Genome-scale algorithms

The course provides an introduction to the technologies, methods, and algorithms used in genomics. High-thoughput sequencing technologies have revolutionized the field of genomics, allowing the reconstruction of genomes, epigenomes, and transcriptomes across entire populations and at the level of individual cells. These ¿omics¿ technologies provide unique insights into the core program of life but analyzing the resulting data poses significant bioinformatics challenges.

The course is divided into two parts. The first part gives an overview of modern long and short-read sequencing technologies and their applications, including capturing global biodiversity, meta-genomics, single-cell sequencing, epi-genetics, and genomic medicine.

The second part of the course focuses on state-of-the-art algorithms and data structures for processing and analyzing high-throughput sequencing data, including the use of machine learning methods for integrating and obtaining biological insights from large-scale omics data. These techniques include de Bruijn graphs and genome assemblers, the Burrows-Wheeler transform, suffix arrays for indexing the genome and detecting repeats, and methods for clustering and network reconstruction from time series, perturbational and population-based omics data. An introduction to the complexity of the presented algorithms and their comparison will be given.

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge:

The student can

• explain different sequencing technologies, their potential and limitations, and their applications
• choose and design approaches to process, analyze, and interpret genome, epigenome, and transcriptome sequencing data,
• choose and design approaches to collect, integrate, analyze, and interpret large datasets of high-throughput sequencing experiments, including single-cell data and multi-omics data from the same samples,
• choose, integrate, and apply appropriate tools from diverse bioinformatics and machine learning libraries.

Skills:

The student is able to:

• implement algorithms for the processing and analysis of high-throughput sequencing data, e.g. de-novo assembly
• implement algorithms for the integration and analysis of large datasets with samples from multiple high-throughput sequencing experiments, including clustering and network reconstruction from time series, perturbational and population-based omics data,
• apply bioinformatics tools on the Linux command-line,
• efficiently query sequence databases,
• implement their own scripts and programs using existing bioinformatics and machine learning libraries,
• interpret the results of bioinformatics and machine learning pipelines using functional annotation, pathway, and interaction databases,
• argue for the choice of specific algorithms and detect causes of failure

General competence:

The student is able to

• work on a high-throughput sequencing data analysis task on their own and in a small group,
• communicate their analysis and its biological interpretation to an interdisciplinary audience,
• select and present relevant new topics in the field of genomics from the published literature.