furthermore, a key part of the work is to understand the state-of-the art in approaches to align RNA sequences and to perform benchmark experiments that compare current tools to the here developed method. It is also important to understand the complexity of measuring the "goodness" of one alignment and to develop and compare such measures.

Local sequence and structure features in long RNA sequences

There is much evidence in molecular biology that RNA plays an important role in living cells. Research results in the last decade have shown that protein coding sequences are only the tip of the iceberg w.r.t. genomic functional elements. Up to 90% of the genome is transcribed into RNA for which the function still remains largely unknown. The structure of an RNA is an important property for its correct function, e.g. the cloverleaf of a tRNA. However, the experimental determination of the structure is still a very challenging task, therefore we try to deduce the structure from the nucleotide sequence, which encodes it. Furthermore, we find evidence that long RNAs have local regions of functionality and that the entire sequence does not always contribute to a particular function. For example cis regulatory elements on mRNA such as SECIS elements and miRNA binding sites. In this project we want to analyse long RNA sequences in respect to different sequence and structure features. The project aims to identify signatures of natural RNAs and dependencies between RNA sequence and structure. Sequence features comprise the A,U,G and C content as well as di-nucleotide and tri-nucleotide content. In terms of structural features we want to consider accessibilities, base-pair probabilities, accuracies (MEA) and predictions from tools like RNALfold. Given these features, we want to identify dependencies between them and between different sequences. First, the project involves a graph visualisation for the raw data of single features and different combinations of features. Because of the huge amount of data, we need to be able to focus or zoom into regions of interest. Further, we want to reduce the feature information to only regions of high significance in comparison to a background model. Thus, a suitable background model needs to be defined for each feature. With the simplified view, it should be easier to visually spot correlations between several features at once. After an initial visual inspection, automatic methods shall be developed to analyse real datasets of different RNA classes to identify distinct sequence and/or structure signals. First we would like to concentrate on known cis regulatory elements within the UTRs of mRNAs and finally we would like to apply the automatic analysis developed in this thesis to find unknown signals in long non-coding RNAs.

RNA-Protein interaction prediction with Graph Kernels

The aim of this work is to help with the implementation and evaluation of the novel algorithm Exparna-P. This algorithm computes all exact pattern matchings between two RNA strands for the entire structure ensemble. In order to speed the algorithm up, a new method needs to be implemented which computes the probability that a position is unpaired under a loop. Then the already existing chaining algorithm has to be slightly modified in order to compute the best set of non-overlapping and non-crossing exact pattern matchings for Exparna-P. The third part of this bachelor thesis is the comparison of the performance of the Exparna-P tool compared to the Exparna tool.

Multiple sequence alignment methods of long non-coding RNAs

Long ncRNA is a rapidly advancing field of genetics, with yet only briely studied roles (in gene regulation), organization, conservation or medical implications. It is however expected that they will play a great role in further genetic studies and progress. Due to their (sometimes impressive) length (of up to several hundreds of kb) and other particularities, their sequences are rather difficult to align. However, valid sequence alignments are the essential pre-requisite for most subsequent bioinformatic studies of lncRNAs. Therefore, we analyse, compare and benchmark different alignment sets of vertebrate long ncRNAs, namely the Ensembl EPO alignmets, the Galaxy Multiz/TBA blocks and alignments generated by a self-developed pipeline and identify advantages and drawbacks of sequence alignments of lncRNAs.

Evaluating contaminations in genomic sequences

Despite continued advances in whole genome sequencing techniques and the development of powerful assembly algorithms, newly sequenced genomes still often suffer from contaminations during the sequencing process. The most common sources of contamination are accessory DNAs deliberately attached to the DNA/RNA under investigation, including vectors, adapters, linkers, and PCR primers. However, there are also unintended events, e.g. caused by transposon activity or simply impurities, leading to contaminated genomic sequences. These may then result in missassemblies of genomic sequences, meaningless analyses and potentially erroneous conclusions. However, noone knows to which extent publicly available genomes are contaminated. To encompass this unsatisfying situation we therefore plan to develop a comparative genomics approach to broadly identify contaminations in available genomic sequences. The project is not only open for bioinformaticians and computer scientists, it is also suitable for students with a background in biology.

A new heuristic algorithm for IntaRNA for improved RNA-RNA interaction prediction

The number of discovered ncRNAs(non-coding RNAs) that regulate target mRNAs by base pairing is growing fast. This demands for identification of the target mRNAs for those ncRNAs. Thus prediction of such interactions between ncRNAs and mRNAs became of great neccesity to help identify targets for known ncRNAs. A few computational algorithms for this purpose were developed to predict such interactions. While some of the algorithms were fast enough for genome-wide searches, they were not so accurate in predicting interactions between long RNAs. This is because they neglected an important factor for interaction formation which is the interacting site accessibility. IntaRNA considers site accessibility while maintaining the same time and space complexities of these fast algorithms. IntaRNA includes two algorithms, one that gives optimal results according to the Turner free energy model, but is time consuming with time complexity O(n 2 m 2 ). The second algorithm is heuristic with time complexity O(nm) only, but does not give optimal results for all input sequences. In this thesis we present improvements over both algorithms of IntaRNA. First we modified the non-heuristic algorithm to model more accurately how RNAs are actually forming an interaction. It simulates - in the same order - the sequence of events in which interaction formation is thought to happen in real. The new implementation allows to forbid high energy barriers that might be encountered during interaction formation and that are less likely to be overcome. Second we improved the accuracy of the heuristic algorithm of IntaRNA, making it more accurate and reliable for use in biological researches, without significantly increasing its runtime and space requirements.

Development and Implementation of an Alignment Program for Canonical Pseudoknots

At our lab, a general method to align various restricted classes of pseudoknots has been developed. The alignment scheme has also been implemented, but due to its generality, it is comparably slow and not suitable for many large scale practical applications. This work focuses on developing an efficient implementation of only one specialized instance of this scheme (The R&G pseudoknot class) that can be used in real practical scenarios. The topic is suitable for people interested in algorithms, datastructures, software development, and C++ programming.

RNA Consensus Interaction Prediction

RNA-RNA interaction is a subject of considerable biological relevance as the binding of ncRNA to mRNA can affect both the transcription and translation of the bound mRNA and hence regulate gene expression. The accuracy and reliability of single sequence RNA structure prediction has been shown to increase significantly when the structure of an aligned set of RNA homologs is computed. As such, it is posited that by augmenting an existing RNA-RNA interaction prediction algorithm, that determines an interaction structure based only on thermodynamics, with a phylogenetic component a structure prediction of improved quality can be obtained. This thesis presents the theory, implementation and evaluation of an algorithm that combines thermodynamic and phylogenetic information to predict a consensus interaction structure on a set of aligned mRNAs and ncRNAs.

Experimentelle und theoretische Untersuchungen zur Echtzeitanalyse Mikroarray-basierter RNA-Amplifikation

• Application of CDDM to a NASBA microarray with fixed amounts of target RNA
• Incorporation of NASBA amplification into the CDDM binding kinetics

Centroid-based identification of local RNA elements

In this thesis we try to tackle the problem of identifying local RNA elements in a genomewide scale. We employ a fast sparse algorithm to predict maximum expected accuracy structures based on base-pairing/unpairing probabilities. Moreover, we introduce a new locality definition and present an accuracy function reflecting this locality. Base-pairing and base-unpairing probabilities can be efficiently computed using RNAplfold included in the Vienna package. Based on these probabilities, we identify structured regions that have high probabilities of containing significant local RNA motifs. After that, we introduce our new program RNAMotid together with other included features that enables it to scan genome-wide sequences for structured regions. Moreover, we discuss how several modules were integrated together in our program to allow flexibility and optionality of the analysis. Finally, we evaluate the performance of RNAMotid in identifying local RNA motifs embedded in randomly shuffled context. Before that, we apply an overall parameter training followed by a family-based parameter training. Then we discuss the factors that affect the performance of RNAMotid.

Exploring structural characteristics of mRNA target sites using local folding

• Research and preparation of the topic: read about RNA secondary structure, local folding, accessibility, positional entropy, etc. Also gather information on what has previously been done in the structural analysis of target sites.
• Gather data: find experimentally validated target sites for different types of non-coding RNA (and proteins if possible).
• Apply existing local folding programs to the data and calculate the structural characteristics of the target sites.
• Implement a well-documented pipeline with Perl to be able to analyse arbitrary target sites in future.
• Written manuscript.

Folding simulations in side chain lattice protein models

Side chain lattice protein models are a reasonable and necessary extension of the widely used backbone lattice protein models. To enable folding simulations a structural neighborhood relation, a so called move set, has to be defined that is utilizes that enable e.g. Monte-Carlo simulations of the folding process. The thesis presents the K-local move set, a local move set defined generically for lattice protein models. The K-local move set is defined for both backbone and side-chain protein models via constraint satisfaction problems. The use of the constraint-based approach enabled its use for an arbitrary lattice. The K-local move set is then used for a simulation procedure for side-chain protein structures in the face-centered cubic lattice using real protein sequences and structures.

Infering RNA Stem-Loop descriptors from multiple sequence-structure alignments for an indexed-based RNA search method

RNA can be grouped into certain RNA families according structural and functional similarities. Currently, the Rfam 9.1 database ( http://rfam.sanger.ac.uk ) contains more than 1300 such families. We have already developed a fast index-based (with affix-trees) search method for RNAs. Here, the query is a descriptor and it consists of a stem-loop structure with possible wildcards at different positions. The more sequence information is given the faster is the underlying index-based search engine. On the other hand, if too much sequence information is given, related, but inexact matching stem-loop structure would not be found. Therefore, the goal of this bachelor thesis is to derive such descriptors from Rfam seed-alignments (or other multiple RNA sequence-structure alignments) too feed them into the search engine. If each necessary single descriptor gives a match within a certain region, one could infer a match of the underlying RNA family. A descriptor can been seen therefore as a necessary local motif of an RNA familiy.

Approximate pattern matching under generalised edit distance and extensions to suffix array library

The approximate pattern matching problem is the problem of finding all occurences of a certain pattern in a usually much longer text allowing for a fixed error threshold in the matching. The problem has been studied extensively and many very good solutions were found. However, general enough instances of the problem, namely those allowing for generalised error functions, remain with without satisfactory algorithms. This thesis is an attempt to provide such a solution. The new method provided relies on the suffix array data structure to preprocess the text linearly and allow later for fast queries. The new algorithm has the two desirable features of having a fairly simple explanation and implementation and having space and time bounds independent of the size of the alphabet, allowing for arbitrarily large alphabets. Furthermore, the new algorithm handles wildcards quite well while retaining the same time and space worst-case complexities. The algorithms are compared on genuine genetic data from Zebrafish genome and the results are presented. Finally, a parallelized version of the algorithm is presented on CREW-PRAM model of computation. In addition to presenting the new algorithm, several contributions were made to an existing affix array library.

A Library for Index-based Bidirectional Pattern Search with an Application to RNA Structural Motifs

In dieser Masterarbeit präsentieren wir sowohl bekannte, als auch neue Algorithmen zur effzienten Konstruktion und Verwendung von Indexdatenstrukturen. Diese Datenstrukturen haben mannigfaltige Anwendungsmöglichkeiten im Bereich des String-processings. Insbesondere können durch sie Mustersuchen in indexierten Texten beschleunigt werden, wodurch sie eine wichtige Rolle in der Analyse biomolekulare Sequenzen wie z.B. DNA- (Desoxyribonukleinsäure), RNA- (Ribonukleinsäure) und Protein-Sequenzen, spielen.

Variations of the Sankoff-Algorithm with a Focus on Heuristics

The combination of the alignment and secondary structure prediction solutions of two RNA sequences can significantly improve the accuracy of the structural predictions. The algorithm which simultaneously solves these problems tends to be computationally expensive like the original form "Sankoff Algorithm" (Sankoff, 1985). Thus, the methods which addressed this problem impose constraints that reduce the computational complexity by restricting the folding and/or alignment and thus make the Sankoff algorithm more practical. In this thesis, reviewing the different Sankoff-style methods in such a way that compares them corresponding to the Sankoff algorithm, through the parallels and differences. As well as, the focus is on the heuristics (i.e. the imposed constraints on the alignments and/or the structures) and comparing between them.

Abstractions for barrier estimations in RNA energy landscapes

RNAs take part in diverse processes in cells. Energy landscapes can be used to characterize the structural space of an RNA and thus can help us to better understand the processes in which RNAs are involved. The task of estimating energy barriers in RNA landscapes is important in many practical problems such that kinetic RNA folding (Geis et al., 2008) and search for bistable RNA molecules (Flamm et al., 2001). A few approaches has been developed to solve this problem. They need to be improved in two ways: improve time complexity and, at the same time, improve the accuracy of estimations. This master thesis has a task of investigating possible solutions to above-mentioned problem. We apply shape abstraction to the barrier height estimation problem. In the master thesis a number of precise algorithms based on this abstraction have been developed and compared to already existing ones.

Kinetics of RNA-RNA hybridization

There are two conceptually different approaches to predict probable structures of RNA molecules: thermodynamic and kinetic modeling of RNA folding. While purely thermodynamic approaches (e.g. RNAfold) solely consider thermodynamic properties to determine favourable structures, kinetic approaches (e.g. Kinfold) consider the structural changes over a timeframe in addition to their thermodynamic properties. While thermodynamic RNA structure prediction has been extended to RNA-RNA interactions, there doesn't seem to exist a kinetic modeling approach yet. The goal of my diploma thesis will be the implementation and evaluation of two kinetic folding algorithms for RNA-RNA interactions based on stochastic simulations using a Gillespie algorithm as well as macro states.

Signifikanz von RNA-RNA Interaktionen und RNA Sequenz-Struktur Alignments

In dieser Arbeit wurden Signifikanzuntersuchungen für die am Lehrstuhl für Bioinformatik der Universität Freiburg entwickelten Programme LocARNA und IntaRNA angestellt. LocARNA bewertet anhand eines Sequenz-Struktur-Alignments die strukturelle, sowie sequenzielle Ähnlichkeit zwischen zwei ncRNA Sequenzen, IntaRNA bewertet mögliche Bindestellen zwischen ncRNA und mRNA, unter Berücksichtigung nicht nur der Sequenz, sondern auch der Sekundärstruktur der Sequenzen. Es wurde analysiert, wie sich die ausgegebenen Bewertungen dieser zwei Programme in Abhängigkeit von den Eigenschaften Länge, AU gegen GC Anteil und minimaler freier Energie der eingegebenen Sequenzen verändern. Dazu wurden große Mengen an zufälligen Sequenzen erzeugt, die Verteilung bei Sequenzen mit gleichen Eigenschaften untersucht und geprüft, wie sich die Verteilung bei Variation der Länge und des AU zu GC Mengenverhältnisses ändert. Bei LocARNA wurde mit den Daten eine Support Vektor Maschine trainiert, die nun für Sequenzpaare die zu erwartende Verteilung angeben kann. Mit dieser Verteilung als Nullmodell ist es möglich, die P-Werte, und damit die Signifikanz, der von LocARNA ausgegebenen Bewertungen zu bestimmen. Bei IntaRNA wurde festgestellt, dass die ncRNA Sequenzen einen Einfluss auf die Ausgabe haben, der sich nicht allein durch Länge, AU-Anteil und freier Energie erklären lässt. Hier sind weitere Untersuchungen nötig, bevor Gesetzmäßigkeiten bestimmt werden können mit denen die Signifikanz bewertet werden kann.

Effiziente Algorithmen zum paarweisen Sequenz-Struktur-Alignment unter Beachtung von Pseudoknoten

Sehr viele Probleme, die sich mit Berechnungen von RNA beschäftigen, die Pseudoknoten enthalten, sind NP-hart. Am bekanntesten sind hier die Fälle der Strukturvorhersage und der Berechnung des Alignments. Im Bereich der Strukturvorhersage wurden schon verschiedenste Algorithmen vorgestellt. Diese beinhalten allerdings Einschränkungen was die Art der vorkommenden Pseudoknoten angeht, um effizient arbeiten zu können. Diese hier vorgestellte Arbeit leistet einen Beitrag zu einem neuen Algorithmus, der vom Lehrstuhl für Bioinformatik vorgestellt wurde. Mit seiner Hilfe ist es möglich, effizient Sequenz-Struktur-Alignments von RNA Sequenzen zu berechnen, die Pseudoknoten enthalten. Hierfür wird auf das Prinzip der dynamischen Programmierung zurückgegriffen. Der Algorithmus besteht dabei im Wesentlichen aus zwei Teilen. Zuerst wird eine der beiden Sequenzen in einen Parsetree zerlegt und anschliessend das Alignment gebildet. Das Alignment profitiert hierbei von Einschränkungen auf bestimmte Klassen von Pseudoknoten auf die selbe Weise wie die jeweilige Strukturvorhersage. Dies hat zur Folge, dass die Komplexität des Alignments nur um einen linearen Faktor in Bezug auf den jeweiligen Vorhersagealgorithmus steigt. Diese Arbeit beschäftigt sich mit dem ersten Teil, dem Berechnen einer Zerlegung der ersten Sequenz. Hier werden verschiedene Methoden untersucht, wie dies geschehen kann, sowie diese hinsichtlich ihrer Auswirkungen auf das spätere Alignment analysiert.

Multiples Sequenz-Struktur-Alignment von RNAs fester Eingabestrukturen mit konsistenzbasierter Erweiterung

Partition function alignment of rnas.

ncRNAs are observed to have important roles in transcription, translation and in post-translation activities. Computational detection of ncRNAs requires sophisticated methods which take into account structural conservation apart from sequence information. We present a new pairwise sequence-structure alignment algorithm, LocARNAp with an aim of obtaining more accurate multiple alignments than its ancestor LocARNA by using partition function of alignments and consistency based transformation. LocARNAp is dynamic programming based sequence-structure alignment algorithm which computes posterior probabilities of edge alignment from the partition function of pairwise alignments. Obtained posterior probabilities are then consistency transformed to include information about other sequences, and thereby making an improvement in multiple alignment computed using mLocARNA. We compare the multiple alignments generated by LocARNAp to those obtained from LocARNA, LARA, STRAL and FoldAlign using benchmark - BRAliBase 2.1�s datasets and extensively study the effect of each parameter setting on the alignment quality. The analysis of results suggests that our algorithm obtains overall better quality of results compared to its ancestor - LocARNA and other algorithms. While there is a huge scope of further improvements, LocARNAp develops a strong foundation for further research in this direction.

Ein Hybdridkinetik Ansatz für RNA Faltungswahrscheinlichkeiten

Es werden in der derzeitigen Forschung zwei wesentliche Ansätze für die RNA Faltungsvorhersage verwendet. Zum einen die direkte Simulation des Faltungsprozesses, bei der über viele Iterationen hinweg stochastisch Wahrscheinlichkeiten für verschiedene Faltungen/Strukturen bestimmt werden. Dies ist die am häufigsten genutzte, aber auch bei weitem aufwendigste Methode. Daher wird oft auf Kinetiken ausgewichen, welche auf einer Vereinfachung der Energielandschaft basieren. Energielandschaften sind hierbei eine diskrete Beschreibung des Strukturraums einer RNA, in dem sich der Faltungsprozess abspielt. Zum einen werden ganze Teile der Energielandschaft zu sogenannten Macrostates zusammengefasst, zum anderen wird die Landschaft oft vereinfachend durch BarrierTree repräsentiert wodurch Adjazenzinformationen zugunsten einer effizienten Berechnung verworfen werden. In dieser Arbeit wird ein Hybridansatz vorgestellt, welcher die häufig verwendeten Macrostate- und Arrheniuskinetiken miteinander verknüpft. Für den unteren Bereich der Energielandschaft wird die Macrostatekinetik verwendet, während im oberen Bereich durch ein Sampling der Übergangshöhen eine Arrheniuskinetik möglich wird. Diese beiden Kinetiken arbeiten jedoch mit unterschiedlichen Zeitfaktoren, so dass eine Skalierung der jeweiligen Ratenmatrizen nötig ist, um die resultierende Ratenmatrix zu verwenden. Die Arbeit untersucht Möglichkeiten der Hybridisierung beider Kinetiken, und zeigt grundsätzliche Limitierungen des Kinetikansatzes auf. Zudem wird eine Metrik für den Vergleich von Kinetiken vorgestellt, um optische Unterschiede im Faltungsverhalten zu quantifizieren. Dieses Mass wird schliesslich verwendet, um die Qualität der Hybridkinetik zu bewerten.

Sampling von Folding funnels in diskreten Energielandschaften

Im Rahmen des Projektes soll eine Methode umgesetzt werden, um den folding funnel von Modellmolekuelen mit diskreten Energielandschaften zu schaetzen. Der zu erstellende Ansatz baut direkt auf vorangegangenen Arbeiten auf und ergaenzt diese um neue Datenstrukturen und Methoden. Die Implementierung soll auf der C++ Programmierbibliothek Energy Landscape Library (ELL) aufbauen und diese ggf. ergaenzen. Zudem soll ein standalone Programm entwickelt werden, mit dem direkt folding funnel Studien ermoeglicht werden. Fuer einige gegebene, vorhandene Modelle sollen hierzu die gesampelten Daten mit bestehenden exakten Studien verglichen werden, um Qualitaet und Laufzeit des neuen Ansatzes zu bestimmen.. F�r Hintergrunddetails siehe pdf-Version der Projektbeschreibung.

Constraint Approach for Protein Structure Prediction in the Side Chain HP Model

Protein structure prediction has been always a very interesting problem to solve, especially in the last 10 years. Many previous methods tried to focus on HP model prediction, however most of those methods have the drawback of giving approximate solutions. Another drawback of most of those works is that they ignore the representation of the side chains of the amino acids. In this master thesis, we develop an approach of a concrete constraint model that takes the side chains of the amino acids into consideration and gives the exact solutions for a given sequence in the side chain HP model in terms of minimizing the energy, without any approximation. In this work we also present the obtained results of the predication model and show some important statistics especially about the degeneracy. In addition to that we present some interesting results on generating protein like sequences, although this is a hard task in the model. The protein like sequences can be found in a very low probability in random sequences in the side chain model as we explain in this thesis.

Core Construction in the Cubic Lattice

Für das kombinatorische Problem der Kernkonstruktion existieren bereits ausführliche theoretische Vorarbeiten, sowie eine Implementierung in Mozart/Oz. Es geht daher ausdrücklich um eine effiziente Reimplementierung. C++- oder Javakenntnisse, sowie Erfahrung mit Constraintprogrammierung, bzw. die Bereitschaft zur tieferen Einarbeitung in diese Technik sind für das Projekt vorausgesetzt.

