The facebook of genes: community detection in biological networks

Background: Various genomic data have been used to construct biological networks. It is well known that such networks have a high degree of modularity, and that the corresponding modules often comprise genes or proteins that are involved in the same biological functions. Network module identification methods — also known as community detection and graph clustering methods — attempt to reveal these functional units, which is key to derive biological insights from genomic networks.

Goal: The aim of this project is to explore methods that leverage biological networks to predict groups of functionally related genes (modules). We will explore different module identification and visualization methods. We will then analyze whether the identified modules are novel or correspond to known groups of functionally related genes using information from the gene ontology and pathway databases.

Computational tools: This project has a computational flavor and is best suited for students with interest in programming. We will apply standard tools used in many diverse problems in computational biology (e.g., clustering, gene ontology enrichment analysis, permutation tests, etc). This project will be implemented using R.

Keywords: Network biology, pathway analysis

Supervisor: Daniel Marbach

Download project introduction slides

Our wiki : The-facebook-of-genes.pdf