Integrated Bioinformatics

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 worked with data sets containing 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).

Topics covered in this course:

• Data integration using python and pandas
• Genotype - Phenotype centered analyses (GWAS and PheWAS) using scipy, statsmodels and PLINK
• Differential expression analysis using scipy and statsmodels
• Inference of biological networks based on gene expression data
• Combination of multiple analysis layers for systems biology-centered data analysis
• Data visaulisation in python using matplotlib and seaborn
• MachineLearning methods