Combining the results of different motif discovery programs for de novo prediction of TFBS - A critical approach

The project tackled the question : Can we trust the results of tools for de novo motif (TFBS) detection? If not, how can we improve the results?

Strukturraumanalyse von Gitterproteinen unter Verwendung von Pull Moves

Pairwise comparison of rna secondary structures via exact pattern matches.

In this thesis we have developed two pairwise comparison methods on the basis of exact matching substructures, called exact pattern matches. In a first step, a set of overlapping and crossing substructures for two nested RNA secondary structures is found with the approach of pairwise common substructures from Siebert/Backofen. Our first method deals with the task to identify the best global subset of Non-Crossing exact pattern matches for two given RNAs. In relation to the LAPCS problem, we call this problem the Longest Common Subsequence of Exact RNA Patterns. The developed dynamic programming algorithm needs O(n�m�) time and O(nm) space. Our second approach detects (local) clusters of exact pattern matches. A cluster is a Non-Crossing arrangement of exact pattern matches with a distance constraint between the substructures included in a cluster. The developed clustering strategy to find clusters is fast and flexible enough for different analytical problems. We have tested both methods with two Hepatitis C virus RNAs and two 16s ribosomal RNAs. The results show that both methods are able to identify significant similarities between two RNA secondary structures in a fast way.

Identifying Key Regulators in Genetic Networks

Genetic Regulatory Networks are a method of representing the complex assemblages of interconnected genes, proteins and other molecules. Components are represented as nodes, and activations and repressions between them are represented as labeled edges. Within these networks are Key Regulators; these are nodes capable of regulating a sub-network of the network. The task at hand is to present a model for genetic regulatory networks and to implement a means to identify the most suitable key regulator within the network. In addition, cycles representing positive or negative feedback loops increase the complexity of the task. The model is further refined by considering constraints such as choosing a key regulator that regulates a certain sub-network but has no effect on another sub-network.

3D-Structure-Motifs Aware Sequence Structure Alignment of RNAs

Comparison of RNAs is mainly based on information about the sequence and their secondary structure. The function of RNAs on the other hand is based on their 3D-Structure, which is hard to determine. However, there are wide-spread 3D motifs which can be identified more easily. Such a motif can be defined, due to Eric Westhof, as an ordered assembly of non-watson-crick basepairs within a helix. Current sequence structure alignment methods are not aware of such motifs. However, these motifs can give strong guidance for such alignments. The project's aim is to integrate the knowledge about motifs into the recent tool LocARNA, which is a program for simultaneous folding and alignment of RNAs. The dynamic programming algorithm should be modified to detect the motifs and tested on biological data.

Exploration of biopolymer energy landscapes via random sampling

The structures of RNA molecules and proteins, which are both important biopolymers, are commonly assumed to be uniquely determined by their sequences. The structures of these biomolecules are in turn necessary to carry out the molecules' biological functions. Discretized structure models provide a coarse-grained description of the molecular structure, which is necessary to perform computational studies. In this research, RNA molecules were modeled as secondary structures for RNA, and proteins were modeled as self-avoiding walks on a lattice. The structure formation process of biopolymers is crucially determined by the properties and the topology of the underlying energy landscape, in which the folding proceeds. Typical characteristics of the energy landscape, like the number of local optima, the basin distribution as well as the transition states between the optima, can be visualized by barrier trees. Barrier trees provide a reduced representation of energy landscapes, which can be used to study the dynamical behavior of biopolymer folding. The research described in this thesis aimed to present a generic, problem-independent approach for the generation of barrier trees.

Efficient solving of alignment-problems with side conditions using constraint techniques

Many problems and open questions of modern microbiology demand the comparison of RNA, DNA or protein sequences. The computational effort in performing these calculations is high and microbiology urges the development of efficient tools. An important requirement is the capability of these tools to deal with certain constraints. Examples might be a specified number of matches of two compared sequences within certain regions or a consideration of the secondary structure of a proteine. Subject of this thesis is the efficient alignment of sequences, where we focus especially on different constraints on the alignment and how to combine these constraints, even in multiple alignments.

Vollständige Aufzählung der optimalen Strukturen von Gitterproteinen durch dynamische Zerlegung des assoziierten Constraint Satisfaction Problems

The standard depth first search (DFS) method to solve Constraint Satisfaction Problems (CPSs) shows much redundant work if the CSP contains several unsolved independent partial problems. This is a frequent observation when constraint programming is applied to solve the structure prediction problem in the HP model. Within this thesis, an implementation of a dynamically decomposing search strategy is developed and implemented. The resulting prototypical implementation is applied to the structure prediction problem and yields significant speedups compared to standard DFS. This speedup is neccessary e.g. to allow for high throughput degeneracy calculation of lattice proteins in the HP model.

MuLoRa - Ein Ansatz für multiple, lokale RNA-Sequenz-Struktur-Alignments

Merkmalauswahlverfahren zur lokalisierung der bindungsstellen von transkriptionsfaktoren, signifikanz von rna sequenz-struktur-motiven.

Medical Bioinformatics and Computational Modelling

PhD students at the Bioinformatics Laboratory

In Progress 

• Balashova, D. Repertoire sequencing . University of Amsterdam, Amsterdam. ARCAID . Marie Curie COFUND, Horizon 2020. Van Kampen, A.H.C. (promotor), De Vries N. (promotor), Greiff V. (co-promotor).
• Lashgari, D. Kinetic maturation in the Germinal Center . University of Amsterdam, Amsterdam. Supported by AMC. Van Kampen, A.H.C. (promotor), Van Gils, M. (co-promotor), Hoefsloot, H. C. (co-promotor).
• Mahamune, U. Single Cell RNAseq and computational modelling .   University of Amsterdam, Amsterdam. ARCAID . Marie Curie COFUND, Horizon 2020. Van Kampen, A.H.C. (promotor), Moerland, P.D. (co-promotor), E.G.M. van Baarsen (co-promotor).
• Valiente, R. G. Development of multiscale mathematical models of the germinal center (GC) to study its role in B-cell lymphoma (BCL) and/or rheumatoid arthritis (RA). (PhD thesis). University of Amsterdam, Amsterdam. COSMIC . Marie Curie ITN, Horizon 2020. Van Kampen, A.H.C. (promotor), De Vries, N. (promotor), Hoefsloot, H. C. (co-promotor), Guikema, J. E. (co-promotor).
• Stobbe, M. (2012). 18 October 2012. The road to knowledge: from biology to databases and back again. University of Amsterdam, Amsterdam. NBIC BioRange. Van Kampen,  A.H.C. (promotor),  Moerland, P. D. (co-promotor). [ UvA-DARE ]
• Shahand, S. (2015). 29 October 2015. Science gateways for biomedical big data analysis. University of Amsterdam, Amsterdam. COMMIT. Van Kampen,  A. (promotor), Olabarriaga, S. (co-promotor). [ UvA-DARE ]
• Reshetova, P. (2017). 2 March 2017. Use of Prior Knowledge in Biological Systems Modelling. University of Amsterdam, Amsterdam. NBIC Biorange. Van Kampen,  A.H.C (promotor), Smilde, A.  (promotor), Westerhuis, J.  (co-promotor). [ UvA-DARE ]
• Tejero Merino, E. (2022). 7 November 2022 Multiscale modelling of plasma cell differentiation in the Germinal Center. University of Amsterdam, Amsterdam. Supported by AMC. Van Kampen, A.H.C. (promotor), Guikema, J.E.J. (co-promotor), Hoefsloot, H. C. (co-promotor). [ PhD thesis] [ UvA-DARE ]
• Nandal, U. (2023). Computational approaches for biological data integration. University of Amsterdam, Amsterdam. NBIC BioRange. Van Kampen, A.H.C. (promotor), Moerland, P.D. (co-promotor). [ UvA-DARE ]

Co-supervised PhD students from other research groups

In Progress

• Balzaretti, G. Repertoire Sequencing . University of Amsterdam, Amsterdam. De Vries, N. (promotor), Van Kampen, A.H.C. (promotor).
• Lermo Jimenez, M. Epigenetics and breast cancer drug resistance . University of Amsterdam, Amsterdam. Verschure P. J. (promotor), Moerland, P.D. (co-promotor).
• Olivieri, A. Repertoire Sequencing. University of Amsterdam, Amsterdam. ARCAID , Marie Curie COFUND, Horizon 2020. De Vries, N. (promotor), Van Kampen, A.H.C. (promotor).
• Pollastro, S. Repertoire Sequencing . University of Amsterdam, Amsterdam. De Vries, N. (promotor), Van Kampen, A.H.C. (co-promotor).
• Stratigopoulou, M. Germinal Center and B-cell Lymphoma . University of Amsterdam, Amsterdam. COSMIC. Marie Curie ITN, Horizon 2020. Van Kampen, A.H.C. (promotor), Van Noesel, C. J. (promotor), De Vries, N. (co- promotor), Guikema, J. E. (co-promotor).
• Sontrop, H. (2015). 15 January 2015. A critical perspective on microarray breast cancer gene expression profiling. TU Delft, Delft. NBIC BioRange. Reinders, M. (promotor), Moerland, P. D. (co-promotor). [ Link ]
• Beckman, W. (2021). 17 August 2021. The Role of Epigenetics in Transcriptional Stochasticity and the Implications for Breast Cancer Drug Resistance . University of Amsterdam, Amsterdam. EpiPredict. Marie Curie ITN, Horizon 2016. Verschure P.J. (promotor), Van Kampen, A.H.C. (promotor). [ UvA-DARE ]
• Barros, R. S. (2022). 1 November 2022 High performance computing for clinical medical imaging . University of Amsterdam, Amsterdam. Henk Marquering (promotor), Van Kampen, A.H.C. (promotor), Olabarriaga, S. (co-promotor). [ UvA-DARE ]
• Anang, D. (2023) 6 November 2023. B and T Cell Immune Responses in Rheumatoid Arthritis and Myositis. In Search for the Immunological Drummers and Dancers . University of Amsterdam, Amsterdam. COSMIC . Marie Curie ITN, Horizon 2020. De Vries, N. (promotor), Van Kampen, A.H.C. (promotor), van Baarsen, E.G.M. (co-promotor). [ UvA-DARE ]
• Wegdam, W. (2024). In search of protein biomarkers in ovarian cancer and Gaucher disease. University of Amsterdam, Amsterdam. Aerts J.M.F.G. (promotor), Kenter, G.G.  (promotor), Moerland, P.D. (co-promotor). [ UvA-DARE ]

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PhD thesis Umesh Nandal

compendiumdb

Participation in single-cell genomics 2022 conference

PhD thesis Elena Merino Tejero

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Bioinformatics & Systems Biology Research Areas

The Bioinformatics & Systems Biology doctoral program covers a wide array of topics to address questions in biomedical research from novel algorithm development to the application of bioinformatics tools for innovative research.

The expertise of the participating faculty members varies widely from pure wet lab research to pure computational research. Students have the option to choose co-mentors to provide bioinformatics and biological expertise or to or work exclusively with one mentor with bioinformatics expertise. Most of the research projects involve bioinformatic data analyses either preceding (hypothesis-generation) or succeeding (hypothesis-testing) an experimental component.

New method or algorithm development in bioinformatics

Active method/algorithm development in the areas of cancer genomics, neuroinformatics, immunoinformatics, microbial metagenomics, structural bioinformatics, graph-theory based biological network analysis, and natural language processing (NLP).

Application of bioinformatics in systems biology research

Research in this area typically involves both experimental and bioinformatics aspects of a project in a basic science research laboratory at UNMC. Broad research topics include analysis of multi-omic datasets (genomics, transcriptomics, proteomics, metabolomics, etc), pathway and network analyses, agent-based modeling, drug repurposing, microbiome characterization in health and disease states, etc.

Mathematical/statistical applications in bioinformatics

Developing novel statistical approaches to study splice variants in human cancers or immunogenicity of protein variants, survival analysis and disease risk prediction using molecular and clinical datasets, mathematical modeling to characterize/visualize high-dimensional experimental data in biology, etc.

Thesis or Dissertation

Each graduate student in the program will work on a dissertation project under dual mentorship, consisting of a primary advisor who is Program Training Faculty, and a co-advisor who may or may not be Program Training Faculty, but must be from a different disciplinary area.

It is expected that the student will meet at least annually with the committee to update the members on his or her progress. As a partial fulfillment for the PhD degree, the student will submit a complete dissertation to be evaluated by a doctoral committee chosen by his or her mentors in consultation with the bioinformatics steering committee. The doctoral dissertation will be submitted to each member of the doctoral committee at least four weeks before the final examination. The student will defend his or her final thesis after the committee's evaluation and will pass or fail depending on the committee's decision.

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• Master’s Degree Program
• Master's Thesis

Master's Thesis

Master’s thesis with accompanying colloquium (30 credits).

The master’s thesis is meant to prove the student’s ability to work independently on an advanced problem from the bioinformatical field using scientific methods, as well as the student's ability to evaluate the findings appropriately and to depict them both orally and in written form in an adequate manner. (SPO 2019, § 9)

If the study regulations of 2012 apply to you, please have a look here .

If you're looking for a thesis , here are some suggestions.

Unofficial Extract from the Regulations:

• Students can only be admitted to the master's thesis if they have successfully completed modules totaling 60 credits or more within the master's degree program.
• For the registration of the master thesis please use the form "Registration for the master thesis". You can find it on the pages of the examination office ! Important: Be sure to register your master thesis right at the beginning of work! Otherwise you risk that the examiner combination or the topic will not be accepted!
• The master's thesis should be approximately 70 pages in length.
• The processing time is 23 weeks . Note: An extension is not possible. If your thesis is delayed for an important reason (for which you are not responsible), please contact the Examination Office with the relevant supporting documents.
• The written part must be written in English.
• The master's thesis must be evaluated by two authorized examiners . One of the two examiners should be the supervisor of the master's thesis. At least one of the two authorized examiners* must be involved in teaching the master's program and simultaneously be a lecturer at the Department of Mathematics and Computer Science or the Department of Biology, Chemistry, Pharmacy of the Freie Universität Berlin or at Charité.
• If approved by the examining board, the work on the master's thesis can also be done externally at a suitable business or scientific or research institution, as long as scientific and scholarly supervision by an examiner in the program in bioinformatics is ensured.
• The master's thesis is accompanied by a colloquium , which usually takes place in the assigned working group during the processing time. Students are expected to give a one-time presentation lasting approximately 30 minutes on the progress of their master's thesis.
• The master's thesis must be submitted in electronic form (PDF), by e-mail to the examination office. When submitting the thesis, the student must certify in writing that he or she has written the thesis independently and has not used any sources or aids other than those specified. Use the Declaration of Authorship provided by the examination office for this purpose.

*These are usually all PhD scientists involved in teaching in the Master's program in Bioinformatics. However, persons who are not directly involved in teaching may also be authorized. In case of doubt, please contact the examination office , which can check if a certain examiner or combination of examiners is possible or not. Note: The two examiners of a master thesis should come from different working groups.

The Informationen & Anleitungen of the examination office offer further information concerning the registration and submitting regulations of the master’s thesis (in german). The registration form is available in English.

Please note: If you have completed all the coursework and only need to finish the master's thesis, you no longer need to be enrolled, (but you are allowed to, of course).

Every summer semester the Mentoring organizes the workshop “How to write a bachelor’s / master’s thesis in bioinformatics”. Here you receive helpful tips and are free to ask your questions.

Here you can find a compilation of important information (FAQ Abschlussarbeit, in German).

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Bioinformatics research topics ideas.

List of Bioinformatics Research Topics Ideas for.

