Zur JKU Startseite
Institut für Machine Learning
Was ist das?

Institute, Schools und andere Einrichtungen oder Angebote haben einen Webauftritt mit eigenen Inhalten und Menüs.

Um die Navigation zu erleichtern, ist hier erkennbar, wo man sich gerade befindet.

Genome Analysis & Transcriptomics (2KV)

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

Due to the Covid situation the lecture will be held remotely (via videos, posted in moodle). The exam will be held in-class, if the virus allows it. Otherwise, it will be also remote. Things might change, however, possibly on short notice.

 

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.

 

Genomics (Genome assembly):

  • Sequencing technology
  • Assembly algorithms

Transcriptomics (Microarrays):

  • Microarray Basics
  • Quality Control
  • Preprocessing
  • Differential Expression
  • Downstream analysis