Bioinformatics

Programming techniques with Python in the context of biological problems:

• Variables, data types, operators, control structures, functions
• Sequential data types, assignments and sets
• Concepts of object-oriented programming and modularization
• IO functionalities locally and in the network

Runtime environment with compiler and Google Colab

Blended learning

Continuous assessment:

Exercise quiz, midterm test and written exam

Central algorithmic paradigms (brute force, branch and bound, divide and conquer, greedy, .... ) are discussed on the basis of classical and current bioinformatics problems. Furthermore, probability theory and graph theory approaches as well as some aspects of theoretical computer science (complexity theory) are presented.

Blended learning

Final exam: Written examination and assessment of the practical tasks

Basics in R for Data Science

• Introduction to R for data analysis
• Introduction to data science and relevant methods
• Overview of available R packages
• Overview of various analytical methods up to big data methods
• Insight into visualizations
• Advantages and disadvantages of visualizations and methods

Blended learning

Continuous assessment: Active participation, exercises and presentations are assessed.

This lecture introduces the use of a shell.

• Differences in the shell between different operating systems are discussed.
• A working environment is set up for further courses.
• Basic Unix shell commands and possibilities are taught.
• The connection and handling of Linux servers via command line tools is explained and practiced.
• Students learn to write their own shell scripts.

Blended learning

Continuous assessment: Exercises and written test

Descriptive, inductive and explorative statistics with a focus on explorative statistics, hypothesis generation and testing, multiple testing problem; introduction to R, basic handling, simple examples. The statistical software "R" will be used (freeware).

Blended learning

Continuous assessment: Exercise examples, cooperation, written exam

Part 1: Proteomics basics, analysis of by mass spectrometry (MS) with focus on bottom-up shotgun MS data analysis:

• Identification of peptides & quantification,
• Databases, public repositories,
• quality control,
• applications and statistical analysis

Part 2: Metabolomics basics:

• Fields of application for metabolomics
• Analytical workflows
• Metabolites and their analysis methods
• Targeted and non-targeted analysis
• Tools for data analysis

Blended learning

Final exam: written exam

In the wet lab DNA will be extracted, and sequenced. The resulting data will be analysed and visualized.

Flipped classroom, with laboratory exercise and subsequent analysis project

Continuous assessment: Protocol of analysis results

Theoretical foundations of genome and transcriptome analysis:

• Introduction to the topics of genomics and transcriptomics, including the generation of data.
• Software solutions and algorithms in the field of genomics and transcriptomics

Practical analysis examples from genomics and transcriptomics. Application of appropriate bioinformatics tools for

• Quality control and processing of sequencing data
• Assembling genomes and transcriptomes.
• Mapping (aligning) of sequence data
• Specialized examples from the subject area (e.g. scRNA-Seq, ChIP-Seq, etc.)

Blended learning, flipped classroom

Continuous assessment: Practical exercises and written test.

Current topics in bioinformatics are discussed using examples from research and industry.

Graduates will talk about their careers and the experiences they have had since graduating.

An overview of the current development of the field will be given, as well as the requirements and expectations placed on graduates.

Students will be introduced to possible topics for their Master's thesis.

Seminar series with lectures and discussions

Continuous assessment: Written report and discussions

Brief introduction to the basics of epigenetics

• Mechanisms of epigenetic regulation: DNA methylation, histone modifications, chromatin structure, and non-coding RNAs.
• Differences between genetic and epigenetic modifications.

Epigenetic data types and experimental methods for generating epigenetic data

• DNA methylation data (e.g. from bisulfite sequencing), histone modification data (e.g. from ChIP-Seq), chromatin accessibility data (e.g. ATAC-Seq, DNase-Seq), transcriptional effects of non-coding RNAs (e.g. miRNAs, lncRNAs).
• Overview of specific sequencing technologies (e.g. WGBS, RRBS), methods for ChIP-Seq and ATAC-Seq.

Bioinformatic analysis of epigenetic data

• Identification of differentially methylated regions (DMRs) and comparison between samples.
• Recognition of histone modification patterns and their biological interpretation.
• Analysis of chromatin accessibility and its relationship to gene expression.
• Integration of multi-omics data (epigenetics, transcriptomics, proteomics).
• Introduction to common bioinformatics tools

Databases

• Overview of relevant databases (e.g. ENCODE, GEO, Roadmap Epigenomics).

Application examples with interpretation and visualization

• Visualization of epigenetic data (e.g. heatmaps, genome browser).
• Interpretation of the results in a biological and clinical context.