1. Data access control in the cloud computing environment for bioinformatics 2. The bioinformatics toolbox for circRNA discovery and analysis 3. Want to track pandemic variants faster? Fix the bioinformatics bottleneck 4. A constructivist-based proposal for bioinformatics teaching practices during lockdown 5. Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research 6. Therapeutic targets and signaling mechanisms of vitamin C activity against sepsis: a bioinformatics study 7. Bioinformatics helping to mitigate the impact of COVID-19–Editorial 8. Network bioinformatics analysis provides insight into drug repurposing for COVID-19 9. Deep learning-based clustering approaches for bioinformatics 10. User-friendly bioinformatics pipeline gDAT (graphical downstream analysis tool) for analysing rDNA sequences 11. Bioinformatics analysis of SARS-CoV-2 to approach an effective vaccine candidate against COVID-19 12. The Bio3D packages for structural bioinformatics 13. Metabolic Basis of Creatine in Health and Disease: A Bioinformatics-Assisted Review 14. The European Bioinformatics Institute: empowering cooperation in response to a global health crisis 15. Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization 16. Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de. NBI) exemplified by selected use cases 17. Bioinformatics resources for SARS-CoV-2 discovery and surveillance 18. Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive … 19. Insights into mineralocorticoid receptor homodimerization from a combined molecular modeling and bioinformatics study 20. Analysis and identification of novel biomarkers involved in neuroblastoma via integrated bioinformatics 21. Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2 22. BioContainers Registry: Searching Bioinformatics and Proteomics Tools, Packages, and Containers 23. A Review of Pharmacological and Toxicological Effects of Sophora tonkinensis with Bioinformatics Prediction 24. Application of Multilayer Network Models in Bioinformatics 25. Identification of potential biomarkers of polycystic ovary syndrome via integrated bioinformatics analysis 26. Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma 27. Bioinformatics tools developed to support BioCompute Objects 28. Bioinformatics-based prediction of conformational epitopes for Enterovirus A71 and Coxsackievirus A16 29. Emulsifier peptides derived from seaweed, methanotrophic bacteria, and potato proteins identified by quantitative proteomics and bioinformatics 30. … of the molecular targets and mechanisms of compound mylabris capsules for hepatocellular carcinoma treatment through network pharmacology and bioinformatics … 31. Improving the Thermostability of Xylanase A from Bacillus subtilis by Combining Bioinformatics and Electrostatic Interactions Optimization 32. Physical exercise, obesity, inflammation and neutrophil extracellular traps (NETs): a review with bioinformatics analysis 33. MMP7 as a potential biomarker of colon cancer and its prognostic value by bioinformatics analysis 34. Bioinformatics: new tools and applications in life science and personalized medicine 35. OPCML Methylation and the Risk of Ovarian Cancer: A Meta and Bioinformatics Analysis 36. … mechanisms of GegenQinlian decoction on improving insulin resistance in adipose, liver, and muscle tissue by integrating system pharmacology and bioinformatics … 37. Determination of Potential Therapeutic Targets and Prognostic Markers of Ovarian Cancer by Bioinformatics Analysis 38. Structure–function engineering of novel fish gelatin-derived multifunctional peptides using high-resolution peptidomics and bioinformatics 39. Chemical composition, biological properties and bioinformatics analysis of two Caesalpina species: A new light in the road from nature to pharmacy shelf 40. … Cloud-Based Tutorials That Combine Bioinformatics Software, Interactive Coding, and Visualization Exercises for Distance Learning on Structural Bioinformatics 41. Functional characterization of ABCC8 variants of unknown significance based on bioinformatics predictions, splicing assays, and protein analyses: Benefits for the … 42. Integrative pharmacological mechanism of vitamin C combined with glycyrrhizic acid against COVID-19: findings of bioinformatics analyses 43. NGS-µsat: Bioinformatics framework supporting high throughput microsatellite genotyping from next generation sequencing platforms 44. Integrated bioinformatics analysis for the identification of key genes and signaling pathways in thyroid carcinoma 45. Integrative bioinformatics and omics data source interoperability in the next-generation sequencing era 46. Comprehensive bioinformatics analysis reveals kinase activity profiling associated with heart failure 47. Nimodipine attenuates dibutyl phthalate-induced learning and memory impairment in kun ming mice: An in vivo study based on bioinformatics analysis 48. Construction of circRNA-miRNA-mRNA network in the pathogenesis of recurrent implantation failure using integrated bioinformatics study 49. Bioinformatics analysis of differentially expressed miRNAs in non-small cell lung cancer 50. A systematic evaluation of bioinformatics tools for identification of long noncoding RNAs 51. Using Integrated Bioinformatics Analysis to Identify Abnormally Methylated Differentially Expressed Genes in Hepatocellular Carcinoma 52. Bioinformatics analysis of the microRNA-mRNA network in sebaceous gland carcinoma of the eyelid 53. A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding 54. Bioinformatics analysis of candidate genes involved in ethanol-induced microtia pathogenesis based on a human genome database: GeneCards 55. Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression 56. Integrative analysis of miRNA–mRNA network in high altitude retinopathy by bioinformatics analysis 57. Screening and verification of hub genes involved in osteoarthritis using bioinformatics 58. Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics 59. TRIB3 Promotes the Malignant Progression of Bladder Cancer: An Integrated Analysis of Bioinformatics and in vitro Experiments 60. Identified GNGT1 and NMU as Combined Diagnosis Biomarker of Non-Small-Cell Lung Cancer Utilizing Bioinformatics and Logistic Regression 61. A bioinformatics WGS workflow for clinical Mycobacterium tuberculosis complex isolate analysis, validated using a reference collection extensively characterized with … 62. A bioinformatics Approach for Identification of the core ontologies and signature genes of Pulmonary Disease and Associated Disease 63. Author Correction: Single-cell RNA sequencing technologies and bioinformatics pipelines 64. A new bioinformatics tool to recover missing gene expression in single-cell RNA sequencing data 65. Comprehensive analysis of PLOD family members in low-grade gliomas using bioinformatics methods 66. Identification of four genes and biological characteristics associated with acute spinal cord injury in rats integrated bioinformatics analysis 67. Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis 68. Identification of Inflammatory Genes, Pathways, and Immune Cells in Necrotizing Enterocolitis of Preterm Infant by Bioinformatics Approaches 69. Bioinformatics Resources for RNA Editing 70. … /immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics … 71. A Critical Review on the Application of Artificial Neural Network in Bioinformatics 72. Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning 73. Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach 74. Key Genes and Molecular Mechanism Investigation in the Synthesis of Maize Quercetin Based on SNP and Bioinformatics Analysis 75. Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms 76. A Bioinformatics Systems Biology Analysis of the Current Oral Proteomic Biomarkers and Implications for Diagnosis and Treatment of External Root Resorption 77. Bioinformatics Analyses of Potential miRNA-mRNA Regulatory Axis in HBV-related Hepatocellular Carcinoma 78. … the Mechanisms and Molecular Targets of Qishen Yiqi Formula for the Treatment of Pulmonary Arterial Hypertension using a Bioinformatics/Network Topology-based … 79. Introduction to Unsupervised Learning in Bioinformatics 80. Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses 81. Bioinformatics analysis and biochemical characterisation of ABC transporter-associated periplasmic substrate-binding proteins ModA and MetQ from Helicobacter … 82. Identification of biomarkers and construction of a microRNA mRNA regulatory network for clear cell renal cell carcinoma using integrated bioinformatics … 83. Identifying the p65-Dependent Effect of Sulforaphene on Esophageal Squamous Cell Carcinoma Progression via Bioinformatics Analysis 84. Clinical heterogeneity of the SLC26A4 gene in UAE patients with hearing loss and bioinformatics investigation of DFNB4/Pendred syndrome missense mutations 85. Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis 86. Bioinformatics in Plant Pathology 87. Identification of hub genes in triple-negative breast cancer by integrated bioinformatics analysis 88. Functional Bioinformatics Analyses of the Matrisome and Integrin Adhesome 89. Identification of potential markers for differentiating epithelial ovarian cancer from ovarian low malignant potential tumors through integrated bioinformatics … 90. Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus 91. … Neurotrophic Factor Functions as a Potential Candidate Gene in Obstructive Sleep Apnea Based on a Combination of Bioinformatics and Targeted Capture … 92. Bioinformatics analysis indicates that microRNA 628 5p overexpression may alleviate Alzheimer’s disease by targeting TYROBP 93. POS0851 IDENTIFICATION OF HUB GENES AND PATHWAYS IN DERMATOMYOSITIS BY BIOINFORMATICS ANALYSIS 94. Bioinformatics analysis of Myelin Transcription Factor 1 95. Development and Optimization of Clinical Informatics Infrastructure to Support Bioinformatics at an Oncology Center 96. A Systems Bioinformatics Approach to Interconnect Biological Pathways 97. Bioinformatics identification of green tea anticancer properties: a network-based approach 98. Diatom metabarcoding and microscopic analyses from sediment samples at Lake Nam Co, Tibet: The effect of sample-size and bioinformatics on the identified … 99. Bioinformatics Applied to the Development of Biomolecules of Pharmaceutical Interest 100. Bioinformatics analysis of WRKY transcription factors in grape and their potential roles prediction in sugar and abscisic acid signaling pathway 101. The clinical and prognostic significance of LGR5 in GC: A meta-analysis of IHC assay and bioinformatics analysis. 102. Bioinformatics Investigation and Contribution of Other Chromosomes Besides Chromosome 21 in the Risk of Down Syndrome Development 103. Network Pharmacological Analysis through a Bioinformatics Approach of Novel NSC765600 and NSC765691 Compounds as Potential Inhibitors of CCND1/CDK4 … 104. Cohort Identification for Translational Bioinformatics Studies 105. Advances in Omics and Bioinformatics Tools for Phyllosphere Studies 106. Harmonic Progression in Bioinformatics and Recurrent Series in Inherited Biostructures 107. OverCOVID: an integrative web portal for SARS-CoV-2 bioinformatics resources 108. Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments 109. A Bioinformatics Tutorial for Comparative Development Genomics in Diverse Meiofauna 110. Multidrug resistance protein structure of Trypanosoma evansi isolated from buffaloes in Ngawi District, Indonesia: A bioinformatics analysis 111. Integrative Bioinformatics Analysis Reveals Noninvasive miRNA Biomarkers for Lung Cancer 112. Quantifying plasmid dynamics using single-cell microfluidics and image bioinformatics 113. Identification of four differentially expressed genes associated with acute and chronic spinal cord injury based on bioinformatics data 114. Identification of key pathways and gene expression in the activation of mast cells via calcium flux using bioinformatics analysis 115. Identification of Significant Genes and Therapeutic Agents for Breast Cancer by Integrated Bioinformatics 116. Using supercomputer to finish M1 Bioinformatics Exercise from Ogata Lab 117. Transcriptomic Alterations Induced by Vemurafenib After Treatment of Melanoma: A Comprehensive Bioinformatics Analysis 118. … OF MOLECULAR PHENOTYPES AND IMMUNE CELL INFILTRATION IN PSORIATIC ARTHRITIS PATIENTS’SKIN TISSUES BY INTEGRATED BIOINFORMATICS … 119. Bioinformatics: A New Insight Tool to Deal with Environment Management 120. Identification of acute spinal cord injury and autophagy-related potential key genes, pathways, and targeting drugs through bioinformatics analysis 121. Identification of Molecular Mechanisms Underlying Sex-Associated Differences in the Chronic Obstructive Pulmonary Disease through Bioinformatics Analysis 122. Bioinformatics and In Vitro Studies Reveal the Importance of p53, PPARG and Notch Signaling Pathway in Inhibition of Breast Cancer Stem Cells by … 123. Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs 124. A Comprehensive Phylogenetic and Bioinformatics Survey of Lectins in the Fungal kingdom 125. Deep networks and network representation in bioinformatics 126. Identifying Potential Prognostic Biomarkers Associated With Clinicopathologic Characteristics of Hepatocellular Carcinoma by Bioinformatics Analysis 127. Identification of key pathways and hub genes in the myogenic differentiation of pluripotent stem cell: a bioinformatics and experimental study 128. … of intestinal microbiome in a process of faecal microbiota transplantation in a patient with Clostridioides difficile infection: NGS analysis with different bioinformatics … 129. Clinical significance of long noncoding RNA MNX1-AS1 in human cancers: a meta-analysis of cohort studies and bioinformatics analysis based on TCGA datasets 130. Anticancer property of Zika virus proteins: Lack of evidence from predictive clinical bioinformatics study 131. Identification of Differentially Expressed Genes Using Deep Learning in Bioinformatics 132. Bioinformatics Analysis Predicts hsa_circ_0026337/miR-197-3p as a Potential Oncogenic ceRNA Network for Non-small Cell Lung Cancers 133. … Molecular Mechanism of Xiao Huoluo Pills in the Treatment of Cartilage Degeneration of Knee Osteoarthritis Based on Bioinformatics Analysis and Molecular … 134. A bioinformatics analysis of differentially expressed proteins in plasma exosome of acute-on-chronic liver failure patients with different prognoses 135. Bioinformatics Analyses of Serine Acetyltransferase (SAT) Gene Family in Rice (Oryza sativa) and their Expressions under Salt Stress 136. Expression profiling and bioinformatics analysis of exosomal long noncoding RNAs in patients with myasthenia gravis by RNA sequencing 137. Bioinformatics Analysis in Different Expression Genes and Potential Pathways of CD4+ Cells in Childhood Allergic Asthma 138. … Key Genes in Anaplastic Thyroid Cancer Using Bioinformatics AnalysisIdentification of Potential Key Genes in Anaplastic Thyroid Cancer using Bioinformatics … 139. … predicts poor prognosis in patients with surgically resected Lung Adenocarcinoma: A study based on Immunohistochemical Analysis and Bioinformatics 140. High-throughput screening and bioinformatics analysis of 2,000 177Lu-PSMA and Radiotherapy+ drug combinations 141. Bioinformatics Analysis of C3 and CXCR4 act as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma (ccRCC) 142. Complexity matters: Evaluating the impact of bioinformatics parameters on eukaryotic MOTU delimitation and taxonomy assignment 143. Bioinformatics analysis of the expression and role of microRNA-221-3p in head and neck squamous cell carcinoma 144. In-silico analysis of BCL2 gene using multiple bioinformatics tools to identify the most lethal mutations that are crucial for its structural and functional integrity 145. A Novel Ferroptosis-related Lncrna Prognostic Signature for Colorectal Cancer by Bioinformatics Analysis 146. Correction to “Complementary Genomic Bioinformatics and Chemical Approaches Facilitate the Absolute Structure Assignment of Ionostatin, a Linear Polyketide from … 147. Correction to: A Bioinformatics Tutorial for Comparative Genomics of Meiofauna 148. Want to track pandemic variants faster? Fix the bioinformatics bottleneck 149. Use of Bioinformatics Technologies and Databases to Teach Analysis of Genetic Sequences to Undergraduate Students in Physics, Biotechnology, and … 150. A Single-Cell Bioinformatics Analysis of the Host Transcriptional Response to Infection Consisting of Natural Combinations of Influenza A Virus Gene Segments 151. Erratum to comprehensive bioinformatics analysis of the TP53 signaling pathway in Wilms’ tumor 152. Bioinformatics Analysis of DNA Methylation Through Bisulfite Sequencing Data 153. Correction to: Development and Optimization of Clinical Informatics Infrastructure to Support Bioinformatics at an Oncology Center 154. Comparison of bioinformatics pipelines for eDNA metabarcoding data analysis of fish populations in Czech reservoirs 155. Bioinformatics analysis combined with experiments predicts CENPK as a potential prognostic factor for lung adenocarcinoma 156. Bioinformatics Analysis of the Lycopene ß-Cyclase Gene in Jujube (Ziziphus jujube Mill) 157. Gene expression collective data analysis for studying the effects of high-LET ionizing radiation: A bioinformatics approach 158. CoV-AbDab: the coronavirus antibody database 159. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides 160. Sanhuang Jiangtang tablet protects type 2 diabetes osteoporosis via AKT-GSK3ß-NFATc1 signaling pathway by integrating bioinformatics analysis and experimental … 161. … Tuning onto Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-Dimensional Bioinformatics … 162. pyGenomeTracks: reproducible plots for multivariate genomic datasets 163. IS900 RFLP Analysis of Mycobacterium avium subsp. Paratuberculosis of Iranian Isolates and Analyze Using Bioinformatics Tools 164. Screening druggable targets and predicting therapeutic drugs for COVID-19 via integrated bioinformatics analysis 165. Supplementary Material to “Integrated analysis of label-free quantitative proteomics and bioinformatics reveal insights into signaling pathways in male breast … 166. … gallate interaction in SARS-CoV-2 spike-protein central channel with reference to the hydroxychloroquine interaction: bioinformatics and molecular docking study 167. CAFE: a software suite for analysis of paired-sample transposon insertion sequencing data 168. Multidrug resistance protein structure of Trypanosoma evansi isolated from buffaloes in Ngawi District, Indonesia: A bioinformatics analysis, Veterinary World, 14 (1) … 169. A Complete Bibliography of IEEE/ACM Transactions on Computational Biology and Bioinformatics 170. Ribbon: intuitive visualization for complex genomic variation 171. Bioinformatics analysis of gene expression profile and key pathways related to fatty infiltration after rotator cuff injury 172. Significance and Mechanisms Analyses of RB1 Mutation in Bladder Cancer Disease Progression and Drug Selection by Bioinformatics Analysis 173. Towards Investigating the Role of Proprotein Convertase Subtilisin/Kexin Family (PCSK/7/9) in Cancer by Using Bioinformatics Motif Detection Technique 174. CNVfilteR: an R/bioconductor package to identify false positives produced by germline NGS CNV detection tools 175. Potential prediction of phenolic compounds in red ginger (Zingiber officinale var. rubrum) as an AT1R antagonist by bioinformatics approach for antihypertensive oral … 176. [PS][PS] Interval Versions of Statistical Techniques, with Applications to Environmental Analysis, Bioinformatics, and Privacy in Statistical Databases 177. Using Interpretable Deep Learning to Model Cancer Dependencies 178. iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features 179. iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor 180. Outlier detection in Bioinformatics with Mixtures of Gaussian and heavy-tailed distributions 181. A Modelling Framework for Embedding-based Predictions for Compound-Viral Protein Activity 182. SimText: A text mining framework for interactive analysis and visualization of similarities among biomedical entities 183. UniBioDicts: unified access to biological dictionaries 184. GraphDTA: Predicting drug–target binding affinity with graph neural networks 185. COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database 186. Discovering footprints of evolutionary chromatin response to transposons activity: merging biophysics with bioinformatics 187. mzRAPP: a tool for reliability assessment of data pre-processing in non-targeted metabolomics 188. In-silico prediction of in-vitro protein liquid-liquid phase separation experiments outcomes with multi-head neural attention 189. Pentraxin 3 is a diagnostic and prognostic marker for ovarian epithelial cancer patients based on comprehensive bioinformatics and experiments 190. ViralMSA: Massively scalable reference-guided multiple sequence alignment of viral genomes 191. PBSIM2: a simulator for long-read sequencers with a novel generative model of quality scores 192. MELODI Presto: a fast and agile tool to explore semantic triples derived from biomedical literature 193. UglyTrees: a browser-based multispecies coalescent tree visualizer 194. Mutation-Simulator: fine-grained simulation of random mutations in any genome 195. SAIGEgds—an efficient statistical tool for large-scale PheWAS with mixed models 196. Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function 197. Detecting Let-7 isoforms of Salmonids by bioinformatics data 198. PICS2: next-generation fine mapping via probabilistic identification of causal SNPs 199. mixtureS: a novel tool for bacterial strain genome reconstruction from reads 200. Genozip: a universal extensible genomic data compressor 201. A systems-biology model of the tumor necrosis factor (TNF) interactions with TNF receptor 1 and 2 202. Early cancer detection from genome-wide cell-free DNA fragmentation via shuffled frog leaping algorithm and support vector machine 203. MAT2: Manifold alignment of single-cell transcriptomes with cell triplets 204. MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery 205. Narrative Scientific Data Visualization in an Immersive Environment 206. Assessing the fit of the multi-species network coalescent to multi-locus data 207. Predicting candidate genes from phenotypes, functions and anatomical site of expression 208. BamSnap: a lightweight viewer for sequencing reads in BAM files 209. ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data 210. Characterizing protein conformers by cross-linking mass spectrometry and pattern recognition 211. GraphQA: protein model quality assessment using graph convolutional networks 212. CaNDis: a web server for investigation of causal relationships between diseases, drugs and drug targets 213. ProkSeq for complete analysis of RNA-seq data from prokaryotes 214. HATK: HLA analysis toolkit 215. A combined recall and rank framework with online negative sampling for chinese procedure terminology normalization 216. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control 217. DELPHI: accurate deep ensemble model for protein interaction sites prediction 218. SOLQC: Synthetic oligo library quality control tool 219. shinyÉPICo: A graphical pipeline to analyze Illumina DNA methylation arrays 220. dream: Powerful differential expression analysis for repeated measures designs 221. ASimulatoR: splice-aware RNA-Seq data simulation 222. SARS-CoV-2 Through the Lens of Computational Biology: How bioinformatics is playing a key role in the study of the virus and its origins 223. Interactive gene networks with KNIT 224. A database of flavivirus RNA structures with a search algorithm for pseudoknots and triple base interactions 225. IGD: high-performance search for large-scale genomic interval datasets 226. NetSets. js: a JavaScript framework for compositional assessment and comparison of biological networks through Venn-integrated network diagrams 227. Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity 228. GWASinspector: comprehensive quality control of genome-wide association study results 229. FASTRAL: Improving scalability of phylogenomic analysis 230. A network-based deep learning methodology for stratification of tumor mutations 231. Deuteros 2.0: peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry 232. The Russian Drug Reaction Corpus and neural models for drug reactions and effectiveness detection in user reviews 233. eHSCPr discriminating the cell identity involved in endothelial to hematopoietic transition 234. EM-stellar: benchmarking deep learning for electron microscopy image segmentation 235. mzRecal: universal MS1 recalibration in mzML using identified peptides in mzIdentML as internal calibrants 236. MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates 237. PyRice: a Python package for querying Oryza sativa databases 238. Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations 239. The iPPI-DB initiative: A Community-centered database of Protein-Protein Interaction modulators 240. MotifGenie: A Python Application for Searching Transcription Factor Binding Sequences Using ChIP-Seq Datasets 241. Network-guided search for genetic heterogeneity between gene pairs 242. Annotating high-impact 5′ untranslated region variants with the UTRannotator 243. Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms 244. Recognition of small molecule-RNA binding sites using RNA sequence and structure 245. Machine Boss: rapid prototyping of bioinformatic automata 246. Automated download and clean-up of family-specific databases for kmer-based virus identification 247. PC2P: Parameter-free network-based prediction of protein complexes 248. CRAFT: Compact genome Representation toward large-scale Alignment-Free daTabase 249. CABEAN: a software for the control of asynchronous Boolean networks 250. Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics 251. Large-scale entity representation learning for biomedical relationship extraction 252. Higher infectivity of the SARS-CoV-2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data 253. EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences 254. ProteomeExpert: a docker image based web-server for exploring, modeling, visualizing, and mining quantitative proteomic data sets 255. PhyloCorrelate: inferring bacterial gene-gene functional associations through large-scale phylogenetic profiling 256. VIDHOP, viral host prediction with Deep Learning 257. CCmed: cross-condition mediation analysis for identifying replicable trans-associations mediated by cis-gene expression 258. Network-adjusted Kendall’s Tau Measure for Feature Screening with Application to High-dimensional Survival Genomic Data 259. Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data 260. xGAP: A python based efficient, modular, extensible and fault tolerant genomic analysis pipeline for variant discovery 261. P69. 02 Identification of Potential Core Gene in Immune Infiltrates of EGFR Mutant Lung Adenocarcinoma using Bioinformatics Analysis 262. CellTracker: An Automated Toolbox for Single-Cell Segmentation and Tracking of Time-lapse Microscopy Images 263. Inferring cancer progression from single-cell sequencing while allowing mutation losses 264. ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques 265. MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes 266. Robust and ultrafast fiducial marker correspondence in electron tomography by a two-stage algorithm considering local constraints 267. KORP-PL: a coarse-grained knowledge-based scoring function for protein–ligand interactions 268. A Method for Subtype Analysis with Somatic Mutations 269. PoSeiDon: a Nextflow pipeline for the detection of evolutionary recombination events and positive selection 270. FuSe: a tool to move RNA-Seq analyses from chromosomal/gene loci to functional grouping of mRNA transcripts 271. A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data 272. Diamond: A Multi-Modal DIA Mass Spectrometry Data Processing Pipeline 273. TreeMap: a structured approach to fine mapping of eQTL variants 274. Co-phosphorylation networks reveal subtype-specific signaling modules in breast cancer 275. TSPTFBS: a docker image for Trans-Species Prediction of Transcription Factor Binding Sites in Plants 276. Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data 277. Network approach to mutagenesis sheds insight on phage resistance in mycobacteria 278. QAlign: Aligning nanopore reads accurately using current-level modeling 279. Machine-OlF-Action: A unified framework for developing and interpreting machine-learning models for chemosensory research 280. Backward Pattern Matching on Elastic Degenerate Strings. 281. Coordinate Systems for Pangenome Graphs based on the Level Function and Minimum Path Covers. 282. CoRC: the COPASI R connector 283. MSL-ST: Development of Mass Spectral Library Search Tool to Enhance Compound Identification. 284. Genome-wide identification and bioinformatics characterization of superoxide dismutases in the desiccation-tolerant cyanobacterium Chroococcidiopsis … 285. Unpaired data empowers association tests 286. DataRemix: a universal data transformation for optimal inference from gene expression datasets 287. … -regulated Differentially Expressed Genes and Related Pathways in Hepatocellular Carcinoma: A Study Based on TCGA Database and Bioinformatics … 288. Discovering a sparse set of pairwise discriminating features in high-dimensional data 289. Gastric cancer-associated microRNA expression signatures: integrated bioinformatics analysis, validation, and clinical significance 290. Cataloguing experimentally confirmed 80.7 kb-long ACKR1 haplotypes from the 1000 Genomes Project database 291. BoardION: real-time monitoring of Oxford Nanopore sequencing instruments 292. Augur: a bioinformatics toolkit for phylogenetic analyses 293. TANTIGEN 2.0: a knowledge base of tumor T cell antigens and epitopes 294. Exploring the potential of Galangin in Cholangiocarcinoma cells using a bioinformatics approach 295. Efflux proteins at the blood-brain barrier: review and bioinformatics analysis (vol 48, pg 506, 2018) 296. GLEANER: a web server for GermLine cycle Expression ANalysis and Epigenetic Roadmap visualization 297. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics 298. Advances in the prediction of mouse liver microsomal studies: From machine learning to deep learning 299. Advantages of using graph databases to explore chromatin conformation capture experiments 300. P* R* O* P: a web application to perform phylogenetic analysis considering the effect of gaps 301. Introduction to the JBCB Special Issue on Selected Papers from BICOB-2020 302. ViR: a tool to solve intrasample variability in the prediction of viral integration sites using whole genome sequencing data 303. Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers 304. Genome-resolved metagenomics using environmental and clinical samples. 305. Set-theory based benchmarking of three different variant callers for targeted sequencing 306. Targeting a cytokine checkpoint enhances the fitness of armored cord blood CAR-NK cells 307. Construction and Analysis of mRNA and lncRNA Regulatory Networks Reveal the Key Genes Associated with Prostate Cancer Related Fatigue During Localized … 308. Current RNA-seq methodology reporting limits reproducibility 309. Search for SINE repeats in the rice genome using correlation-based position weight matrices 310. AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders 311. Mining Biomedical Texts for Pediatric Information. 312. Identification of Key mRNAs, miRNAs, and mRNA-miRNA Network Involved in Papillary Thyroid Carcinoma 313. RaacLogo: a new sequence logo generator by using reduced amino acid clusters 314. NGlyAlign: an automated library building tool to align highly divergent HIV envelope sequences 315. CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network 316. Measurements of venous oxygen saturation in the superior sagittal sinus using conventional 3D multiple gradient-echo MRI: Effects of flow velocity and acceleration 317. SARS-CoV-2 hot-spot mutations are significantly enriched within inverted repeats and CpG island loci 318. Six genes involved in prognosis of hepatocellular carcinoma identified by Cox hazard regression 319. Deep Learning-Based Experimentation for Predicting Secondary Structure of Amino Acid Sequence 320. A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network 321. MMFGRN: a multi-source multi-model fusion method for gene regulatory network reconstruction 322. Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment 323. BugSeq: a highly accurate cloud platform for long-read metagenomic analyses 324. The complete genome sequence of Hafnia alvei A23BA; a potential antibiotic-producing rhizobacterium 325. Web tools to fight pandemics: the COVID-19 experience 326. Single-cell transcriptomic profiling of satellite glial cells in stellate ganglia reveals developmental and functional axial dynamics 327. Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research 328. Toll-like Receptor 4 Gene Polymorphisms in Chinese Population After Allogeneic Hematopoietic Stem Cell Transplantation 329. ReCGBM: a gradient boosting-based method for predicting human dicer cleavage sites 330. NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction 331. Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon … 332. Interaction of Nucleic Acids: Hidden Order of Interaction 333. Identification of Glioma Specific Genes as Diagnostic and Prognostic Markers for Glioma 334. Visual4DTracker: a tool to interact with 3D+ t image stacks 335. Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations 336. Temperature and latitude correlate with SARS-CoV-2 epidemiological variables but not with genomic change worldwide 337. Comparing de novo transcriptome assembly tools in di-and autotetraploid non-model plant species 338. SPServer: split-statistical potentials for the analysis of protein structures and protein–protein interactions 339. Identifying the sequence specificities of circRNA-binding proteins based on a capsule network architecture 340. Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients 341. MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism 342. DeepDist: real-value inter-residue distance prediction with deep residual convolutional network 343. CHTKC: a robust and efficient k-mer counting algorithm based on a lock-free chaining hash table 344. DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms 345. COVID-19: disease pathways and gene expression changes predict methylprednisolone can improve outcome in severe cases. 346. Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform 347. Brain Interface: Nano-Scaled Device as an Improvement in the Process of Learning 348. Profile hidden Markov model sequence analysis can help remove putative pseudogenes from DNA barcoding and metabarcoding datasets 349. Grafting Methionine on 1F1 Ab Increases the Broad-Activity on HA Structural-Conserved Residues of H1, H2, and H3 Influenza a Viruses 350. Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier 351. Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology 352. A decade of de novo transcriptome assembly: Are we there yet? 353. Feature selection based on fuzzy joint mutual information maximization [J] 354. tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R 355. A survey of gene expression meta-analysis: methods and applications 356. Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach 357. PCirc: random forest-based plant circRNA identification software 358. Alvis: a tool for contig and read ALignment VISualisation and chimera detection 359. Small noncoding RNA discovery and profiling with sRNAtools based on high-throughput sequencing 360. DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration 361. CoronaPep: An Anti-coronavirus Peptide Generation Tool 362. Identification of deregulation mechanisms specific to cancer subtypes 363. Computational resources for identifying and describing proteins driving liquid–liquid phase separation 364. Successful identification of predictive profiles for infection utilising systems-level immune analysis: a pilot study in patients with relapsed and refractory multiple … 365. SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model 366. MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes 367. Propedia: a database for protein–peptide identification based on a hybrid clustering algorithm 368. Epidemiological data analysis of viral quasispecies in the next-generation sequencing era 369. Prediction of tumor purity from gene expression data using machine learning 370. Practical Workflow from High-Throughput Genotyping to Genomic Estimated Breeding Values (GEBVs) 371. Design powerful predictor for mRNA subcellular location prediction in Homo sapiens 372. First Complete Genome of the Thermophilic Polyhydroxyalkanoates Producing Bacterium Schlegelella thermodepolymerans DSM 15344 373. recoup: flexible and versatile signal visualization from next generation sequencing 374. Predicting chemosensitivity using drug perturbed gene dynamics 375. Learning curves for drug response prediction in cancer cell lines 376. Anticancer peptides prediction with deep representation learning features 377. Structured sparsity regularization for analyzing high-dimensional omics data 378. MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction 379. Novel perspectives for SARS-CoV-2 genome browsing 380. DISTEVAL: a web server for evaluating predicted protein distances 381. Fast and Accurate Multiple Sequence Alignment with MSAProbs-MPI 382. Prediction of RNA-binding protein and alternative splicing event associations during epithelial–mesenchymal transition based on inductive matrix completion 383. BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study 384. Murine induced pluripotent stem cell-derived neuroimmune cell culture models emphasize opposite immune-effector functions of interleukin 13-primed microglia and … 385. A novel essential protein identification method based on PPI networks and gene expression data 386. Repeat DNA expands our understanding of autism spectrum disorder 387. Error-corrected estimation of a diagnostic accuracy index of a biomarker against a continuous gold standard 388. Using deep neural networks and biological subwords to detect protein S-sulfenylation sites 389. ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm 390. G-Tric: generating three-way synthetic datasets with triclustering solutions 391. Twelve years of SAMtools and BCFtools 392. mixIndependR: a R package for statistical independence testing of loci in database of multi-locus genotypes 393. PredCID: prediction of driver frameshift indels in human cancer 394. Dualmarker: a flexible toolset for exploratory analysis of combinatorial dual biomarkers for clinical efficacy 395. A novel computational framework for genome-scale alternative transcription units prediction 396. H2V: a database of human genes and proteins that respond to SARS-CoV-2, SARS-CoV, and MERS-CoV infection 397. Isolating SARS-CoV-2 strains from countries in the same meridian: genome evolutionary analysis 398. Federated sharing and processing of genomic datasets for tertiary data analysis 399. SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets 400. Postoperative radiotherapy is associated with improved overall survival for alveolar ridge squamous cell carcinoma with adverse pathologic features 401. Mass spectrometry–based protein identification in proteomics—a review 402. CodAn: predictive models for precise identification of coding regions in eukaryotic transcripts 403. Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning 404. Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine … 405. Graph and Convolution Recurrent Neural Networks for Protein-Compound Interaction Prediction 406. FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network 407. Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities 408. A computational platform to identify origins of replication sites in eukaryotes 409. Toli c N, Jaitly N, Shaw JL, Adkins JN, Smith RD 410. Adverse events associated with potential drugs for COVID-19: a case study from real-world data 411. KFGRNI: A robust method to inference gene regulatory network from time-course gene data based on ensemble Kalman filter. 412. Updates to HCOP: the HGNC comparison of orthology predictions tool 413. Bicuspid aortic valve sparing root replacement 414. Unsupervised and self-supervised deep learning approaches for biomedical text mining 415. HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding 416. Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed 417. Wireless Wi-Fi module Testing Procedure in Gigabyte Passive Optical Network to Optical Network Terminal of Equipment 418. Receiver for m-ary Radio Communication System Between Motile Objects in the Microwave Range 419. Impact of perioperative factors on nadir serum prostate-specific antigen levels after holmium laser enucleation of prostate 420. gutMEGA: a database of the human gut MEtaGenome Atlas 421. Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19 422. Toxicological assessment of newly expressed proteins (NEPs) in genetically modified (GM) plants 423. SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning 424. Determining Cell Death Pathway and Regulation by Enrichment Analysis 425. The European Nucleotide Archive in 2020 426. iCysMod: an integrative database for protein cysteine modifications in eukaryotes 427. A review on viral data sources and search systems for perspective mitigation of COVID-19 428. Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars 429. Current challenges and best-practice protocols for microbiome analysis 430. The Coronavirus Network Explorer: Mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function 431. Deep-belief network for predicting potential miRNA-disease associations 432. DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences 433. A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico … 434. SG-LSTM-FRAME: A computational frame using sequence and geometrical information via LSTM to predict miRNA–gene associations 435. Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development 436. OrthoDB in 2020: evolutionary and functional annotations of orthologs 437. PDB-tools web: A user-friendly interface for the manipulation of PDB files 438. Comparative evaluation of full-length isoform quantification from RNA-Seq 439. SurvivalMeth: a web server to investigate the effect of DNA methylation-related functional elements on prognosis 440. The peripheral and core regions of virus-host network of COVID-19. 441. Accucopy: accurate and fast inference of allele-specific copy number alterations from low-coverage low-purity tumor sequencing data 442. Unveiling COVID-19-associated organ-specific cell types and cell-specific pathway cascade 443. Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm 444. MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm 445. M6A2Target: a comprehensive database for targets of m6A writers, erasers and readers 446. Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer 447. MNDR v3. 0: mammal ncRNA–disease repository with increased coverage and annotation 448. CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer 449. DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites 450. Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infection induces dysregulation of immunity: in silico gene expression analysis 451. A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1) 452. The Distinction of Omics in Amelioration of Food Crops Nutritional Value 453. DeepCNV: a deep learning approach for authenticating copy number variations 454. Key residues influencing binding affinities of 2019-nCoV with ACE2 in different species 455. A survey on computational models for predicting protein–protein interactions 456. ADeditome provides the genomic landscape of A-to-I RNA editing in Alzheimer’s disease 457. Integrated hybrid de novo assembly technologies to obtain high-quality pig genome using short and long reads 458. WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data 459. Current Situation and Prospect of EMDB/EMPIAR-China 460. isomiRs–Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them? 461. Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational … 462. rmvp: A memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study 463. Roles of host small RNAs in the evolution and host tropism of coronaviruses 464. CpG-island-based annotation and analysis of human housekeeping genes 465. Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network 466. A novel privacy-preserving federated genome-wide association study framework and its application in identifying potential risk variants in ankylosing spondylitis 467. QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs 468. Comparative host–pathogen protein–protein interaction analysis of recent coronavirus outbreaks and important host targets identification 469. Adult polyglucosan body disease—an atypical compound heterozygous with a novel GBE1 mutation 470. Differential expression of Triggering Receptor Expressed on Myeloid cells 2 (Trem2) in tissue eosinophils 471. Breast Tumor Microenvironment in Black Women: A Distinct Signature of CD8+ T Cell Exhaustion 472. MiBiOmics: An interactive web application for multi-omics data exploration and integration 473. Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework 474. Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma 475. SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references 476. MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm 477. Homoeologous gene expression and co-expression network analyses and evolutionary inference in allopolyploids 478. Exosomal circRNA as a novel potential therapeutic target for multiple myeloma-related peripheral neuropathy 479. Predicting protein subchloroplast locations: the 10th anniversary 480. Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations 481. GPS-Palm: a deep learning-based graphic presentation system for the prediction of S-palmitoylation sites in proteins 482. DSG2 expression is low in colon cancer and correlates with poor survival 483. Deep sparse transfer learning for remote smart tongue diagnosis [J] 484. A hybrid method for classification of physical action using discrete wavelet transform and artificial neural network 485. cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function 486. Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach 487. Upregulation of peroxisome proliferator-activated receptor-a and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer 488. HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid–protein interface with a case study 489. Mesenchymal stromal cells provide hepatic support after extended hepatectomy by modulating thrombospondin-1/TGF-ß 490. PySmash: Python package and individual executable program for representative substructure generation and application 491. Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS 492. CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data 493. NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion 494. Allogeneic hematopoietic stem cell transplantation in leukocyte adhesion deficiency type I and III 495. Justification of the Choice of Signal Processing Method and Its Implementation in the Digital Part of the Receiver for Radar Stations 496. TRlnc: a comprehensive database for human transcriptional regulatory information of lncRNAs 497. Biomedical data and computational models for drug repositioning: a comprehensive review 498. ?????????????? 499. Comprehensive characterization of alternative splicing in renal cell carcinoma 500. Clinically relevant updates of the HbVar database of human hemoglobin variants and thalassemia mutations 501. Transcriptome analysis of cepharanthine against a SARS-CoV-2-related coronavirus 502. Toward a gold standard for benchmarking gene set enrichment analysis 503. Web-Based Base Editing Toolkits: BE-Designer and BE-Analyzer 504. Multigene editing: current approaches and beyond 505. Guía docente 200630-FBIO-Fundamentos de Bioinformática 506. Identification of Potential Gene and MicroRNA Biomarkers of Acute Kidney Injury 507. Further promotion of “the JSH plan for the future” conscious of new normal after/with COVID-19: message from the new president of the Japanese Society of … 508. Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies 509. The international nucleotide sequence database collaboration 510. Integrating multi-network topology for gene function prediction using deep neural networks 511. PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation 512. Recent advances in user-friendly computational tools to engineer protein function 513. Interpretable detection of novel human viruses from genome sequencing data 514. Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes 515. DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction 516. LncRNA LIFR-AS1 promotes proliferation and invasion of gastric cancer cell via miR-29a-3p/COL1A2 axis 517. Application of deep learning methods in biological networks 518. Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when 519. Genome Resource: Ralstonia solanacearum Phylotype II Sequevar 1 (Race 3 Biovar 2) Strain UW848 From the 2020 US Geranium Introduction 520. Genome-wide expression profiling of long non-coding RNAs and competing endogenous RNA networks in alopecia areata [J] 521. Diagnostic and prognostic value of thymidylate synthase expression in breast cancer 522. The functional determinants in the organization of bacterial genomes 523. Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19 524. From ArrayExpress to BioStudies 525. AntiCP 2.0: an updated model for predicting anticancer peptides 526. Exploration of natural compounds with anti-SARS-CoV-2 activity via inhibition of SARS-CoV-2 Mpro 527. The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring county-level vulnerability using visualization, statistical modeling, and machine … 528. How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles 529. Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein stability and receptor-binding affinity 530. Progress and challenge for computational quantification of tissue immune cells 531. Closing the circle: current state and perspectives of circular RNA databases 532. The Compensation of Radiation-Induced Losses in the Fiber Optic Communication Line in Its Operation Mode 533. A comprehensive integrated drug similarity resource for in-silico drug repositioning and beyond 534. A Comprehensive Analysis Identified Hub Genes and Associated Drugs in Alzheimer’s Disease 535. GENCODE 2021 536. Network analyses in microbiome based on high-throughput multi-omics data 537. Different molecular enumeration influences in deep learning: an example using aqueous solubility 538. Integrated omics analysis reveals the alteration of gut microbe–metabolites in obese adults 539. Archaeal roots of intramembrane aspartyl protease siblings signal peptide peptidase and presenilin 540. RICORD: A Precedent for Open AI in COVID-19 Image Analytics 541. A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets 542. Discovery of G-quadruplex-forming sequences in SARS-CoV-2 543. Algorithm optimization for weighted gene co-expression network analysis: accelerating the calculation of Topology Overlap Matrices with OpenMP and SQLite 544. Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment 545. QSAR-assisted-MMPA to expand chemical transformation space for lead optimization 546. Regulatory Assessment of Off-Target Changes and Spurious DNA Insertions in Gene-Edited Organisms for Agri-Food Use 547. DNSS2: improved ab initio protein secondary structure prediction using advanced deep learning architectures 548. DPCMNE: detecting protein complexes from protein-protein interaction networks via multi-level network embedding 549. PanACoTA: A modular tool for massive microbial comparative genomics 550. Peptides: Molecular and Biotechnological Aspects. Biomolecules 2021, 11, 52

