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Institute for Machine Learning
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Genome Analysis & Transcriptomics (2KV)

Course no.: 365.109
Lecturer: Alois Regl (alois.regl@regl.net, +43 664 4502030)

It is deeply recommended to hear both lectures (Structural Bioinformatics and Genome Analysis & Transcriptomics).

Lecture notes:

There are no lecture notes but all slides will be provided via KUSSS.

Please pay attention to the Big Picture provided in KUSSS!!!

Motivation and course outline

Bioinformatics is an interdisciplinary field at the interface of life sciences and computational sciences that deals with the development and application of methods for storing, retrieving, and, in particular, analyzing biological data. The massive data amounts produced by recent and currently emerging high-throughput biotechnologies provide unprecedented potentials, but also pose yet unseen computational challenges – making bioinformatics an essential success factor for the advancement of fields, such as, molecular biology, genetics, medicine, and pharmacology.

The goal of this course is to provide an overview of foundational and computational aspects of genetic variation and gene expression. The first part is mainly concerned with genetic commonalities and differences between individuals, how these commonalities and differences emerge, and how they can be associated to diseases and other traits. The second part is concerned with the dynamics of genes, how they are organized, how they can be detected, how the activation of genes is controlled, and how gene expression can be measured and analyzed computationally.

 

Genome Analysis:

  • Single nucleotide variants/polymorphisms
  • Copy number variations and aberrations
  • Genotype-phenotype association studies
  • Linkage disequilibrium
  • Haplotype blocks
  • Identity by descent
  • Phasing and genotype imputation
  • Population genetics models

Transcriptomics:

  • Gene expression
  • Microarrays
  • Next generation sequencing, read mapping, RNA-seq
  • Gene and promoter detection
  • Detection of splice sites and alternative splice sites
  • Epigenomics
  • Pathways and gene modules