Network-based functional analysis in transcriptomics

Abstract

A standard bioinformatics analysis of ’omics data will produce a list of molecules following statistical analysis. In the context of transcriptomics, these molecules are genes or transcripts and the statistical approach used to identify them is mostly a differential expression analysis. Once genes have been identified as differentially expressed in an experiment, biologists are often interested in understanding their biological implications. This is done by understanding their functional role in the biological system being investigated. The role and function of many genes is known to some extent and this is an area of continued research. Knowledge on gene function is often encoded into knowledgebases such as the gene ontology and other pathway databases. Given these functional annotations, we are interested in identifying over-represented functions in our data. To do so, we use gene-set enrichment analysis, a group of methods designed to identify enriched functions represented by collections of genes known as gene-sets. These approaches often identify 100s-1000s of gene-sets/pathways that then need to be curated manually. To automate the process of condensing this knowledge, we developed vissE a tool to summarise, interpret, and visualise higher-order pathways/processes. It then provides a suite of modules to assess the functional roles in each higher-order pathway, thus providing biologists with a holistic view of the biological system they are investigating.

Dharmesh D Bhuva

Senior post-doctoral researcher at SAiGENCI

My research interests include cancer systems biology, spatial statistics and computational biology.