Research Topics Computer Science

Bioinformatics

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College of Agricultural Sciences

Project Examples

In this section.

• Bioinformatics

Here are some examples of Bioinformatic analyses we have expertise in conducting. 

We have experience working with many diverse data and organism types, so even if your topic is not listed in our project examples, we are likely to be able to assist you.

Deliverables for Basic/Standard Analysis

MAX Turnaround time – 2 months depending on application and sample size

1. Whole Genome Sequencing

Prokaryotes.

• RE-SEQUENCING: Raw Data QC and Report, Alignment Statistics and Report, Variation Calling Report (SNP, InDels), Gene Annotation Table with Variations.
• DENOVO: Raw Data QC and Report, Assembly Statistics and Report, Genome Finishing using Closest homolog, rRNA identification and analysis report, Phage Identification and analysis report, Plasmid Identification, and analysis report, RAST Annotation.
• RE-SEQUENCING/TARGETED/EXOME: Raw Data QC and Report, Alignment Report, Variation calling Report (SNP, InDels), Basic Variation Annotation, and Effect Analysis Report.
• DENOVO: Raw Data QC and Report, Assembly Statistics and Report, Gene Prediction and Annotation Report.  Data generation depends on predicted genome size

2. Transcriptome Sequencing

• RE-SEQUENCING: Ribo Depletion (rRNA Depletion) – Raw Data QC and Report, Read Alignment to reference genome and transcript Identification, Comprehensive Transcript Annotation, Functional Classification of Annotated Transcript, Expression Profiling, Quantification & Expression Profiling of transcripts, Differential Analysis among the conditions, Biological Significance Analysis of differentials.
• RE-SEQUENCING: – Raw Data QC and Report, Read Alignment to reference genome and transcript Identification, Quantification & Expression Profiling of transcripts, Differential, Analysis among the conditions, Biological Significance Analysis of differentials (n-1). All pictorial representations of comparisons will be according to n-1
• DENOVO: Raw Data QC and Report, De novo assembly, Assembly Evaluation & Filtering, Sequence homology-based Transcript Annotation using Blast2Go – REFSEQ, Expression Profiling, Differential Analysis among the conditions, Biological Significance Analysis of differentials (n-1). ALL pictorial representation LL of comparisons will be according to n-1.

3. Chip Sequencing

Raw Data QC and Report, Alignment Report, Peak Identification, and Enrichment Report, Peak Annotation Report

4. Metagenome Sequencing

Sample Grouping or individual as per experimental design, Group-wise OTU Clustering and abundance Report, OTU identification and taxonomic annotation Report (Sample Wise – Genius Level) and OTU Fasta file will be provided, Pie chart representation TOP 10 taxonomic classification; phylum to species-level.

5. SmallRNA Sequencing

Sample wise Raw Data QC, Unique tags and abundance Report, Known Small RNA analysis report, Identification and Quantitation of Known miRNAs, Expression Profiling and Differential Expression Analysis of Known miRNAs.

6. Microbiome Sequencing

Pre-processing of reads including Quality Filtering, trimming low-quality reads, De-Replication, Sequence reconstruction and grouping, Gene prediction, Functional Annotation.

Deliverables for Advanced Analysis

MAX TAT – 3 months depending on the project requirement and sample size

• RE-SEQUENCING: Raw Data QC and Report, Alignment Statistics and Report, Variation Calling Report (SNP, InDels), Gene Annotation Table with Variations, Structural Variations (Inversion, Deletion, Insertion, Translocation, Transversion) analysis report, Comparative Genome analysis – Across selected genomes, High SNP and Low SNP Region, Generic and NonGeneic SNPs, SNP Density Analysis, Synonymous and Non-synonymous SNPs, Effect of Frameshift Indels on Gene Prediction, Submitting Data to NCBI -SRA, Support in providing write up on methods for the manuscript purpose (Time Limit: 3-6 month)
• DENOVO: Raw Data QC and Report, Assembly Statistics and Report, Genome Finishing using Closest homolog, rRNA identification and analysis report, Phage Identification and analysis report, Plasmid Identification and analysis report, Phylogeny 16s RNA based, COG Analysis, Interproscan Analysis, AAI and ANI analysis with the selected reference genome, Antibiotic resistance gene analysis with reference to transposable elements, PAN and Core genome analysis, Synteny Analysis, Chromosome Mapping, Plasmid Re-construction from whole-genome, Submitting Data to NCBI- SRA, Support in providing write up on methods for the manuscript purpose (Time Limit: 3-6 month)
• RE-SEQUENCING/TARGETED/EXOME: Raw Data QC and Report, Alignment Report, Variation calling Report (SNP, InDels), Basic Variation Annotation and Effect Analysis Report, All the deliverables from Standard Analysis, Structural Variation Analysis Report, Variation Effect Analysis Report, Pathway and GO analysis of variations, Copy Number Variation Analysis, Data Submission to NCBI, Comparative Exome Analysis, Submitting Data to NCBI- SRA, Support in providing write up on methods for the manuscript purpose.
• DENOVO: Raw Data QC and Report, Assembly Statistics and Report, Gene Prediction and Annotation Report, Prediction of rRNAs, tRNAs, Repeat Analysis, Identification of Transposons, Domain Identification, Analysis of Virulence genes, Analysis of CaZymes, Synteny Analysis, Comparative Exome Analysis, Submitting Data to NCBI- SRA, Support in providing write up on methods for the manuscript purpose.
• RE-SEQUENCING: Ribo Depletion (rRNA Depletion) – Raw Data QC and Report, Read Alignment to reference genome and transcript Identification, Comprehensive Transcript Annotation, Functional Classification of Annotated Transcript, Expression Profiling, Quantification & Expression Profiling of transcripts, Differential Analysis among the conditions, Biological Significance Analysis of differentials, Inter and Intra Gene List Comparisons, Gene and Pathway enrichment analysis, GO and Pathways based Gene Regulatory Network Modelling, Submitting Data to NCBI- SRA, Support in providing write up on methods for the manuscript purpose.
• RE-SEQUENCING:  Raw Data QC and Report, Read Alignment to reference genome and transcript Identification, Expression Profiling, Quantification & Expression Profiling of transcripts, Differential Analysis among the conditions, Biological Significance Analysis of differentials, Inter and Intra Gene List Comparisons, Gene and Pathway enrichment analysis, GO and Pathways based Gene Regulatory Network Modeling, Functional classification of expressed transcripts Submitting,  Data to NCBI-SRA, Support in providing write up on methods for the manuscript purpose. 
• DENOVO: Raw Data QC and Report, De novo assembly, Assembly Evaluation & Filtering, Sequence homology-based Transcript Annotation using Blast2Go – NRDB, Expression Profiling, Differential Analysis among the conditions, Biological Significance Analysis of differentials, Sequence homology-based Transcript  Annotation against the customized database, Inter and Intra Gene List Comparisons, Gene and Pathway enrichment analysis, Functional Classification of Annotated Transcript, GO and Pathways based Gene Regulatory Network Modeling, Data to NCBI-SRA, Support in providing write up on methods for the manuscript purpose.

Raw Data QC and Report, Alignment Report, Peak Identification, and Enrichment Report, Peak Annotation Report, Motif Identification, Statistical analysis of Peak Reproducibility (If replicates are provided), Significant GO and Pathway Analysis, Data to NCBI-SRA, Support in providing write up on methods for the manuscript purpose.

Sample Grouping/Individual (either one) as per experimental design, Group-wise OTU Clustering and abundance Report, OTU identification and taxonomic annotation Report (Sample Wise – Genius Level) and OTU Fasta file will be provided, Pie chart representation TOP 10 taxonomic classification (Phylum to Species-level), Differential Metagenome based on sample conditions, Diversity Analysis (Alpha and Beta), Rarefaction Curves, PCoA Plot (required minimum six samples), Krona Plot at the genus level, Heat-Maps for comparisons, Species-level annotation (If V3 & V4 is covered), Data to NCBI-SRA, Support in providing write up on methods for the manuscript purpose.

Raw Data QC and Report, Known Small RNA analysis report, Identification and Quantitation of Known miRNAs, Expression Profiling and Differential Expression Analysis of Known miRNAs, Novel miRNA Identification (In case of reference genome availability) and analysis report, Characterization of other small RNAs like siRNA, piRNA, snoRNA, miRNA Target Prediction / Identification, Significant GO and Pathway Analysis of targets of differentially expressed miRNAs, DData to NCBI-SRA, Support in providing write up on methods for the manuscript purpose.

