The Computational Biology Group (CBG) develops concepts and algorithmic tools for the analysis of large-scale biological data. The focus is on the integration of genotypic and phenotypic datasets from mammalian cells or clinical studies.
Microarrays are firmly established as a standard tool in biological and biomedical research. This high-throughput technology was first used to study genomic gene expression, but is now also employed for measuring genotypic variability, including single nucleotide polymorphisms (SNPs) and copy number variations (CNVs). Biological studies frequently probe cells under a variety of experimental conditions (temperature, pH, mutations, etc.), while clinical studies usually provide additional phenotypic information (state of disease, type of treatment, blood chemistry of patient, etc.).
The CBG goal is to process the different types of data in order to reduce their complexity. This is done by grouping fundamental elements (genes, samples, treatments) that share certain similarities into so-called “modules”. This helps to make the huge amounts of data more amenable for analysis and may reveal the organization of, and relation between, the modules and their elements.
Such a modular approach is useful for each large dataset by itself: analysis of massive amounts of gene expression data reveals “transcription modules” containing genes that are co-expressed in certain samples. Similarly, large amounts of clinical data can be processed into “phenotypic modules” merging clinical variables that behave similarly across certain groups of individuals. Finally, the group is exploring how to extend this modular approach for the joint analysis of different datasets. For example, one may identify a set of genes that is co-expressed in a group of cell-lines all exhibiting a similar sensitivity to a collection of treatments. Unsupervised identification of such “co-modules” provides testable hypotheses, such as candidate genes for the cellular response to certain drug treatments.
Projects and Services
Global data analysis of phenotypic and genotypic data from the CoLaus study
In the CoLaus study, conducted in collaboration withDr. Murielle Bochud and Prof. Peter Vollenweider (CHUV), around 500 clinical parameters were collected for more than 6,000 individuals, who were also genotyped using 500k SNP arrays. A comprehensive genome-wide association study is being performed using both standard and newly developed tools for an integrated analysis of the data generated.
Pharmacogenetic analysis of lipid-response data from genotyped HIV patients treated with multiple antiviral drugs
The Swiss HIV cohort study involves longitudinal lipid measurements from more than 400 HIV genotyped patients receiving an antiviral drug cocktail of 18 drugs. The analysis aims to identify gene-drug interactions with the ultimate goal of developing predictive algorithms that can help doctors to avoid certain drugs combinations that can cause side-effects in patients with a particular genetic profile. This study is carried out in collaboration with Prof. Amalio Telenti (Institute for Medical Microbiology, UNIL).
Integrating microRNA sequence motif analysis with global gene expression data
The sequence of a microRNA is indicative of its gene targets. The CBG integrates massive amounts of expression data for both microRNAs and mRNAs from a variety of healthy and cancerous tissues to complement motif-based target prediction. The analysis is also aimed at a better understanding of the network generated by microRNA-mediated gene regulation.
Pharmacogenetic analysis of the cardiovascular response in mice
In collaboration with Dr. Fabienne Maurer (CHUV), more than 20 genotyped inbred mouse strains are examined to identify naturally occurring genetic variations influencing cardiovascular responses to common β-adrenergic and β-blocking drugs in order to identify polymorphisms in susceptibility genes. The group’s modular analysis tools as well as standard genetic approaches are applied to the data generated by this experimental study.
Websites for Further Information
Computational Biology Group http://serverdgm.unil.ch/bergmann/