• Epigenetic biomarkers in medicine (e.g. cancer diagnostics).
• Role of epigenetics in development and environmental response.

Blended learning

Continuous assessment: Protocol and presentation

This lecture gives an introduction to basic principles of protein and RNA structure using top-down and bottom- up approaches for structure predictions.

• Introduction of experimental structure determination methods of biopolymers
• In silicon prediction of RNA structure
• Genome wide screens of RNA structure
• Introduction of basic structural principles of proteins
• In silicon prediction of protein secondary and tertiary structure with physics based, statistical and deep learning approaches
• Benchmarking of structural prediction tools including new deep learning models.
• Visualizations of RNA and protein structures

Molecular Design:

• from small molecule descriptions to Protein - Ligand and RNA - Ligand complexes, as well as Protein RNA interactions
• principles of structure-based drug discovery
• principles of structure and clustering methods for small molecules
• experimental and in silico methods for fragment screening
• ncRNA in human diseases
• pharmacophore models
• disease networks
• molecular docking
• drug repurposing

Blended learning

Continuous assessment: Exercises and a final project

Fundamentals of machine learning and AI

• Introduction to ML algorithms: Supervised, unsupervised and reinforcement learning.
• Comparison of ML and traditional statistical approaches.
• AI concepts and current methods.

Core algorithms and biological application examples

• Classification: e.g. predicting disease risk from genomic data.
• Clustering: e.g. discovery of cell types in single cell data (e.g. scRNA-Seq).
• Regression: e.g. modeling of gene expression based on epigenetic profiles.

Deep learning for complex data

• Introduction to neural networks and convolutional neural networks (CNNs) for image analysis (e.g. microscopy data).
• Use of Recurrent Neural Networks (RNNs) e.g. for sequence analysis.

Tools and frameworks

• Bioinformatics tools and libraries in Python
• Specialized platforms such as DeepVariant or AlphaFold.

Challenges and ethical aspects

• Interpretability of ML models in biology.
• Dealing with bias and uncertainties in biological data.
• Data protection and ethical implications in health research.

Blended learning

Continuous assessment: Exercise examples and a project at the end

Data handling and preparation

• Loading and saving data in various formats (CSV, Excel, JSON).
• Pre-processing of biomedical data: Cleansing, normalization, and transformation.

Working with biomedical libraries

• Use of pandas for data frame operations.
• Visualization of data with matplotlib and seaborn.
• Analyzing biomedical sequence data with Biopython.

Processing large data sets

• Working with large biomedical datasets using Dask and SQL.
• Basics of parallelization and increasing efficiency.

Examples from bioinformatics-specific applications

• e.g. analysis of genome, proteome and transcriptome data.
• e.g. visualization of networks and biological relationships (e.g. with NetworkX).

Blended learning

Continuous assessment: Submission of programming exercises

The course deals with general topics in statistics:

Introduction to statistical models and their evaluation:?

• Critical evaluation of the significance of statistical results.

Application of statistical software:

• Application of R/Python for bioinformatic data analysis and modeling as well as the use of special libraries.

Statistical methods for data integration

• Merging and harmonization of heterogeneous data sets (e.g. genome, proteome and metabolome data).
• Meta-analysis and pooling strategies for biological studies.
• Dealing with missing data: Imputation techniques.

Blended learning

Continuous assessment: Exercises and examination

Basics of cloud computing:

• Concepts and service models as well as resource management.
• Comparison of cloud architectures.

Technologies and platforms:

• Use of common cloud platforms.
• Automation and orchestration.
• Data management and storage solutions in the cloud.

Security and scaling:

• Cloud security principles (data encryption, access management).
• Scaling strategies for applications and services (auto-scaling, load balancing).
• Cost management and efficiency in cloud environments.

Blended learning

Continuous assessment: Practical exercises and final project as well as written exam

Basics of containerization and virtualization:

• Introduction to container technologies (e.g. Docker).
• Creation, management and optimization of containers for bioinformatics applications.
• Comparison of containers and virtual machines in the context of bioinformatics
• Development of reproducible and portable bioinformatics pipelines.

Basics of version control and collaboration:

• Introduction to versioning and methods of teamwork in IT
• Introduction to Git and GitLab, including the automation of processes with Git hooks and GitLab CI/CD.

Blended learning

Continuous assessment: Practical exercises and final project as well as written exam

Introduction to automation:

Introduction to the need and purpose of automation in bioinformatics.

