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
Skills:
The student is able to:
General competence:
The student is able to
The course is given as lectures and mandatory exercises
Lectures, 4 hours per week
Exercises, 2 hours per week
The forms of assessment are:
All compulsory assignments must be approved before examination.
This course is administered by the Department of Informatics.
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