Pre-processing of reads including Quality Filtering, Trimming low quality reads, De-Replication, Sequence reconstruction and grouping, Gene and regulatory element prediction, Functional Annotation, Differential Microbiome based on sample parameters, Statistical analysis of Microbiome based on OTUs, Diversity Analysis (Alpha and Beta), Rarefaction Curves, Species-level annotation, Seed Subsystem classification, COG, KEGG Analysis, Gene Ontology and Pathway Analysis (Functional Microbiome Analysis), Data to NCBI-SRA, Support in providing write up on methods for the manuscript purpose.

Aarhus University logo

Bioinformatics Research Centre

Master's thesis in bioinformatics.

In the Master’s program in bioinformatics, you must do a 30 ECTS Master’s thesis. You must start your 30 ECTS thesis no later than February 1 (or September 1 ) a year and a half after commencement of your studies (i.e. February 2021 for students admitted in summer 2019, or September 2021 for students admitted in winter 2020). You must complete your thesis (including the exam) no later than June 30 the same year, if you started on February 1 (or January 31 the following year, if you started on September 1).

You can read the course description for the MSc thesis project at:

kursuskatalog.au.dk/en/course/114372/Thesis-30-ECTS-Bioinformatics

You can read some general information and advice about Master’s thesis work at:

https://studerende.au.dk/en/studies/subject-portals/bioinformatics/masters-thesis/masters-thesis/

You can see abstracts of (some) Master's theses from BiRC at:

https://www.birc.au.dk/~cstorm/birc-msc/birc-msc.html

Thesis contract

Before you start your thesis, you must make a thesis contract. The thesis contract must be completed and approved by January 15  (or August 15 ). You can read about how to submit the contract on the above www page. As part of the thesis contract, you must attach a pdf file containing project description, project goals, activity plan, and supervision plan. This is very much like what you have to describe for a Project in Bioinformatics. At BiRC, you should use the following template for this description.

Problem statement, activity plan, and supervision plan (in docx format)

When formulating the thesis project, you should keep in mind that it should cover 30 ECTS of work, i.e. full-time work for the entire semester and the following exam period. Group projects should of course cover this for every group member.

Choosing a topic

Before you can make a thesis contract, and commence your thesis work, you must (of course) chose a topic and a supervisor. The supervisor must be a tenured researcher associated to BiRC, but you can also have one or more co-supervisors.

When choosing a thesis topic, it is a good idea to think about the classes and projects that you have done during your Master’s studies, and what kind of work do you like? Contact potential supervisors as early as possible to discuss your wishes and ideas. Remember that you are always welcome to come by our offices and discuss. You can also ask potential supervisors for examples of thesis’s that they have supervised in order to get a better idea of how a thesis can look.

Also, we plan an information meeting for students that focus on thesis and project work every Fall. Below are the slides from the last such information meeting.

Slides from MSc info meeting (November 2023)

Ten simple rules for writing a great MSc thesis at BiRC (November 2022)

The slides also contain good advice about how to organize your thesis work. The above www page also contains some advice.

Group projects: It is possible to do the thesis project as a group project. Each group member must fill out individual contracts stating the other groups members. A group hand in a single thesis, but each group member is examined individually. In general, we very much encourage group assignments as it for many students is motivating to work together in a group, and to have group member to discuss and solve the many the details of a thesis project together with.

Projects involving external collaborators: It is possible to do a project that involves external collaboration, e.g. with people from industry, or from other university departments. Such collaborators will be associated to your thesis as co-supervisors. In the thesis contract, it is possible to indicate that the thesis project is done in collaboration with an industrial partner, if an NDA has been signed, and if the final thesis report must be made public available.

The thesis report presents the completed work and can be written in Danish or English. The report must contain an English summary/abstract. The summary/abstract is included in the assessment, and the assessment places emphasis on the academic content, as well as the student’s spelling and writing skills. The extent of the thesis report is agreed with the supervisor, but is typically about 50-60 pages excluding frontpage, table of content and appendices. If the MSc thesis is done as a group project provided, the report must be done in such a way that the group members can be assessed individually. This means that you can either (1) do a joint report in which everyone is equally responsible for all parts of the report, or (2) do a joint report, where it is stated (fx in the table of content) who of you has done the individual parts of the report and is responsible for them. See https://studerende.au.dk/en/studies/subject-portals/bioinformatics/masters-thesis/masters-thesis/ under "Group assignment" for details.

In your thesis contract, you state the hand in date. This can between June 1 and 15 (or January 1 and 15 ), earlier dates are also possible. The exact date is (of course) decided in collaboration with your supervisor. You hand in your thesis via Digital Exam (like you are used to for Projects in Bioinformatics).

The thesis exam is 60 min oral exam. It starts with a 30 min presentation from you about your thesis work followed by a 30 min discussion between you, the examiner (your supervisor), and an external examiner. Your presentation is based upon a question that you get from your supervisor one week before the exam. The exam must be held before June 30 (or January 31 ). In principle, the exam can be held from the day after you hand in your thesis. The exact date is decided upon by your supervisor, and often depends on the availability of external examiners. The final grade reflects an overall assessment of your report, your presentation, and your discussion.

If you have any questions about thesis work, then you are always welcome to ask!

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Open Theses Topics

Current suggestions for B.Sc. / M.Sc. / Ph.D. theses topics. Should you have an idea for a topic yourself, don't hesitate to contact us to discuss it.

The bioinformatics masters curriculum contains two lab rotations (Laborpraktika). Students shall independently work on a e.g. programming or analysis project to experience necessary skills prior to their masters thesis. We always welcome interested students and have plenty of topics; some examples are listed here. Please get in contact with us!

Additional open thesis topics are offered by our partner lab of Prof. Dr. Alexander Goesmann for Bioinformatics & Systems Biology. You will find open topics here .

Computer Science Thesis Topics

This page provides a comprehensive list of computer science thesis topics , carefully curated to support students in identifying and selecting innovative and relevant areas for their academic research. Whether you are at the beginning of your research journey or are seeking a specific area to explore further, this guide aims to serve as an essential resource. With an expansive array of topics spread across various sub-disciplines of computer science, this list is designed to meet a diverse range of interests and academic needs. From the complexities of artificial intelligence to the intricate designs of web development, each category is equipped with 40 specific topics, offering a breadth of possibilities to inspire your next big thesis project. Explore our guide to find not only a topic that resonates with your academic ambitions but also one that has the potential to contribute significantly to the field of computer science.

1000 Computer Science Thesis Topics and Ideas

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code, browse computer science thesis topics:, artificial intelligence thesis topics, augmented reality thesis topics, big data analytics thesis topics, bioinformatics thesis topics, blockchain technology thesis topics, cloud computing thesis topics, computer engineering thesis topics, computer vision thesis topics, cybersecurity thesis topics, data science thesis topics, digital transformation thesis topics, distributed systems and networks thesis topics, geographic information systems (gis) thesis topics, human-computer interaction (hci) thesis topics, image processing thesis topics, information system thesis topics, information technology thesis topics.

• Internet Of Things (IoT) Thesis Topics

Machine Learning Thesis Topics

Neural networks thesis topics, programming thesis topics, quantum computing thesis topics, robotics thesis topics, software engineering thesis topics, web development thesis topics.

