Module MA4667-KP05

Duration

1 Semester

Turnus of offer

irregularly in the summer semester

Credit points

5

Course of studies, specific fields and terms:

• Master CLS 2016, optional subject, mathematics
• Master CLS 2023, optional subject, mathematics
• Master MES 2020, optional subject, mathematics / natural sciences

Classes and lectures:

• Advanced Algorithmic and Mathematical Concepts for Molecular Biology (exercise, 1 SWS)
• Advanced Algorithmic and Mathematical Concepts for Molecular Biology (lecture, 2 SWS)

Workload:

• 20 hours exam preparation
• 85 hours private studies and exercises
• 45 hours in-classroom work

Contents of teaching:

• Reproducible research and data management: FAIR-Data, data management plans, public repositories, workflow-management-systems, software-containerisation and concepts of high-performance computing (parallelisation, scheduling, job-monitoring).
• Data types relevant in bioinformatics and biotechnology: Knowledge of data generation, formats and properties of different biological data, including DNA-/RNA-sequences, protein structures, SNP-genotyping and expression arrays.
• Sequence analysis: Introduction to representations of biological sequences, application of string-matching-algorithms, alignment and mapping algorithms as well as approaches for multiple alignment.
• Protein Structure Analysis: Representation of molecular structures and mathematical approaches to predict and compare structures.
• Genome and transcriptome assembly: Methods for the construction of genomes and transcriptomes by means of approaches like overlap consensus and De-Bruijn-graphs as well as genome graphs and pangenomes.
• Genetic variant analysis: Identification and genotyping of short and long genetic variants by genome alignment, measurement of sequence conservation as well as variant annotation and coordination transfer between genomes.
• Functional genomics and epigenomics: Analysis of functional and epigenetic marking in genomes, measurement of nucleosome positioning and of the open chromatin, ChIP-seq-analysis for transcription factors and histone marking as well as segmentation of the genome.
• Mathematical and statistical methods in bioinformatics: Application of Hidden-Markov-Modellen (HMMs) for gene prediction and genome segmentation as well as application of statistical methods for transcriptome analysis, including counting, significance tests, normalisation, batch correction and control of false discoveries.
• Advanced machine learning for biological data: Modern techniques of machine learning like nucleotide and protein language models, regulatory sequence models, convolutional neural networks (CNNs) and transformer-models.
• Advanced OMICS-data analysis and integration: Single-cell-(co-)assays and spatial transcriptomics, high-dimensional and sparse/missing data as well as application of methods for combination of different OMICS-layers.

Qualification-goals/Competencies:

• The student learn methods for reproducible data analysis and effective research data management
• The students have knowledge of basic bioinformatics data types and their original technologies
• The students learn computational and mathematical foundations for the analysis of biological sequences and structures
• The students acquire competency in genome and transcriptome assembly
• The students learn how to identify and analyse genetic variants
• The students learn concepts and methods of functional genomics and epigenomics
• The students learn to apply mathematical models and statistical approaches in bioinformatics
• The students have knowledge of advanced machine learning techniques for the analysis of biological data
• The students have comprehension of the challenges in OMICS-Data analysis and their integration strategies

Grading through:

• Viva Voce or test

Responsible for this module:

• Prof. Dr. Cornelia Pokalyuk

Teacher:

• Institute for Mathematics
• Prof. Dr. Cornelia Pokalyuk
• Prof. Dr. Inken Wohlers
• Prof. Dr. rer. nat. Martin Kircher

Literature:

• Durbin et al. : Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids 1998
• Compeau & Pevzner : Bioinformatics Algorithms: An Active Learning Approach 3rd Ed., 2018 /li>
• Holmes & Huber : Modern Statistics for Modern Biology 2019 /li>
• Altuna Akalin : Computational Genomics with R 2020 /li>
• Muhammad Nabeel Asim ,Sheraz Ahmed, Andreas Dengel : Artificial Intelligence for Molecular Biology: Volume II 2025
• Vince Buffalo : Bioinformatics Data Skills 2015

Language:

• English

Notes:

Last Updated:

14.04.2026