Workflow systems:

GNU-Make (understanding the basics of automation in bioinformatics).

Exploration of modern workflow management systems used in the field of bioinformatics. e.g. Galaxy, Nextflow, Snakemake

Practical exercises by creating scripts for automation.

Blended learning

Continuous assessment: Practical exercises and final project as well as written exam

Application of the theoretical content of the course, working with databases and operating databases

• Fundamentals and architectures of database systems
• Transaction concept and SQL core
• Entity Relationship (ER) model
• Relational model
• Relational database design
• Database implementation with SQL-DDL
• Practical design task in the small group

Flipped classroom, blended learning

Continuous assessment: Presentations and collaboration as well as submission of a practical project task

Software development with Python, the planning and implementation of software projects is discussed.

The following areas are covered using practical examples:
- Using strings and container classes
- Data modeling with classes
- Creating Graphical User Interfaces (GUI) with QT

Non-language-related focal points are:
- Understanding project planning and the development cycle
- Efficient development process for small projects
- Clear programming style

Blended learning

Continuous assessment: Exercises and practical work at the end of the lecture

Students learn to use commandline tools to script bioinformatic analyses in order to detect genetic abnormalities.

Requirements for software and data analysis in clinical studies. Analyses that are relevant in the context of personalized medicine (e.g. genetic markers) and their requirements for data management and data security.

Blended learning

Continuous assessment: Exercises and practical final project

Introduction to biomarkers and their role:

• Definition and classification of biomarkers (diagnostic, prognostic, predictive).
• Applications of biomarkers in personalized medicine and disease monitoring.
• Examples of biomarkers in genomics, proteomics and metabolomics.

Data analysis and validation of biomarkers:

• Use of bioinformatics methods to identify and analyze potential biomarkers.
• Statistical and machine learning approaches for predictive modeling.
• Validation strategies: robustness, reproducibility and clinical relevance.

Applications in predictive medicine:

• Integration of multi-omics data to predict disease risk and treatment response.
• Development of predictive models for individual treatment strategies.
• Challenges and ethical aspects of biomarker use in medicine.

Blended learning

Continuous assessment: Exercises during the lecture and exam

Part 1: Teaching the essential know-how on the subject of "Software as a medical device":

• What is a medical device, what is its legal basis, how is it defined and delimited? When can software be a medical device?
• What phases are there in the development of a medical device and what needs to be considered in this context? Quality assurance measures and current requirements from changing legislation (MDR - Medical Device Regulation). A further focus in the above context is on the validation of software as a medical device, which also covers clinical testing.

Part 2: Legal basics regarding the integration of AI algorithms in medical software (AI Act) and the implementation of the DSVG in bioinformatic software solutions.

Blended learning

Continuous assessment: Exercises and presentations

Basics of IT project management:

• Introduction to project management methods (e.g. waterfall, agile, hybrid).
• Project life cycle: initiation, planning, implementation, monitoring and completion.
• Roles and responsibilities in IT projects (e.g. project manager, scrum master).

Tools and techniques:

• Use of project management tools.
• Resource, time and cost management in IT projects.
• Risk management and change management in the IT environment.

Team and communication management:

• Promoting collaboration and efficient communication in interdisciplinary teams.
• Conflict resolution and stakeholder management.
• Success criteria and evaluation of IT projects.

Examples from the medical/scientific environment.

Blended learning

Continuous assessment: Exercises and practical project

Introduction to academic writing and writing the topic paper.

• Correct citation style
• Writing workshop

Blended learning

Final exam: Master's thesis proposal (thematic paper), practical project

The content of this course is the writing of a Master's thesis.

Many students develop Master's thesis topics, conduct research and write their Master's thesis in cooperation with or at the suggestion of companies. Around 20% find a topic with the help of their lecturers or supervisors.

The Master's thesis must be written in consultation with the UAS supervisor and after approval by the head of the degree program.

Continuous assessment: Assessment of the Master's thesis according to a standardized catalog.

• Instructions and presentation of the Master's thesis with discussion on the planning, design, implementation, documentation and presentation of scientific or engineering work.
• Practice of the presentation to be given as part of the Master's examination
• What is particularly important when presenting a scientific paper?
• Receiving & giving feedback.

Presentations, discussion of a scientific article

Continuous assessment: Assessment of the presentation (structure, design of the slides, presentation style, adherence to time limits)

Examination interview on study-relevant content.

Self-study

Final exam: Presentation of the Master's thesis, discussion, examination interview