• Ethical Implications of AI in Decision-Making Processes
• The Role of AI in Personalized Medicine: Opportunities and Challenges
• Advances in AI-Driven Predictive Analytics in Retail
• AI in Autonomous Vehicles: Safety, Regulation, and Technology Integration
• Natural Language Processing: Improving Human-Machine Interaction
• The Future of AI in Cybersecurity: Threats and Defenses
• Machine Learning Algorithms for Real-Time Data Processing
• AI and the Internet of Things: Transforming Smart Home Technology
• The Impact of Deep Learning on Image Recognition Technologies
• Reinforcement Learning: Applications in Robotics and Automation
• AI in Finance: Algorithmic Trading and Risk Assessment
• Bias and Fairness in AI: Addressing Socio-Technical Challenges
• The Evolution of AI in Education: Customized Learning Experiences
• AI for Environmental Conservation: Tracking and Predictive Analysis
• The Role of Artificial Neural Networks in Weather Forecasting
• AI in Agriculture: Predictive Analytics for Crop and Soil Management
• Emotional Recognition AI: Implications for Mental Health Assessments
• AI in Space Exploration: Autonomous Rovers and Mission Planning
• Enhancing User Experience with AI in Video Games
• AI-Powered Virtual Assistants: Trends, Effectiveness, and User Trust
• The Integration of AI in Traditional Industries: Case Studies
• Generative AI Models in Art and Creativity
• AI in LegalTech: Document Analysis and Litigation Prediction
• Healthcare Diagnostics: AI Applications in Radiology and Pathology
• AI and Blockchain: Enhancing Security in Decentralized Systems
• Ethics of AI in Surveillance: Privacy vs. Security
• AI in E-commerce: Personalization Engines and Customer Behavior Analysis
• The Future of AI in Telecommunications: Network Optimization and Service Delivery
• AI in Manufacturing: Predictive Maintenance and Quality Control
• Challenges of AI in Elderly Care: Ethical Considerations and Technological Solutions
• The Role of AI in Public Safety and Emergency Response
• AI for Content Creation: Impact on Media and Journalism
• AI-Driven Algorithms for Efficient Energy Management
• The Role of AI in Cultural Heritage Preservation
• AI and the Future of Public Transport: Optimization and Management
• Enhancing Sports Performance with AI-Based Analytics
• AI in Human Resources: Automating Recruitment and Employee Management
• Real-Time Translation AI: Breaking Language Barriers
• AI in Mental Health: Tools for Monitoring and Therapy Assistance
• The Future of AI Governance: Regulation and Standardization
• AR in Medical Training and Surgery Simulation
• The Impact of Augmented Reality in Retail: Enhancing Consumer Experience
• Augmented Reality for Enhanced Navigation Systems
• AR Applications in Maintenance and Repair in Industrial Settings
• The Role of AR in Enhancing Online Education
• Augmented Reality in Cultural Heritage: Interactive Visitor Experiences
• Developing AR Tools for Improved Sports Coaching and Training
• Privacy and Security Challenges in Augmented Reality Applications
• The Future of AR in Advertising: Engagement and Measurement
• User Interface Design for AR: Principles and Best Practices
• AR in Automotive Industry: Enhancing Driving Experience and Safety
• Augmented Reality for Emergency Response Training
• AR and IoT: Converging Technologies for Smart Environments
• Enhancing Physical Rehabilitation with AR Applications
• The Role of AR in Enhancing Public Safety and Awareness
• Augmented Reality in Fashion: Virtual Fitting and Personalized Shopping
• AR for Environmental Education: Interactive and Immersive Learning
• The Use of AR in Building and Architecture Planning
• AR in the Entertainment Industry: Games and Live Events
• Implementing AR in Museums and Art Galleries for Interactive Learning
• Augmented Reality for Real Estate: Virtual Tours and Property Visualization
• AR in Consumer Electronics: Integration in Smart Devices
• The Development of AR Applications for Children’s Education
• AR for Enhancing User Engagement in Social Media Platforms
• The Application of AR in Field Service Management
• Augmented Reality for Disaster Management and Risk Assessment
• Challenges of Content Creation for Augmented Reality
• Future Trends in AR Hardware: Wearables and Beyond
• Legal and Ethical Considerations of Augmented Reality Technology
• AR in Space Exploration: Tools for Simulation and Training
• Interactive Shopping Experiences with AR: The Future of Retail
• AR in Wildlife Conservation: Educational Tools and Awareness
• The Impact of AR on the Publishing Industry: Interactive Books and Magazines
• Augmented Reality and Its Role in Automotive Manufacturing
• AR for Job Training: Bridging the Skill Gap in Various Industries
• The Role of AR in Therapy: New Frontiers in Mental Health Treatment
• The Future of Augmented Reality in Sports Broadcasting
• AR as a Tool for Enhancing Public Art Installations
• Augmented Reality in the Tourism Industry: Personalized Travel Experiences
• The Use of AR in Security Training: Realistic and Safe Simulations
• The Role of Big Data in Improving Healthcare Outcomes
• Big Data and Its Impact on Consumer Behavior Analysis
• Privacy Concerns in Big Data: Ethical and Legal Implications
• The Application of Big Data in Predictive Maintenance for Manufacturing
• Real-Time Big Data Processing: Tools and Techniques
• Big Data in Financial Services: Fraud Detection and Risk Management
• The Evolution of Big Data Technologies: From Hadoop to Spark
• Big Data Visualization: Techniques for Effective Communication of Insights
• The Integration of Big Data and Artificial Intelligence
• Big Data in Smart Cities: Applications in Traffic Management and Energy Use
• Enhancing Supply Chain Efficiency with Big Data Analytics
• Big Data in Sports Analytics: Improving Team Performance and Fan Engagement
• The Role of Big Data in Environmental Monitoring and Sustainability
• Big Data and Social Media: Analyzing Sentiments and Trends
• Scalability Challenges in Big Data Systems
• The Future of Big Data in Retail: Personalization and Customer Experience
• Big Data in Education: Customized Learning Paths and Student Performance Analysis
• Privacy-Preserving Techniques in Big Data
• Big Data in Public Health: Epidemiology and Disease Surveillance
• The Impact of Big Data on Insurance: Tailored Policies and Pricing
• Edge Computing in Big Data: Processing at the Source
• Big Data and the Internet of Things: Generating Insights from IoT Data
• Cloud-Based Big Data Analytics: Opportunities and Challenges
• Big Data Governance: Policies, Standards, and Management
• The Role of Big Data in Crisis Management and Response
• Machine Learning with Big Data: Building Predictive Models
• Big Data in Agriculture: Precision Farming and Yield Optimization
• The Ethics of Big Data in Research: Consent and Anonymity
• Cross-Domain Big Data Integration: Challenges and Solutions
• Big Data and Cybersecurity: Threat Detection and Prevention Strategies
• Real-Time Streaming Analytics in Big Data
• Big Data in the Media Industry: Content Optimization and Viewer Insights
• The Impact of GDPR on Big Data Practices
• Quantum Computing and Big Data: Future Prospects
• Big Data in E-Commerce: Optimizing Logistics and Inventory Management
• Big Data Talent: Education and Skill Development for Data Scientists
• The Role of Big Data in Political Campaigns and Voting Behavior Analysis
• Big Data and Mental Health: Analyzing Patterns for Better Interventions
• Big Data in Genomics and Personalized Medicine
• The Future of Big Data in Autonomous Driving Technologies
• The Role of Bioinformatics in Personalized Medicine
• Next-Generation Sequencing Data Analysis: Challenges and Opportunities
• Bioinformatics and the Study of Genetic Diseases
• Computational Models for Understanding Protein Structure and Function
• Bioinformatics in Drug Discovery and Development
• The Impact of Big Data on Bioinformatics: Data Management and Analysis
• Machine Learning Applications in Bioinformatics
• Bioinformatics Approaches for Cancer Genomics
• The Development of Bioinformatics Tools for Metagenomics Analysis
• Ethical Considerations in Bioinformatics: Data Sharing and Privacy
• The Role of Bioinformatics in Agricultural Biotechnology
• Bioinformatics and Viral Evolution: Tracking Pathogens and Outbreaks
• The Integration of Bioinformatics and Systems Biology
• Bioinformatics in Neuroscience: Mapping the Brain
• The Future of Bioinformatics in Non-Invasive Prenatal Testing
• Bioinformatics and the Human Microbiome: Health Implications
• The Application of Artificial Intelligence in Bioinformatics
• Structural Bioinformatics: Computational Techniques for Molecular Modeling
• Comparative Genomics: Insights into Evolution and Function
• Bioinformatics in Immunology: Vaccine Design and Immune Response Analysis
• High-Performance Computing in Bioinformatics
• The Challenge of Proteomics in Bioinformatics
• RNA-Seq Data Analysis and Interpretation
• Cloud Computing Solutions for Bioinformatics Data
• Computational Epigenetics: DNA Methylation and Histone Modification Analysis
• Bioinformatics in Ecology: Biodiversity and Conservation Genetics
• The Role of Bioinformatics in Forensic Analysis
• Mobile Apps and Tools for Bioinformatics Research
• Bioinformatics and Public Health: Epidemiological Studies
• The Use of Bioinformatics in Clinical Diagnostics
• Genetic Algorithms in Bioinformatics
• Bioinformatics for Aging Research: Understanding the Mechanisms of Aging
• Data Visualization Techniques in Bioinformatics
• Bioinformatics and the Development of Therapeutic Antibodies
• The Role of Bioinformatics in Stem Cell Research
• Bioinformatics and Cardiovascular Diseases: Genomic Insights
• The Impact of Machine Learning on Functional Genomics in Bioinformatics
• Bioinformatics in Dental Research: Genetic Links to Oral Diseases
• The Future of CRISPR Technology and Bioinformatics
• Bioinformatics and Nutrition: Genomic Insights into Diet and Health
• Blockchain for Enhancing Cybersecurity in Various Industries
• The Impact of Blockchain on Supply Chain Transparency
• Blockchain in Healthcare: Patient Data Management and Security
• The Application of Blockchain in Voting Systems
• Blockchain and Smart Contracts: Legal Implications and Applications
• Cryptocurrencies: Market Trends and the Future of Digital Finance
• Blockchain in Real Estate: Improving Property and Land Registration
• The Role of Blockchain in Managing Digital Identities
• Blockchain for Intellectual Property Management
• Energy Sector Innovations: Blockchain for Renewable Energy Distribution
• Blockchain and the Future of Public Sector Operations
• The Impact of Blockchain on Cross-Border Payments
• Blockchain for Non-Fungible Tokens (NFTs): Applications in Art and Media
• Privacy Issues in Blockchain Applications
• Blockchain in the Automotive Industry: Supply Chain and Beyond
• Decentralized Finance (DeFi): Opportunities and Challenges
• The Role of Blockchain in Combating Counterfeiting and Fraud
• Blockchain for Sustainable Environmental Practices
• The Integration of Artificial Intelligence with Blockchain
• Blockchain Education: Curriculum Development and Training Needs
• Blockchain in the Music Industry: Rights Management and Revenue Distribution
• The Challenges of Blockchain Scalability and Performance Optimization
• The Future of Blockchain in the Telecommunications Industry
• Blockchain and Consumer Data Privacy: A New Paradigm
• Blockchain for Disaster Recovery and Business Continuity
• Blockchain in the Charity and Non-Profit Sectors
• Quantum Resistance in Blockchain: Preparing for the Quantum Era
• Blockchain and Its Impact on Traditional Banking and Financial Institutions
• Legal and Regulatory Challenges Facing Blockchain Technology
• Blockchain for Improved Logistics and Freight Management
• The Role of Blockchain in the Evolution of the Internet of Things (IoT)
• Blockchain and the Future of Gaming: Transparency and Fair Play
• Blockchain for Academic Credentials Verification
• The Application of Blockchain in the Insurance Industry
• Blockchain and the Future of Content Creation and Distribution
• Blockchain for Enhancing Data Integrity in Scientific Research
• The Impact of Blockchain on Human Resources: Employee Verification and Salary Payments
• Blockchain and the Future of Retail: Customer Loyalty Programs and Inventory Management
• Blockchain and Industrial Automation: Trust and Efficiency
• Blockchain for Digital Marketing: Transparency and Consumer Engagement
• Multi-Cloud Strategies: Optimization and Security Challenges
• Advances in Cloud Computing Architectures for Scalable Applications
• Edge Computing: Extending the Reach of Cloud Services
• Cloud Security: Novel Approaches to Data Encryption and Threat Mitigation
• The Impact of Serverless Computing on Software Development Lifecycle
• Cloud Computing and Sustainability: Energy-Efficient Data Centers
• Cloud Service Models: Comparative Analysis of IaaS, PaaS, and SaaS
• Cloud Migration Strategies: Best Practices and Common Pitfalls
• The Role of Cloud Computing in Big Data Analytics
• Implementing AI and Machine Learning Workloads on Cloud Platforms
• Hybrid Cloud Environments: Management Tools and Techniques
• Cloud Computing in Healthcare: Compliance, Security, and Use Cases
• Cost-Effective Cloud Solutions for Small and Medium Enterprises (SMEs)
• The Evolution of Cloud Storage Solutions: Trends and Technologies
• Cloud-Based Disaster Recovery Solutions: Design and Reliability
• Blockchain in Cloud Services: Enhancing Transparency and Trust
• Cloud Networking: Managing Connectivity and Traffic in Cloud Environments
• Cloud Governance: Managing Compliance and Operational Risks
• The Future of Cloud Computing: Quantum Computing Integration
• Performance Benchmarking of Cloud Services Across Different Providers
• Privacy Preservation in Cloud Environments
• Cloud Computing in Education: Virtual Classrooms and Learning Management Systems
• Automation in Cloud Deployments: Tools and Strategies
• Cloud Auditing and Monitoring Techniques
• Mobile Cloud Computing: Challenges and Future Trends
• The Role of Cloud Computing in Digital Media Production and Distribution
• Security Risks in Multi-Tenancy Cloud Environments
• Cloud Computing for Scientific Research: Enabling Complex Simulations
• The Impact of 5G on Cloud Computing Services
• Federated Clouds: Building Collaborative Cloud Environments
• Managing Software Dependencies in Cloud Applications
• The Economics of Cloud Computing: Cost Models and Pricing Strategies
• Cloud Computing in Government: Security Protocols and Citizen Services
• Cloud Access Security Brokers (CASBs): Security Enforcement Points
• DevOps in the Cloud: Strategies for Continuous Integration and Deployment
• Predictive Analytics in Cloud Computing
• The Role of Cloud Computing in IoT Deployment
• Implementing Robust Cybersecurity Measures in Cloud Architecture
• Cloud Computing in the Financial Sector: Handling Sensitive Data
• Future Trends in Cloud Computing: The Role of AI in Cloud Optimization
• Advances in Microprocessor Design and Architecture
• FPGA-Based Design: Innovations and Applications
• The Role of Embedded Systems in Consumer Electronics
• Quantum Computing: Hardware Development and Challenges
• High-Performance Computing (HPC) and Parallel Processing
• Design and Analysis of Computer Networks
• Cyber-Physical Systems: Design, Analysis, and Security
• The Impact of Nanotechnology on Computer Hardware
• Wireless Sensor Networks: Design and Optimization
• Cryptographic Hardware: Implementations and Security Evaluations
• Machine Learning Techniques for Hardware Optimization
• Hardware for Artificial Intelligence: GPUs vs. TPUs
• Energy-Efficient Hardware Designs for Sustainable Computing
• Security Aspects of Mobile and Ubiquitous Computing
• Advanced Algorithms for Computer-Aided Design (CAD) of VLSI
• Signal Processing in Communication Systems
• The Development of Wearable Computing Devices
• Computer Hardware Testing: Techniques and Tools
• The Role of Hardware in Network Security
• The Evolution of Interface Designs in Consumer Electronics
• Biometric Systems: Hardware and Software Integration
• The Integration of IoT Devices in Smart Environments
• Electronic Design Automation (EDA) Tools and Methodologies
• Robotics: Hardware Design and Control Systems
• Hardware Accelerators for Deep Learning Applications
• Developments in Non-Volatile Memory Technologies
• The Future of Computer Hardware in the Era of Quantum Computing
• Hardware Solutions for Data Storage and Retrieval
• Power Management Techniques in Embedded Systems
• Challenges in Designing Multi-Core Processors
• System on Chip (SoC) Design Trends and Challenges
• The Role of Computer Engineering in Aerospace Technology
• Real-Time Systems: Design and Implementation Challenges
• Hardware Support for Virtualization Technology
• Advances in Computer Graphics Hardware
• The Impact of 5G Technology on Mobile Computing Hardware
• Environmental Impact Assessment of Computer Hardware Production
• Security Vulnerabilities in Modern Microprocessors
• Computer Hardware Innovations in the Automotive Industry
• The Role of Computer Engineering in Medical Device Technology
• Deep Learning Approaches to Object Recognition
• Real-Time Image Processing for Autonomous Vehicles
• Computer Vision in Robotic Surgery: Techniques and Challenges
• Facial Recognition Technology: Innovations and Privacy Concerns
• Machine Vision in Industrial Automation and Quality Control
• 3D Reconstruction Techniques in Computer Vision
• Enhancing Sports Analytics with Computer Vision
• Augmented Reality: Integrating Computer Vision for Immersive Experiences
• Computer Vision for Environmental Monitoring
• Thermal Imaging and Its Applications in Computer Vision
• Computer Vision in Retail: Customer Behavior and Store Layout Optimization
• Motion Detection and Tracking in Security Systems
• The Role of Computer Vision in Content Moderation on Social Media
• Gesture Recognition: Methods and Applications
• Computer Vision in Agriculture: Pest Detection and Crop Analysis
• Advances in Medical Imaging: Machine Learning and Computer Vision
• Scene Understanding and Contextual Inference in Images
• The Development of Vision-Based Autonomous Drones
• Optical Character Recognition (OCR): Latest Techniques and Applications
• The Impact of Computer Vision on Virtual Reality Experiences
• Biometrics: Enhancing Security Systems with Computer Vision
• Computer Vision for Wildlife Conservation: Species Recognition and Behavior Analysis
• Underwater Image Processing: Challenges and Techniques
• Video Surveillance: The Evolution of Algorithmic Approaches
• Advanced Driver-Assistance Systems (ADAS): Leveraging Computer Vision
• Computational Photography: Enhancing Image Capture Techniques
• The Integration of AI in Computer Vision: Ethical and Technical Considerations
• Computer Vision in the Gaming Industry: From Design to Interaction
• The Future of Computer Vision in Smart Cities
• Pattern Recognition in Historical Document Analysis
• The Role of Computer Vision in the Manufacturing of Customized Products
• Enhancing Accessibility with Computer Vision: Tools for the Visually Impaired
• The Use of Computer Vision in Behavioral Research
• Predictive Analytics with Computer Vision in Sports
• Image Synthesis with Generative Adversarial Networks (GANs)
• The Use of Computer Vision in Remote Sensing
• Real-Time Video Analytics for Public Safety
• The Role of Computer Vision in Telemedicine
• Computer Vision and the Internet of Things (IoT): A Synergistic Approach
• Future Trends in Computer Vision: Quantum Computing and Beyond
• Advances in Cryptography: Post-Quantum Cryptosystems
• Artificial Intelligence in Cybersecurity: Threat Detection and Response
• Blockchain for Enhanced Security in Distributed Networks
• The Impact of IoT on Cybersecurity: Vulnerabilities and Solutions
• Cybersecurity in Cloud Computing: Best Practices and Tools
• Ethical Hacking: Techniques and Ethical Implications
• The Role of Human Factors in Cybersecurity Breaches
• Privacy-preserving Technologies in an Age of Surveillance
• The Evolution of Ransomware Attacks and Defense Strategies
• Secure Software Development: Integrating Security in DevOps (DevSecOps)
• Cybersecurity in Critical Infrastructure: Challenges and Innovations
• The Future of Biometric Security Systems
• Cyber Warfare: State-sponsored Attacks and Defense Mechanisms
• The Role of Cybersecurity in Protecting Digital Identities
• Social Engineering Attacks: Prevention and Countermeasures
• Mobile Security: Protecting Against Malware and Exploits
• Wireless Network Security: Protocols and Practices
• Data Breaches: Analysis, Consequences, and Mitigation
• The Ethics of Cybersecurity: Balancing Privacy and Security
• Regulatory Compliance and Cybersecurity: GDPR and Beyond
• The Impact of 5G Technology on Cybersecurity
• The Role of Machine Learning in Cyber Threat Intelligence
• Cybersecurity in Automotive Systems: Challenges in a Connected Environment
• The Use of Virtual Reality for Cybersecurity Training and Simulation
• Advanced Persistent Threats (APT): Detection and Response
• Cybersecurity for Smart Cities: Challenges and Solutions
• Deep Learning Applications in Malware Detection
• The Role of Cybersecurity in Healthcare: Protecting Patient Data
• Supply Chain Cybersecurity: Identifying Risks and Solutions
• Endpoint Security: Trends, Challenges, and Future Directions
• Forensic Techniques in Cybersecurity: Tracking and Analyzing Cyber Crimes
• The Influence of International Law on Cyber Operations
• Protecting Financial Institutions from Cyber Frauds and Attacks
• Quantum Computing and Its Implications for Cybersecurity
• Cybersecurity and Remote Work: Emerging Threats and Strategies
• IoT Security in Industrial Applications
• Cyber Insurance: Risk Assessment and Management
• Security Challenges in Edge Computing Environments
• Anomaly Detection in Network Security Using AI Techniques
• Securing the Software Supply Chain in Application Development
• Big Data Analytics: Techniques and Applications in Real-time
• Machine Learning Algorithms for Predictive Analytics
• Data Science in Healthcare: Improving Patient Outcomes with Predictive Models
• The Role of Data Science in Financial Market Predictions
• Natural Language Processing: Emerging Trends and Applications
• Data Visualization Tools and Techniques for Enhanced Business Intelligence
• Ethics in Data Science: Privacy, Fairness, and Transparency
• The Use of Data Science in Environmental Science for Sustainability Studies
• The Impact of Data Science on Social Media Marketing Strategies
• Data Mining Techniques for Detecting Patterns in Large Datasets
• AI and Data Science: Synergies and Future Prospects
• Reinforcement Learning: Applications and Challenges in Data Science
• The Role of Data Science in E-commerce Personalization
• Predictive Maintenance in Manufacturing Through Data Science
• The Evolution of Recommendation Systems in Streaming Services
• Real-time Data Processing with Stream Analytics
• Deep Learning for Image and Video Analysis
• Data Governance in Big Data Analytics
• Text Analytics and Sentiment Analysis for Customer Feedback
• Fraud Detection in Banking and Insurance Using Data Science
• The Integration of IoT Data in Data Science Models
• The Future of Data Science in Quantum Computing
• Data Science for Public Health: Epidemic Outbreak Prediction
• Sports Analytics: Performance Improvement and Injury Prevention
• Data Science in Retail: Inventory Management and Customer Journey Analysis
• Data Science in Smart Cities: Traffic and Urban Planning
• The Use of Blockchain in Data Security and Integrity
• Geospatial Analysis for Environmental Monitoring
• Time Series Analysis in Economic Forecasting
• Data Science in Education: Analyzing Trends and Student Performance
• Predictive Policing: Data Science in Law Enforcement
• Data Science in Agriculture: Yield Prediction and Soil Health
• Computational Social Science: Analyzing Societal Trends
• Data Science in Energy Sector: Consumption and Optimization
• Personalization Technologies in Healthcare Through Data Science
• The Role of Data Science in Content Creation and Media
• Anomaly Detection in Network Security Using Data Science Techniques
• The Future of Autonomous Vehicles: Data Science-Driven Innovations
• Multimodal Data Fusion Techniques in Data Science
• Scalability Challenges in Data Science Projects
• The Role of Digital Transformation in Business Model Innovation
• The Impact of Digital Technologies on Customer Experience
• Digital Transformation in the Banking Sector: Trends and Challenges
• The Use of AI and Robotics in Digital Transformation of Manufacturing
• Digital Transformation in Healthcare: Telemedicine and Beyond
• The Influence of Big Data on Decision-Making Processes in Corporations
• Blockchain as a Driver for Transparency in Digital Transformation
• The Role of IoT in Enhancing Operational Efficiency in Industries
• Digital Marketing Strategies: SEO, Content, and Social Media
• The Integration of Cyber-Physical Systems in Industrial Automation
• Digital Transformation in Education: Virtual Learning Environments
• Smart Cities: The Role of Digital Technologies in Urban Planning
• Digital Transformation in the Retail Sector: E-commerce Evolution
• The Future of Work: Impact of Digital Transformation on Workplaces
• Cybersecurity Challenges in a Digitally Transformed World
• Mobile Technologies and Their Impact on Digital Transformation
• The Role of Digital Twin Technology in Industry 4.0
• Digital Transformation in the Public Sector: E-Government Services
• Data Privacy and Security in the Age of Digital Transformation
• Digital Transformation in the Energy Sector: Smart Grids and Renewable Energy
• The Use of Augmented Reality in Training and Development
• The Role of Virtual Reality in Real Estate and Architecture
• Digital Transformation and Sustainability: Reducing Environmental Footprint
• The Role of Digital Transformation in Supply Chain Optimization
• Digital Transformation in Agriculture: IoT and Smart Farming
• The Impact of 5G on Digital Transformation Initiatives
• The Influence of Digital Transformation on Media and Entertainment
• Digital Transformation in Insurance: Telematics and Risk Assessment
• The Role of AI in Enhancing Customer Service Operations
• The Future of Digital Transformation: Trends and Predictions
• Digital Transformation and Corporate Governance
• The Role of Leadership in Driving Digital Transformation
• Digital Transformation in Non-Profit Organizations: Challenges and Benefits
• The Economic Implications of Digital Transformation
• The Cultural Impact of Digital Transformation on Organizations
• Digital Transformation in Transportation: Logistics and Fleet Management
• User Experience (UX) Design in Digital Transformation
• The Role of Digital Transformation in Crisis Management
• Digital Transformation and Human Resource Management
• Implementing Change Management in Digital Transformation Projects
• Scalability Challenges in Distributed Systems: Solutions and Strategies
• Blockchain Technology: Enhancing Security and Transparency in Distributed Networks
• The Role of Edge Computing in Distributed Systems
• Designing Fault-Tolerant Systems in Distributed Networks
• The Impact of 5G Technology on Distributed Network Architectures
• Machine Learning Algorithms for Network Traffic Analysis
• Load Balancing Techniques in Distributed Computing
• The Use of Distributed Ledger Technology Beyond Cryptocurrencies
• Network Function Virtualization (NFV) and Its Impact on Service Providers
• The Evolution of Software-Defined Networking (SDN) in Enterprise Environments
• Implementing Robust Cybersecurity Measures in Distributed Systems
• Quantum Computing: Implications for Network Security in Distributed Systems
• Peer-to-Peer Network Protocols and Their Applications
• The Internet of Things (IoT): Network Challenges and Communication Protocols
• Real-Time Data Processing in Distributed Sensor Networks
• The Role of Artificial Intelligence in Optimizing Network Operations
• Privacy and Data Protection Strategies in Distributed Systems
• The Future of Distributed Computing in Cloud Environments
• Energy Efficiency in Distributed Network Systems
• Wireless Mesh Networks: Design, Challenges, and Applications
• Multi-Access Edge Computing (MEC): Use Cases and Deployment Challenges
• Consensus Algorithms in Distributed Systems: From Blockchain to New Applications
• The Use of Containers and Microservices in Building Scalable Applications
• Network Slicing for 5G: Opportunities and Challenges
• The Role of Distributed Systems in Big Data Analytics
• Managing Data Consistency in Distributed Databases
• The Impact of Distributed Systems on Digital Transformation Strategies
• Augmented Reality over Distributed Networks: Performance and Scalability Issues
• The Application of Distributed Systems in Smart Grid Technology
• Developing Distributed Applications Using Serverless Architectures
• The Challenges of Implementing IPv6 in Distributed Networks
• Distributed Systems for Disaster Recovery: Design and Implementation
• The Use of Virtual Reality in Distributed Network Environments
• Security Protocols for Ad Hoc Networks in Emergency Situations
• The Role of Distributed Networks in Enhancing Mobile Broadband Services
• Next-Generation Protocols for Enhanced Network Reliability and Performance
• The Application of Blockchain in Securing Distributed IoT Networks
• Dynamic Resource Allocation Strategies in Distributed Systems
• The Integration of Distributed Systems with Existing IT Infrastructure
• The Future of Autonomous Systems in Distributed Networking
• The Integration of GIS with Remote Sensing for Environmental Monitoring
• GIS in Urban Planning: Techniques for Sustainable Development
• The Role of GIS in Disaster Management and Response Strategies
• Real-Time GIS Applications in Traffic Management and Route Planning
• The Use of GIS in Water Resource Management
• GIS and Public Health: Tracking Epidemics and Healthcare Access
• Advances in 3D GIS: Technologies and Applications
• GIS in Agricultural Management: Precision Farming Techniques
• The Impact of GIS on Biodiversity Conservation Efforts
• Spatial Data Analysis for Crime Pattern Detection and Prevention
• GIS in Renewable Energy: Site Selection and Resource Management
• The Role of GIS in Historical Research and Archaeology
• GIS and Machine Learning: Integrating Spatial Analysis with Predictive Models
• Cloud Computing and GIS: Enhancing Accessibility and Data Processing
• The Application of GIS in Managing Public Transportation Systems
• GIS in Real Estate: Market Analysis and Property Valuation
• The Use of GIS for Environmental Impact Assessments
• Mobile GIS Applications: Development and Usage Trends
• GIS and Its Role in Smart City Initiatives
• Privacy Issues in the Use of Geographic Information Systems
• GIS in Forest Management: Monitoring and Conservation Strategies
• The Impact of GIS on Tourism: Enhancing Visitor Experiences through Technology
• GIS in the Insurance Industry: Risk Assessment and Policy Design
• The Development of Participatory GIS (PGIS) for Community Engagement
• GIS in Coastal Management: Addressing Erosion and Flood Risks
• Geospatial Analytics in Retail: Optimizing Location and Consumer Insights
• GIS for Wildlife Tracking and Habitat Analysis
• The Use of GIS in Climate Change Studies
• GIS and Social Media: Analyzing Spatial Trends from User Data
• The Future of GIS: Augmented Reality and Virtual Reality Applications
• GIS in Education: Tools for Teaching Geographic Concepts
• The Role of GIS in Land Use Planning and Zoning
• GIS for Emergency Medical Services: Optimizing Response Times
• Open Source GIS Software: Development and Community Contributions
• GIS and the Internet of Things (IoT): Converging Technologies for Advanced Monitoring
• GIS for Mineral Exploration: Techniques and Applications
• The Role of GIS in Municipal Management and Services
• GIS and Drone Technology: A Synergy for Precision Mapping
• Spatial Statistics in GIS: Techniques for Advanced Data Analysis
• Future Trends in GIS: The Integration of AI for Smarter Solutions
• The Evolution of User Interface (UI) Design: From Desktop to Mobile and Beyond
• The Role of HCI in Enhancing Accessibility for Disabled Users
• Virtual Reality (VR) and Augmented Reality (AR) in HCI: New Dimensions of Interaction
• The Impact of HCI on User Experience (UX) in Software Applications
• Cognitive Aspects of HCI: Understanding User Perception and Behavior
• HCI and the Internet of Things (IoT): Designing Interactive Smart Devices
• The Use of Biometrics in HCI: Security and Usability Concerns
• HCI in Educational Technologies: Enhancing Learning through Interaction
• Emotional Recognition and Its Application in HCI
• The Role of HCI in Wearable Technology: Design and Functionality
• Advanced Techniques in Voice User Interfaces (VUIs)
• The Impact of HCI on Social Media Interaction Patterns
• HCI in Healthcare: Designing User-Friendly Medical Devices and Software
• HCI and Gaming: Enhancing Player Engagement and Experience
• The Use of HCI in Robotic Systems: Improving Human-Robot Interaction
• The Influence of HCI on E-commerce: Optimizing User Journeys and Conversions
• HCI in Smart Homes: Interaction Design for Automated Environments
• Multimodal Interaction: Integrating Touch, Voice, and Gesture in HCI
• HCI and Aging: Designing Technology for Older Adults
• The Role of HCI in Virtual Teams: Tools and Strategies for Collaboration
• User-Centered Design: HCI Strategies for Developing User-Focused Software
• HCI Research Methodologies: Experimental Design and User Studies
• The Application of HCI Principles in the Design of Public Kiosks
• The Future of HCI: Integrating Artificial Intelligence for Smarter Interfaces
• HCI in Transportation: Designing User Interfaces for Autonomous Vehicles
• Privacy and Ethics in HCI: Addressing User Data Security
• HCI and Environmental Sustainability: Promoting Eco-Friendly Behaviors
• Adaptive Interfaces: HCI Design for Personalized User Experiences
• The Role of HCI in Content Creation: Tools for Artists and Designers
• HCI for Crisis Management: Designing Systems for Emergency Use
• The Use of HCI in Sports Technology: Enhancing Training and Performance
• The Evolution of Haptic Feedback in HCI
• HCI and Cultural Differences: Designing for Global User Bases
• The Impact of HCI on Digital Marketing: Creating Engaging User Interactions
• HCI in Financial Services: Improving User Interfaces for Banking Apps
• The Role of HCI in Enhancing User Trust in Technology
• HCI for Public Safety: User Interfaces for Security Systems
• The Application of HCI in the Film and Television Industry
• HCI and the Future of Work: Designing Interfaces for Remote Collaboration
• Innovations in HCI: Exploring New Interaction Technologies and Their Applications
• Deep Learning Techniques for Advanced Image Segmentation
• Real-Time Image Processing for Autonomous Driving Systems
• Image Enhancement Algorithms for Underwater Imaging
• Super-Resolution Imaging: Techniques and Applications
• The Role of Image Processing in Remote Sensing and Satellite Imagery Analysis
• Machine Learning Models for Medical Image Diagnosis
• The Impact of AI on Photographic Restoration and Enhancement
• Image Processing in Security Systems: Facial Recognition and Motion Detection
• Advanced Algorithms for Image Noise Reduction
• 3D Image Reconstruction Techniques in Tomography
• Image Processing for Agricultural Monitoring: Crop Disease Detection and Yield Prediction
• Techniques for Panoramic Image Stitching
• Video Image Processing: Real-Time Streaming and Data Compression
• The Application of Image Processing in Printing Technology
• Color Image Processing: Theory and Practical Applications
• The Use of Image Processing in Biometrics Identification
• Computational Photography: Image Processing Techniques in Smartphone Cameras
• Image Processing for Augmented Reality: Real-time Object Overlay
• The Development of Image Processing Algorithms for Traffic Control Systems
• Pattern Recognition and Analysis in Forensic Imaging
• Adaptive Filtering Techniques in Image Processing
• Image Processing in Retail: Customer Tracking and Behavior Analysis
• The Role of Image Processing in Cultural Heritage Preservation
• Image Segmentation Techniques for Cancer Detection in Medical Imaging
• High Dynamic Range (HDR) Imaging: Algorithms and Display Techniques
• Image Classification with Deep Convolutional Neural Networks
• The Evolution of Edge Detection Algorithms in Image Processing
• Image Processing for Wildlife Monitoring: Species Recognition and Behavior Analysis
• Application of Wavelet Transforms in Image Compression
• Image Processing in Sports: Enhancing Broadcasts and Performance Analysis
• Optical Character Recognition (OCR) Improvements in Document Scanning
• Multi-Spectral Imaging for Environmental and Earth Studies
• Image Processing for Space Exploration: Analysis of Planetary Images
• Real-Time Image Processing for Event Surveillance
• The Influence of Quantum Computing on Image Processing Speed and Security
• Machine Vision in Manufacturing: Defect Detection and Quality Control
• Image Processing in Neurology: Visualizing Brain Functions
• Photogrammetry and Image Processing in Geology: 3D Terrain Mapping
• Advanced Techniques in Image Watermarking for Copyright Protection
• The Future of Image Processing: Integrating AI for Automated Editing
• The Evolution of Enterprise Resource Planning (ERP) Systems in the Digital Age
• Information Systems for Managing Distributed Workforces
• The Role of Information Systems in Enhancing Supply Chain Management
• Cybersecurity Measures in Information Systems
• The Impact of Big Data on Decision Support Systems
• Blockchain Technology for Information System Security
• The Development of Sustainable IT Infrastructure in Information Systems
• The Use of AI in Information Systems for Business Intelligence
• Information Systems in Healthcare: Improving Patient Care and Data Management
• The Influence of IoT on Information Systems Architecture
• Mobile Information Systems: Development and Usability Challenges
• The Role of Geographic Information Systems (GIS) in Urban Planning
• Social Media Analytics: Tools and Techniques in Information Systems
• Information Systems in Education: Enhancing Learning and Administration
• Cloud Computing Integration into Corporate Information Systems
• Information Systems Audit: Practices and Challenges
• User Interface Design and User Experience in Information Systems
• Privacy and Data Protection in Information Systems
• The Future of Quantum Computing in Information Systems
• The Role of Information Systems in Environmental Management
• Implementing Effective Knowledge Management Systems
• The Adoption of Virtual Reality in Information Systems
• The Challenges of Implementing ERP Systems in Multinational Corporations
• Information Systems for Real-Time Business Analytics
• The Impact of 5G Technology on Mobile Information Systems
• Ethical Issues in the Management of Information Systems
• Information Systems in Retail: Enhancing Customer Experience and Management
• The Role of Information Systems in Non-Profit Organizations
• Development of Decision Support Systems for Strategic Planning
• Information Systems in the Banking Sector: Enhancing Financial Services
• Risk Management in Information Systems
• The Integration of Artificial Neural Networks in Information Systems
• Information Systems and Corporate Governance
• Information Systems for Disaster Response and Management
• The Role of Information Systems in Sports Management
• Information Systems for Public Health Surveillance
• The Future of Information Systems: Trends and Predictions
• Information Systems in the Film and Media Industry
• Business Process Reengineering through Information Systems
• Implementing Customer Relationship Management (CRM) Systems in E-commerce
• Emerging Trends in Artificial Intelligence and Machine Learning
• The Future of Cloud Services and Technology
• Cybersecurity: Current Threats and Future Defenses
• The Role of Information Technology in Sustainable Energy Solutions
• Internet of Things (IoT): From Smart Homes to Smart Cities
• Blockchain and Its Impact on Information Technology
• The Use of Big Data Analytics in Predictive Modeling
• Virtual Reality (VR) and Augmented Reality (AR): The Next Frontier in IT
• The Challenges of Digital Transformation in Traditional Businesses
• Wearable Technology: Health Monitoring and Beyond
• 5G Technology: Implementation and Impacts on IT
• Biometrics Technology: Uses and Privacy Concerns
• The Role of IT in Global Health Initiatives
• Ethical Considerations in the Development of Autonomous Systems
• Data Privacy in the Age of Information Overload
• The Evolution of Software Development Methodologies
• Quantum Computing: The Next Revolution in IT
• IT Governance: Best Practices and Standards
• The Integration of AI in Customer Service Technology
• IT in Manufacturing: Industrial Automation and Robotics
• The Future of E-commerce: Technology and Trends
• Mobile Computing: Innovations and Challenges
• Information Technology in Education: Tools and Trends
• IT Project Management: Approaches and Tools
• The Role of IT in Media and Entertainment
• The Impact of Digital Marketing Technologies on Business Strategies
• IT in Logistics and Supply Chain Management
• The Development and Future of Autonomous Vehicles
• IT in the Insurance Sector: Enhancing Efficiency and Customer Engagement
• The Role of IT in Environmental Conservation
• Smart Grid Technology: IT at the Intersection of Energy Management
• Telemedicine: The Impact of IT on Healthcare Delivery
• IT in the Agricultural Sector: Innovations and Impact
• Cyber-Physical Systems: IT in the Integration of Physical and Digital Worlds
• The Influence of Social Media Platforms on IT Development
• Data Centers: Evolution, Technologies, and Sustainability
• IT in Public Administration: Improving Services and Transparency
• The Role of IT in Sports Analytics
• Information Technology in Retail: Enhancing the Shopping Experience
• The Future of IT: Integrating Ethical AI Systems

