Integrative Bioinformatics (BIOINF 4220 - 3 ECTS)

Overview

Many biological research questions are centered around trying to understand how small changes in the organisms genome and environment can result in major changes in the cellular and organismal phenotype. Examples for such changes in phenotype can be different body height/weight, pathogenisis, or altered metabolic rates. To gain insights into the mechanism regulating how an organism functions, ideally the full underlying biological system needs to be understood and modeled. To do so, available measurements of the different layers in the central dogma (transcription, translation) and further cellular function (metabolism) have to be integrated.

This course thus deals with one of the central tasks of bioinformatics: integration and unification of biological data from different sources. Specifically, we well work with data sets for genome, transcriptome, proteome and phenome measurements and try to coax out mechanistic insights into the correlation between the genome and downstream processes (altered gene expression and altered metabolism).

Goals

• Data Integration using Python (and relevant packages)
• Genotype-Phenotype centered analyses (GWAS & PheWAS)
• Differential gene expression and pathway analysis
• Machine Learning with biological data

Requirements

• Master in Bioinformatics (or at least the lectures Sequence Bioinformatics and Structure and Systems Bioinformatics)
• Proficient in a scripting language, preferably Python and/or willingness to pick the necessary skills prior (!!!) to the course

Evaluation

• To pass the practical course a report will have to be handed in. The grade is based on this report and your performance during the course. Passing requires an overall grade of 4.0 or better.

Preparation

• Clone this forked repo on your machine locally.
• Install conda on your machine.
• Open the terminal and navigate to the local directory.
• Create a conda environment and install the required packages. You can do this in one step by typing conda env create -f environment.yml.
• Active the conda environment with conda activate pibi25.
• Open jupyter lab with jupyter lab.