Internet of Things (IoT) Thesis Topics

• Enhancing IoT Security: Strategies for Safeguarding Connected Devices
• IoT in Smart Cities: Infrastructure and Data Management Challenges
• The Application of IoT in Precision Agriculture: Maximizing Efficiency and Yield
• IoT and Healthcare: Opportunities for Remote Monitoring and Patient Care
• Energy Efficiency in IoT: Techniques for Reducing Power Consumption in Devices
• The Role of IoT in Supply Chain Management and Logistics
• Real-Time Data Processing Using Edge Computing in IoT Networks
• Privacy Concerns and Data Protection in IoT Systems
• The Integration of IoT with Blockchain for Enhanced Security and Transparency
• IoT in Environmental Monitoring: Systems for Air Quality and Water Safety
• Predictive Maintenance in Industrial IoT: Strategies and Benefits
• IoT in Retail: Enhancing Customer Experience through Smart Technology
• The Development of Standard Protocols for IoT Communication
• IoT in Smart Homes: Automation and Security Systems
• The Role of IoT in Disaster Management: Early Warning Systems and Response Coordination
• Machine Learning Techniques for IoT Data Analytics
• IoT in Automotive: The Future of Connected and Autonomous Vehicles
• The Impact of 5G on IoT: Enhancements in Speed and Connectivity
• IoT Device Lifecycle Management: From Creation to Decommissioning
• IoT in Public Safety: Applications for Emergency Response and Crime Prevention
• The Ethics of IoT: Balancing Innovation with Consumer Rights
• IoT and the Future of Work: Automation and Labor Market Shifts
• Designing User-Friendly Interfaces for IoT Applications
• IoT in the Energy Sector: Smart Grids and Renewable Energy Integration
• Quantum Computing and IoT: Potential Impacts and Applications
• The Role of AI in Enhancing IoT Solutions
• IoT for Elderly Care: Technologies for Health and Mobility Assistance
• IoT in Education: Enhancing Classroom Experiences and Learning Outcomes
• Challenges in Scaling IoT Infrastructure for Global Coverage
• The Economic Impact of IoT: Industry Transformations and New Business Models
• IoT and Tourism: Enhancing Visitor Experiences through Connected Technologies
• Data Fusion Techniques in IoT: Integrating Diverse Data Sources
• IoT in Aquaculture: Monitoring and Managing Aquatic Environments
• Wireless Technologies for IoT: Comparing LoRa, Zigbee, and NB-IoT
• IoT and Intellectual Property: Navigating the Legal Landscape
• IoT in Sports: Enhancing Training and Audience Engagement
• Building Resilient IoT Systems against Cyber Attacks
• IoT for Waste Management: Innovations and System Implementations
• IoT in Agriculture: Drones and Sensors for Crop Monitoring
• The Role of IoT in Cultural Heritage Preservation: Monitoring and Maintenance
• Advanced Algorithms for Supervised and Unsupervised Learning
• Machine Learning in Genomics: Predicting Disease Propensity and Treatment Outcomes
• The Use of Neural Networks in Image Recognition and Analysis
• Reinforcement Learning: Applications in Robotics and Autonomous Systems
• The Role of Machine Learning in Natural Language Processing and Linguistic Analysis
• Deep Learning for Predictive Analytics in Business and Finance
• Machine Learning for Cybersecurity: Detection of Anomalies and Malware
• Ethical Considerations in Machine Learning: Bias and Fairness
• The Integration of Machine Learning with IoT for Smart Device Management
• Transfer Learning: Techniques and Applications in New Domains
• The Application of Machine Learning in Environmental Science
• Machine Learning in Healthcare: Diagnosing Conditions from Medical Images
• The Use of Machine Learning in Algorithmic Trading and Stock Market Analysis
• Machine Learning in Social Media: Sentiment Analysis and Trend Prediction
• Quantum Machine Learning: Merging Quantum Computing with AI
• Feature Engineering and Selection in Machine Learning
• Machine Learning for Enhancing User Experience in Mobile Applications
• The Impact of Machine Learning on Digital Marketing Strategies
• Machine Learning for Energy Consumption Forecasting and Optimization
• The Role of Machine Learning in Enhancing Network Security Protocols
• Scalability and Efficiency of Machine Learning Algorithms
• Machine Learning in Drug Discovery and Pharmaceutical Research
• The Application of Machine Learning in Sports Analytics
• Machine Learning for Real-Time Decision-Making in Autonomous Vehicles
• The Use of Machine Learning in Predicting Geographical and Meteorological Events
• Machine Learning for Educational Data Mining and Learning Analytics
• The Role of Machine Learning in Audio Signal Processing
• Predictive Maintenance in Manufacturing Through Machine Learning
• Machine Learning and Its Implications for Privacy and Surveillance
• The Application of Machine Learning in Augmented Reality Systems
• Deep Learning Techniques in Medical Diagnosis: Challenges and Opportunities
• The Use of Machine Learning in Video Game Development
• Machine Learning for Fraud Detection in Financial Services
• The Role of Machine Learning in Agricultural Optimization and Management
• The Impact of Machine Learning on Content Personalization and Recommendation Systems
• Machine Learning in Legal Tech: Document Analysis and Case Prediction
• Adaptive Learning Systems: Tailoring Education Through Machine Learning
• Machine Learning in Space Exploration: Analyzing Data from Space Missions
• Machine Learning for Public Sector Applications: Improving Services and Efficiency
• The Future of Machine Learning: Integrating Explainable AI
• Innovations in Convolutional Neural Networks for Image and Video Analysis
• Recurrent Neural Networks: Applications in Sequence Prediction and Analysis
• The Role of Neural Networks in Predicting Financial Market Trends
• Deep Neural Networks for Enhanced Speech Recognition Systems
• Neural Networks in Medical Imaging: From Detection to Diagnosis
• Generative Adversarial Networks (GANs): Applications in Art and Media
• The Use of Neural Networks in Autonomous Driving Technologies
• Neural Networks for Real-Time Language Translation
• The Application of Neural Networks in Robotics: Sensory Data and Movement Control
• Neural Network Optimization Techniques: Overcoming Overfitting and Underfitting
• The Integration of Neural Networks with Blockchain for Data Security
• Neural Networks in Climate Modeling and Weather Forecasting
• The Use of Neural Networks in Enhancing Internet of Things (IoT) Devices
• Graph Neural Networks: Applications in Social Network Analysis and Beyond
• The Impact of Neural Networks on Augmented Reality Experiences
• Neural Networks for Anomaly Detection in Network Security
• The Application of Neural Networks in Bioinformatics and Genomic Data Analysis
• Capsule Neural Networks: Improving the Robustness and Interpretability of Deep Learning
• The Role of Neural Networks in Consumer Behavior Analysis
• Neural Networks in Energy Sector: Forecasting and Optimization
• The Evolution of Neural Network Architectures for Efficient Learning
• The Use of Neural Networks in Sentiment Analysis: Techniques and Challenges
• Deep Reinforcement Learning: Strategies for Advanced Decision-Making Systems
• Neural Networks for Precision Medicine: Tailoring Treatments to Individual Genetic Profiles
• The Use of Neural Networks in Virtual Assistants: Enhancing Natural Language Understanding
• The Impact of Neural Networks on Pharmaceutical Research
• Neural Networks for Supply Chain Management: Prediction and Automation
• The Application of Neural Networks in E-commerce: Personalization and Recommendation Systems
• Neural Networks for Facial Recognition: Advances and Ethical Considerations
• The Role of Neural Networks in Educational Technologies
• The Use of Neural Networks in Predicting Economic Trends
• Neural Networks in Sports: Analyzing Performance and Strategy
• The Impact of Neural Networks on Digital Security Systems
• Neural Networks for Real-Time Video Surveillance Analysis
• The Integration of Neural Networks in Edge Computing Devices
• Neural Networks for Industrial Automation: Improving Efficiency and Accuracy
• The Future of Neural Networks: Towards More General AI Applications
• Neural Networks in Art and Design: Creating New Forms of Expression
• The Role of Neural Networks in Enhancing Public Health Initiatives
• The Future of Neural Networks: Challenges in Scalability and Generalization
• The Evolution of Programming Paradigms: Functional vs. Object-Oriented Programming
• Advances in Compiler Design and Optimization Techniques
• The Impact of Programming Languages on Software Security
• Developing Programming Languages for Quantum Computing
• Machine Learning in Automated Code Generation and Optimization
• The Role of Programming in Developing Scalable Cloud Applications
• The Future of Web Development: New Frameworks and Technologies
• Cross-Platform Development: Best Practices in Mobile App Programming
• The Influence of Programming Techniques on Big Data Analytics
• Real-Time Systems Programming: Challenges and Solutions
• The Integration of Programming with Blockchain Technology
• Programming for IoT: Languages and Tools for Device Communication
• Secure Coding Practices: Preventing Cyber Attacks through Software Design
• The Role of Programming in Data Visualization and User Interface Design
• Advances in Game Programming: Graphics, AI, and Network Play
• The Impact of Programming on Digital Media and Content Creation
• Programming Languages for Robotics: Trends and Future Directions
• The Use of Artificial Intelligence in Enhancing Programming Productivity
• Programming for Augmented and Virtual Reality: New Challenges and Techniques
• Ethical Considerations in Programming: Bias, Fairness, and Transparency
• The Future of Programming Education: Interactive and Adaptive Learning Models
• Programming for Wearable Technology: Special Considerations and Challenges
• The Evolution of Programming in Financial Technology
• Functional Programming in Enterprise Applications
• Memory Management Techniques in Programming: From Garbage Collection to Manual Control
• The Role of Open Source Programming in Accelerating Innovation
• The Impact of Programming on Network Security and Cryptography
• Developing Accessible Software: Programming for Users with Disabilities
• Programming Language Theories: New Models and Approaches
• The Challenges of Legacy Code: Strategies for Modernization and Integration
• Energy-Efficient Programming: Optimizing Code for Green Computing
• Multithreading and Concurrency: Advanced Programming Techniques
• The Impact of Programming on Computational Biology and Bioinformatics
• The Role of Scripting Languages in Automating System Administration
• Programming and the Future of Quantum Resistant Cryptography
• Code Review and Quality Assurance: Techniques and Tools
• Adaptive and Predictive Programming for Dynamic Environments
• The Role of Programming in Enhancing E-commerce Technology
• Programming for Cyber-Physical Systems: Bridging the Gap Between Digital and Physical
• The Influence of Programming Languages on Computational Efficiency and Performance
• Quantum Algorithms: Development and Applications Beyond Shor’s and Grover’s Algorithms
• The Role of Quantum Computing in Solving Complex Biological Problems
• Quantum Cryptography: New Paradigms for Secure Communication
• Error Correction Techniques in Quantum Computing
• Quantum Computing and Its Impact on Artificial Intelligence
• The Integration of Classical and Quantum Computing: Hybrid Models
• Quantum Machine Learning: Theoretical Foundations and Practical Applications
• Quantum Computing Hardware: Advances in Qubit Technology
• The Application of Quantum Computing in Financial Modeling and Risk Assessment
• Quantum Networking: Establishing Secure Quantum Communication Channels
• The Future of Drug Discovery: Applications of Quantum Computing
• Quantum Computing in Cryptanalysis: Threats to Current Cryptography Standards
• Simulation of Quantum Systems for Material Science
• Quantum Computing for Optimization Problems in Logistics and Manufacturing
• Theoretical Limits of Quantum Computing: Understanding Quantum Complexity
• Quantum Computing and the Future of Search Algorithms
• The Role of Quantum Computing in Climate Science and Environmental Modeling
• Quantum Annealing vs. Universal Quantum Computing: Comparative Studies
• Implementing Quantum Algorithms in Quantum Programming Languages
• The Impact of Quantum Computing on Public Key Cryptography
• Quantum Entanglement: Experiments and Applications in Quantum Networks
• Scalability Challenges in Quantum Processors
• The Ethics and Policy Implications of Quantum Computing
• Quantum Computing in Space Exploration and Astrophysics
• The Role of Quantum Computing in Developing Next-Generation AI Systems
• Quantum Computing in the Energy Sector: Applications in Smart Grids and Nuclear Fusion
• Noise and Decoherence in Quantum Computers: Overcoming Practical Challenges
• Quantum Computing for Predicting Economic Market Trends
• Quantum Sensors: Enhancing Precision in Measurement and Imaging
• The Future of Quantum Computing Education and Workforce Development
• Quantum Computing in Cybersecurity: Preparing for a Post-Quantum World
• Quantum Computing and the Internet of Things: Potential Intersections
• Practical Quantum Computing: From Theory to Real-World Applications
• Quantum Supremacy: Milestones and Future Goals
• The Role of Quantum Computing in Genetics and Genomics
• Quantum Computing for Material Discovery and Design
• The Challenges of Quantum Programming Languages and Environments
• Quantum Computing in Art and Creative Industries
• The Global Race for Quantum Computing Supremacy: Technological and Political Aspects
• Quantum Computing and Its Implications for Software Engineering
• Advances in Humanoid Robotics: New Developments and Challenges
• Robotics in Healthcare: From Surgery to Rehabilitation
• The Integration of AI in Robotics: Enhanced Autonomy and Learning Capabilities
• Swarm Robotics: Coordination Strategies and Applications
• The Use of Robotics in Hazardous Environments: Deep Sea and Space Exploration
• Soft Robotics: Materials, Design, and Applications
• Robotics in Agriculture: Automation of Farming and Harvesting Processes
• The Role of Robotics in Manufacturing: Increased Efficiency and Flexibility
• Ethical Considerations in the Deployment of Robots in Human Environments
• Autonomous Vehicles: Technological Advances and Regulatory Challenges
• Robotic Assistants for the Elderly and Disabled: Improving Quality of Life
• The Use of Robotics in Education: Teaching Science, Technology, Engineering, and Math (STEM)
• Robotics and Computer Vision: Enhancing Perception and Decision Making
• The Impact of Robotics on Employment and the Workforce
• The Development of Robotic Systems for Environmental Monitoring and Conservation
• Machine Learning Techniques for Robotic Perception and Navigation
• Advances in Robotic Surgery: Precision and Outcomes
• Human-Robot Interaction: Building Trust and Cooperation
• Robotics in Retail: Automated Warehousing and Customer Service
• Energy-Efficient Robots: Design and Utilization
• Robotics in Construction: Automation and Safety Improvements
• The Role of Robotics in Disaster Response and Recovery Operations
• The Application of Robotics in Art and Creative Industries
• Robotics and the Future of Personal Transportation
• Ethical AI in Robotics: Ensuring Safe and Fair Decision-Making
• The Use of Robotics in Logistics: Drones and Autonomous Delivery Vehicles
• Robotics in the Food Industry: From Production to Service
• The Integration of IoT with Robotics for Enhanced Connectivity
• Wearable Robotics: Exoskeletons for Rehabilitation and Enhanced Mobility
• The Impact of Robotics on Privacy and Security
• Robotic Pet Companions: Social Robots and Their Psychological Effects
• Robotics for Planetary Exploration and Colonization
• Underwater Robotics: Innovations in Oceanography and Marine Biology
• Advances in Robotics Programming Languages and Tools
• The Role of Robotics in Minimizing Human Exposure to Contaminants and Pathogens
• Collaborative Robots (Cobots): Working Alongside Humans in Shared Spaces
• The Use of Robotics in Entertainment and Sports
• Robotics and Machine Ethics: Programming Moral Decision-Making
• The Future of Military Robotics: Opportunities and Challenges
• Sustainable Robotics: Reducing the Environmental Impact of Robotic Systems
• Agile Methodologies: Evolution and Future Trends
• DevOps Practices: Improving Software Delivery and Lifecycle Management
• The Impact of Microservices Architecture on Software Development
• Containerization Technologies: Docker, Kubernetes, and Beyond
• Software Quality Assurance: Modern Techniques and Tools
• The Role of Artificial Intelligence in Automated Software Testing
• Blockchain Applications in Software Development and Security
• The Integration of Continuous Integration and Continuous Deployment (CI/CD) in Software Projects
• Cybersecurity in Software Engineering: Best Practices for Secure Coding
• Low-Code and No-Code Development: Implications for Professional Software Development
• The Future of Software Engineering Education
• Software Sustainability: Developing Green Software and Reducing Carbon Footprints
• The Role of Software Engineering in Healthcare: Telemedicine and Patient Data Management
• Privacy by Design: Incorporating Privacy Features at the Development Stage
• The Impact of Quantum Computing on Software Engineering
• Software Engineering for Augmented and Virtual Reality: Challenges and Innovations
• Cloud-Native Applications: Design, Development, and Deployment
• Software Project Management: Agile vs. Traditional Approaches
• Open Source Software: Community Engagement and Project Sustainability
• The Evolution of Graphical User Interfaces in Application Development
• The Challenges of Integrating IoT Devices into Software Systems
• Ethical Issues in Software Engineering: Bias, Accountability, and Regulation
• Software Engineering for Autonomous Vehicles: Safety and Regulatory Considerations
• Big Data Analytics in Software Development: Enhancing Decision-Making Processes
• The Future of Mobile App Development: Trends and Technologies
• The Role of Software Engineering in Artificial Intelligence: Frameworks and Algorithms
• Performance Optimization in Software Applications
• Adaptive Software Development: Responding to Changing User Needs
• Software Engineering in Financial Services: Compliance and Security Challenges
• User Experience (UX) Design in Software Engineering
• The Role of Software Engineering in Smart Cities: Infrastructure and Services
• The Impact of 5G on Software Development and Deployment
• Real-Time Systems in Software Engineering: Design and Implementation Challenges
• Cross-Platform Development Challenges: Ensuring Consistency and Performance
• Software Testing Automation: Tools and Trends
• The Integration of Cyber-Physical Systems in Software Engineering
• Software Engineering in the Entertainment Industry: Game Development and Beyond
• The Application of Machine Learning in Predicting Software Bugs
• The Role of Software Engineering in Cybersecurity Defense Strategies
• Accessibility in Software Engineering: Creating Inclusive and Usable Software
• Progressive Web Apps (PWAs): Advantages and Implementation Challenges
• The Future of Web Accessibility: Standards and Practices
• Single-Page Applications (SPAs) vs. Multi-Page Applications (MPAs): Performance and Usability
• The Impact of Serverless Computing on Web Development
• The Evolution of CSS for Modern Web Design
• Security Best Practices in Web Development: Defending Against XSS and CSRF Attacks
• The Role of Web Development in Enhancing E-commerce User Experience
• The Use of Artificial Intelligence in Web Personalization and User Engagement
• The Future of Web APIs: Standards, Security, and Scalability
• Responsive Web Design: Techniques and Trends
• JavaScript Frameworks: Vue.js, React.js, and Angular – A Comparative Analysis
• Web Development for IoT: Interfaces and Connectivity Solutions
• The Impact of 5G on Web Development and User Experiences
• The Use of Blockchain Technology in Web Development for Enhanced Security
• Web Development in the Cloud: Using AWS, Azure, and Google Cloud
• Content Management Systems (CMS): Trends and Future Developments
• The Application of Web Development in Virtual and Augmented Reality
• The Importance of Web Performance Optimization: Tools and Techniques
• Sustainable Web Design: Practices for Reducing Energy Consumption
• The Role of Web Development in Digital Marketing: SEO and Social Media Integration
• Headless CMS: Benefits and Challenges for Developers and Content Creators
• The Future of Web Typography: Design, Accessibility, and Performance
• Web Development and Data Protection: Complying with GDPR and Other Regulations
• Real-Time Web Communication: Technologies like WebSockets and WebRTC
• Front-End Development Tools: Efficiency and Innovation in Workflow
• The Challenges of Migrating Legacy Systems to Modern Web Architectures
• Microfrontends Architecture: Designing Scalable and Decoupled Web Applications
• The Impact of Cryptocurrencies on Web Payment Systems
• User-Centered Design in Web Development: Methods for Engaging Users
• The Role of Web Development in Business Intelligence: Dashboards and Reporting Tools
• Web Development for Mobile Platforms: Optimization and Best Practices
• The Evolution of E-commerce Platforms: From Web to Mobile Commerce
• Web Security in E-commerce: Protecting Transactions and User Data
• Dynamic Web Content: Server-Side vs. Client-Side Rendering
• The Future of Full Stack Development: Trends and Skills
• Web Design Psychology: How Design Influences User Behavior
• The Role of Web Development in the Non-Profit Sector: Fundraising and Community Engagement
• The Integration of AI Chatbots in Web Development
• The Use of Motion UI in Web Design: Enhancing Aesthetics and User Interaction
• The Future of Web Development: Predictions and Emerging Technologies

We trust that this comprehensive list of computer science thesis topics will serve as a valuable starting point for your research endeavors. With 1000 unique and carefully selected topics distributed across 25 key areas of computer science, students are equipped to tackle complex questions and contribute meaningful advancements to the field. As you proceed to select your thesis topic, consider not only your personal interests and career goals but also the potential impact of your research. We encourage you to explore these topics thoroughly and choose one that will not only challenge you but also push the boundaries of technology and innovation.

The Range of Computer Science Thesis Topics

Computer science stands as a dynamic and ever-evolving field that continuously reshapes how we interact with the world. At its core, the discipline encompasses not just the study of algorithms and computation, but a broad spectrum of practical and theoretical knowledge areas that drive innovation in various sectors. This article aims to explore the rich landscape of computer science thesis topics, offering students and researchers a glimpse into the potential areas of study that not only challenge the intellect but also contribute significantly to technological progress. As we delve into the current issues, recent trends, and future directions of computer science, it becomes evident that the possibilities for research are both vast and diverse. Whether you are intrigued by the complexities of artificial intelligence, the robust architecture of networks and systems, or the innovative approaches in cybersecurity, computer science offers a fertile ground for developing thesis topics that are as impactful as they are intellectually stimulating.

Current Issues in Computer Science

One of the prominent current issues in computer science revolves around data security and privacy. As digital transformation accelerates across industries, the massive influx of data generated poses significant challenges in terms of its protection and ethical use. Cybersecurity threats have become more sophisticated, with data breaches and cyber-attacks causing major concerns for organizations worldwide. This ongoing battle demands continuous improvements in security protocols and the development of robust cybersecurity measures. Computer science thesis topics in this area can explore new cryptographic methods, intrusion detection systems, and secure communication protocols to fortify digital defenses. Research could also delve into the ethical implications of data collection and use, proposing frameworks that ensure privacy while still leveraging data for innovation.

Another critical issue facing the field of computer science is the ethical development and deployment of artificial intelligence (AI) systems. As AI technologies become more integrated into daily life and critical infrastructure, concerns about bias, fairness, and accountability in AI systems have intensified. Thesis topics could focus on developing algorithms that address these ethical concerns, including techniques for reducing bias in machine learning models and methods for increasing transparency and explainability in AI decisions. This research is crucial for ensuring that AI technologies promote fairness and do not perpetuate or exacerbate existing societal inequalities.

Furthermore, the rapid pace of technological change presents a challenge in terms of sustainability and environmental impact. The energy consumption of large data centers, the carbon footprint of producing and disposing of electronic waste, and the broader effects of high-tech innovations on the environment are significant concerns within computer science. Thesis research in this domain could focus on creating more energy-efficient computing methods, developing algorithms that reduce power consumption, or innovating recycling technologies that address the issue of e-waste. This research not only contributes to the field of computer science but also plays a crucial role in ensuring that technological advancement does not come at an unsustainable cost to the environment.

These current issues highlight the dynamic nature of computer science and its direct impact on society. Addressing these challenges through focused research and innovative thesis topics not only advances the field but also contributes to resolving some of the most pressing problems facing our global community today.

Recent Trends in Computer Science

In recent years, computer science has witnessed significant advancements in the integration of artificial intelligence (AI) and machine learning (ML) across various sectors, marking one of the most exciting trends in the field. These technologies are not just reshaping traditional industries but are also at the forefront of driving innovations in areas like healthcare, finance, and autonomous systems. Thesis topics within this trend could explore the development of advanced ML algorithms that enhance predictive analytics, improve automated decision-making, or refine natural language processing capabilities. Additionally, AI’s role in ethical decision-making and its societal impacts offers a rich vein of inquiry for research, focusing on mitigating biases and ensuring that AI systems operate transparently and justly.

Another prominent trend in computer science is the rapid growth of blockchain technology beyond its initial application in cryptocurrencies. Blockchain is proving its potential in creating more secure, decentralized, and transparent networks for a variety of applications, from enhancing supply chain logistics to revolutionizing digital identity verification processes. Computer science thesis topics could investigate novel uses of blockchain for ensuring data integrity in digital transactions, enhancing cybersecurity measures, or even developing new frameworks for blockchain integration into existing technological infrastructures. The exploration of blockchain’s scalability, speed, and energy consumption also presents critical research opportunities that are timely and relevant.

Furthermore, the expansion of the Internet of Things (IoT) continues to be a significant trend, with more devices becoming connected every day, leading to increasingly smart environments. This proliferation poses unique challenges and opportunities for computer science research, particularly in terms of scalability, security, and new data management strategies. Thesis topics might focus on optimizing network protocols to handle the massive influx of data from IoT devices, developing solutions to safeguard against IoT-specific security vulnerabilities, or innovative applications of IoT in urban planning, smart homes, or healthcare. Research in this area is crucial for advancing the efficiency and functionality of IoT systems and for ensuring they can be safely and effectively integrated into modern life.

These recent trends underscore the vibrant and ever-evolving nature of computer science, reflecting its capacity to influence and transform an array of sectors through technological innovation. The continual emergence of new research topics within these trends not only enriches the academic discipline but also provides substantial benefits to society by addressing practical challenges and enhancing the capabilities of technology in everyday life.

Future Directions in Computer Science

As we look toward the future, one of the most anticipated areas in computer science is the advancement of quantum computing. This emerging technology promises to revolutionize problem-solving in fields that require immense computational power, such as cryptography, drug discovery, and complex system modeling. Quantum computing has the potential to process tasks at speeds unachievable by classical computers, offering breakthroughs in materials science and encryption methods. Computer science thesis topics might explore the theoretical underpinnings of quantum algorithms, the development of quantum-resistant cryptographic systems, or practical applications of quantum computing in industry-specific scenarios. Research in this area not only contributes to the foundational knowledge of quantum mechanics but also paves the way for its integration into mainstream computing, marking a significant leap forward in computational capabilities.

Another promising direction in computer science is the advancement of autonomous systems, particularly in robotics and vehicle automation. The future of autonomous technologies hinges on improving their safety, reliability, and decision-making processes under uncertain conditions. Thesis topics could focus on the enhancement of machine perception through computer vision and sensor fusion, the development of more sophisticated AI-driven decision frameworks, or ethical considerations in the deployment of autonomous systems. As these technologies become increasingly prevalent, research will play a crucial role in addressing the societal and technical challenges they present, ensuring their beneficial integration into daily life and industry operations.

Additionally, the ongoing expansion of artificial intelligence applications poses significant future directions for research, especially in the realm of AI ethics and policy. As AI systems become more capable and widespread, their impact on privacy, employment, and societal norms continues to grow. Future thesis topics might delve into the development of guidelines and frameworks for responsible AI, studies on the impact of AI on workforce dynamics, or innovations in transparent and fair AI systems. This research is vital for guiding the ethical evolution of AI technologies, ensuring they enhance societal well-being without diminishing human dignity or autonomy.

These future directions in computer science not only highlight the field’s potential for substantial technological advancements but also underscore the importance of thoughtful consideration of their broader implications. By exploring these areas in depth, computer science research can lead the way in not just technological innovation, but also in shaping a future where technology and ethics coexist harmoniously for the betterment of society.

In conclusion, the field of computer science is not only foundational to the technological advancements that characterize the modern age but also crucial in solving some of the most pressing challenges of our time. The potential thesis topics discussed in this article reflect a mere fraction of the opportunities that lie in the realms of theory, application, and innovation within this expansive field. As emerging technologies such as quantum computing, artificial intelligence, and blockchain continue to evolve, they open new avenues for research that could potentially redefine existing paradigms. For students embarking on their thesis journey, it is essential to choose a topic that not only aligns with their academic passions but also contributes to the ongoing expansion of computer science knowledge. By pushing the boundaries of what is known and exploring uncharted territories, students can leave a lasting impact on the field and pave the way for future technological breakthroughs. As we look forward, it’s clear that computer science will continue to be a key driver of change, making it an exciting and rewarding area for academic and professional growth.

Thesis Writing Services by iResearchNet

At iResearchNet, we specialize in providing exceptional thesis writing services tailored to meet the diverse needs of students, particularly those pursuing advanced topics in computer science. Understanding the pivotal role a thesis plays in a student’s academic career, we offer a suite of services designed to assist students in crafting papers that are not only well-researched and insightful but also perfectly aligned with their academic objectives. Here are the key features of our thesis writing services:

• Expert Degree-Holding Writers : Our team consists of writers who hold advanced degrees in computer science and related fields. Their academic and professional backgrounds ensure that they bring a wealth of knowledge and expertise to your thesis.
• Custom Written Works : Every thesis we produce is tailor-made to meet the specific requirements and guidelines provided by the student. This bespoke approach ensures that each paper is unique and of the highest quality.
• In-depth Research : We pride ourselves on conducting thorough and comprehensive research for every thesis. Our writers utilize the latest resources, databases, and scholarly articles to gather the most relevant and up-to-date information.
• Custom Formatting : Each thesis is formatted according to academic standards and the specific requirements of the student’s program, whether it’s APA, MLA, Chicago/Turabian, or Harvard style.
• Top Quality : Quality is at the core of our services. From language clarity to factual accuracy, each thesis is crafted to meet the highest academic standards.
• Customized Solutions : Recognizing that every student’s needs are different, we offer customized solutions that cater to individual preferences and requirements.
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At iResearchNet, we are dedicated to supporting students by providing them with high-quality, reliable, and professional thesis writing services. By choosing us, students can be confident that they are receiving expert help that not only meets but exceeds their expectations. Whether you are tackling complex topics in computer science or any other academic discipline, our team is here to help you achieve academic success.

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For current information about the demonstrations, see uva.nl/protests External link . 

Presentation Master's thesis - Pippi Joannes - Developmental psychology

Roeterseilandcampus, Gebouw: G, Straat: Nieuwe Achtergracht 129-B, Ruimte: GS.04

To reduce and stop the increase of mental health problems in students, action must be taken. Yet, still little is known about the aspects contributing to this increase. Within this study, a total of 112 psychology students (30 males, 82 females) from the University of Amsterdam (UvA) participated, aged 18-33 years old. This research examined two hypotheses. Hypothesis 1 stated that sleep quality positively mediates the relationship between academic hassles and mental health among UvA psychology students. Hypothesis 2 stated that intolerance of uncertainty among UvA psychology students moderates the indirect effect between academic hassles and mental health. These hypotheses are examined through moderated mediation analyses. Results showed support for hypothesis 1, suggesting that sleep quality mediates the relationship between academic hassles and mental health. Moreover, results showed that there is no indirect effect moderated by intolerance of uncertainty. This finding is contrary to hypothesis 2. The findings emphasize the need for future research into intolerance of uncertainty as a predictor in order to develop effective intervention programmes.

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Yale college 2024: meet some of the graduates.

Top row, left to right, Nathan Chen, Awa Cisse, and Rohan Krishnan. Second row, left to right, Olivia Sally, Max Hammond, and Mirabel Nguyen.

Here, Yale News spotlights a few of the exceptional members of the Yale College Class of 2024, a group whose accomplishments and contributions have strengthened the Yale campus and the world beyond.

Selected from nominations submitted by residential college heads and deans, these outstanding students include poets and physicists, pianists, and environmentalists, even an Olympic gold medal winner.  One arrived on campus with visions of becoming a neurosurgeon, but soon found his calling as a writer. Another, who came to Yale through the Eli Whitney Students Program, returned to her native Senegal with other Yale scientists waging a long-term battle on malaria. When they weren’t in the classroom, they were assisting migrant communities, creating new curricula for schoolchildren, or looking to prove long-held physics theories.

We hope this small but impressive sample offers a sense of the creativity, compassion, and resilience of the undergraduate Class of 2024.

Nicole Alleyne

For Nicole Alleyne, the human body is a versatile instrument — a maker of music, a channel to the past, a tool for learning, and a link to the land. She has tapped into these potentials while inspiring others to do the same through her academic and extracurricular pursuits at Yale College and beyond.

In her capstone project at Yale, Alleyne reimagined outdoor education with a focus on connecting Black students to the land.  She also did fieldwork for a “land-based pedagogy” project during a gap year, leading a natural science curriculum for 5th-graders in Oregon.

“ Something that is totally missing in traditional education is treating students’ bodies as active parts and interlocutors in their learning experience,” she said. “For instance, when learning about plant species, we were using our senses. We were looking or touching, smelling… Your body has all of these tools in order to learn about the world. How can we use our bodies and these gifts in ways we might not be able to do in the classroom?”

Read more about Nicole Alleyne

Nathan Chen

Nathan Chen always knew his Yale career would be split in two: when he arrived in the fall of 2018, the figure skater already had the 2022 Winter Olympics in his sights, and would need to take time off to prepare and compete.

While on campus, the demands of training meant that his days were packed, with classes in the morning and rink time in the afternoon. And even as he carried a full course load, he embarked on a remarkable competitive winning streak. He continued to dominate the sport during two years of leave from Yale, reaching the summit of the sport with a gold medal in men's single figure skating at the Beijing Olympics in 2022. Then came a whirlwind few months that included talk show appearances and a tour with Stars on Ice.

Yet when he returned to Yale that fall, it was with a renewed sense of excitement at becoming a student again —  and discovering some of the experiences he’d missed the first time around. 

Read more about Nathan Chen

There’s a word that carries deep cultural meaning in Awa Cisse’s native Senegal; Téranga , which comes from the Wolof language, connotes the values of selflessness and generosity, and the importance of giving back to one’s community.

For Cisse, the word téranga isn’t just a reminder of her Senegalese roots, it’s a sort of guidepost for how she wants to live her life.

As a young, girl she frequented hospitals for a variety of reasons and developed an appreciation for  just how many people it takes to provide patient care.  “There is this whole ecosystem of care, from the people who greet you at the front door to those who assist you when you’re leaving,” said Cisse, who came to Yale as part of the Eli Whitney Students Program.

“ Every single piece of the puzzle is important, and it all helps make everything work. This is what inspired me to pursue a life in health care.”

Read more about Awa Cisse

Drop in on Amy Cohen and chances are you’ll find her creating something beautiful: an architectural frame, a laser-cut puzzle, a strawberry cake, an oil painting. The mechanical engineering student’s online portfolio also displays a music box, a ceramic bowl, even a menorah made of hex nuts.

“ My grandfather bought me an Erector Set when I was little — sort of foreshadowing that I’ve always loved making things,” said Cohen.

She chose her major because it combined math and design — “two things I really like.” While completing a professionally accredited track with a near-perfect GPA, she worked with the acclaimed Hatfield Group as a structural engineering intern.  Meanwhile, the mechanical engineering department, recognizing her enthusiasm for the field, chose her as an undergraduate representative for committee and recruitment work.

“ It’s a small major — we’ve all had many classes together, so I’d say it feels like family,” Cohen said. “And I'm lucky to have several professors who have really taken an interest in helping me far beyond what was required in their class.”

Read more about Amy Cohen

Ben Everett-Lane

Ben Everett-Lane’s academic journey has been driven by a deep curiosity about others — and a desire to connect.

When he learned that in 2012 some residents on the Connecticut shoreline decided to stay in their homes despite the threat of Hurricane Sandy, he became fascinated with how they reached this decision. It would become the topic of his undergraduate thesis.

“ How people perceive and behave around climate change is meaningful to me, because fundamentally, we don’t need more science to prove that climate change exists — there’s clearly some disconnect between the science and the way that we perceive it and interact with it,” he said. “I’m interested in being a person who’s able to re-analyze, communicate, and work with different groups of people around climate change to tackle these knowledge gaps.”

Read more about Ben Everett-Lane

Laura Guerra-Lopez

Growing up in Miami, Laura Guerra-Lopez would pepper her parents with questions about their native Venezuela, the once-democratic nation that was by then mired in political and economic chaos.

Looking back, Guerra-Lopez is grateful for the frank, but age-appropriate way her parents included her in those dinnertime conversations.  “They would have real conversations,” she said. “They would tell me about what was going on, but they introduced things in a way that was not so devastating that I turned away from it.”

Those interactions with her parents, and their closeness as a family in a new country where they had no other relatives, ignited her passion for social justice and serving the immigrant community.

Read more about Laura Guerra-Lopez

Max Hammond

Over the winter, Max Hammond gave more time than usual to the piano — about eight hours a day, up from his long-term average of about five. He was applying to conservatories and aiming to impress.

The mathematics major from Los Angeles also took care to provide for his customary allotment of nightly rest: “I can’t function without eight or nine hours,” he said.

The extra keyboard and pillow time paid off: Hammond, a past winner of Yale’s Sharp Prize for “most outstanding performer in the junior class,” was accepted at six top music conservatories. After Yale, he’ll enroll at Julliard as a master’s degree candidate in solo piano performance, in tune with his pursuit of a career as a recitalist steeped in but hardly confined to the classical music cannon.

“ I want to figure out what’s next in classical music,” he said.

Read more about Max Hammond

Rohan Krishnan

Supporting refugee students as they navigated their new lives in New Haven was just one of many ways Rohan Krishnan made global connections during his time at Yale.

He came to campus committed to helping young migrants succeed academically — a cause he became passionate about while performing community service as a high school student. Beyond that, he was simply eager to experience as many new people, subjects, and activities as time would allow, thinking “the sky’s the limit.”

Still, he was amazed by the multitude of Yale’s offerings.

“ Never would I have imagined that I would meet world leaders, take courses with several former U.S. ambassadors, or tackle a real-world foreign policy issue,” said Krishnan, who majored in global affairs.

Read more about Rohan Krishnan

Alyssa Michel

Before arriving at Yale four years ago, Alyssa Michel imagined that college life would be something like an extension of her high school years. An engaged student in high school, involved in 20 or so clubs, she expected her time at Yale would involve similar pursuits.

“ I thought that if I swam in high school, then I would swim in college. Or if I did journalism in high school, I would have to do journalism in college,” said Michel. “What I didn’t expect is that you can do whatever you want. And, especially at a place like Yale, when you do something, it becomes your baby, and you invest a lot of time into it.”

This realization opened up many new worlds to Michel — including podcasting, gardening, teaching,  and even launching a business inspired by her grandmother’s traditional sorrel recipe.

Read more about Alyssa Michel

Grace Miller

As a Yale sophomore, Grace Miller began working as a student tutor in the Department of Economics. Helping her peers grasp the principles and intricacies of macroeconomics made her realize she that has a passion for education.

A year later, she began working as an intern three days a week at Common Ground High School, a public charter school in New Haven, serving as an aide in an outdoor leadership class and freshman algebra, chaperoning overnight camping trips, and running an afterschool program. And for her senior thesis, the economics major, who also completed the Education Studies Scholars Intensive Certificate, conducted an analysis of the effects of public school district mergers in Vermont.

Next year, she will teach in Chattanooga, Tennessee’s public school system.

Read more about Grace Miller

John Nguyen

John Nguyen entered Yale with plans to become a neurosurgeon. Instead, he found his calling as a writer.

As a high school student in St. Paul, Minnesota, he had been laser-focused on STEM subjects. But after taking some English classes at Yale he discovered a new love for reading and writing — with the support of a community of humanists that included a Nobel Prize-winning poet.  

He decided to major in English and developed such a fondness for poetry that he started writing poems on his own. Last year Nguyen was selected to represent Yale in the annual Connecticut Poetry Circuit, a statewide competition of college student poets.

“ I’d never encountered such community in any other space.”

Read more about John Nguyen

Mirabel Nguyen

Mirabel Nguyen came to Yale having never visited the campus. In fact, she’d barely seen any photographs before arriving in New Haven for the first time. But when selecting a college, she was determined not to be swayed by beautiful scenery anyway; she wanted to choose a school solely on its academic merits.

Nguyen was determined to excel academically and busy herself with extracurricular activities.  But after finally arriving at Yale, she began to develop more holistic goals.

“ I learned that it’s so much more than academic and extracurricular excellence that matters to me,” Nguyen said. “It’s the relationships that I’ve been building with people that show me how to be a better person.”

Read more about Mirabel Nguyen

Olivia Sally

If you had asked Olivia Sally a year ago where she’d be after graduating from Yale, she would have answered that she would be in law school. But a conversation with Mira Debs last spring changed everything.

Sally had just dipped into the icy waters of the Madison Surf Club as part of a polar plunge event at the invitation of her teacher, Debs, executive director of Yale's Education Studies Program. While chatting over post-plunge tacos, Debs mentioned the life-changing experience she had as a Rhodes Scholar, and asked Sally whether she’d considered applying for a similar fellowship.

After eventually applying for, and receiving, a Marshall Scholarship, Sally decided to pursue a Master of Education and Master of Public Policy during two years in the UK, the first member of the Yale Education Studies Program.

Read more about Olivia Sally

Barkotel Zemenu

Barkotel Zemenu is a believer in the revelatory nature of relationships.

At the most fundamental level, he says, relationships between unseen particles form the scaffolding upon which the universe flourishes; on a more human level, relationships between people — especially when speaking a common language — make the experience of living deeply fulfilling.

Zemenu immersed himself in both kinds of relationships at Yale. He excelled in physics, focusing largely on research and development for a detector that may one day prove the existence of a theorized nuclear process called neutrinoless double beta decay, spoke at scientific conferences nationally and internationally, and conducted research on multiple continents.

In a way, however, Zemenu says his treasures lie elsewhere.  “I did not expect college to be the place where academics stopped being my life’s top priority,” he said. “It was a place where I would find far more surpassing joy in the depth of my relationships, living with a fundamental others-centeredness.”

Read more about Barkotel Zemenu

Commencement 2024: A celebration of community

• Eleven graduating seniors honored with top Yale College prizes
• Yale awards nine honorary degrees

Celebrating the Yale MD Class of 2024

On Science Hill, a greener fuel powers a groundbreaking transformation

Yale School of Management celebrates and congratulates the Class of 2024

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