Pathway/Functional Analysis

The results of many “standard” bioinformatics analyses are usually lists of variants, transcripts, or genes and some statistic – e.g., a gene name, fold change, and multiple-testing-corrected P-value for a gene expression study; or a SNP, odds ratio, and p-value for an association to a disease phenotype from a genetic association study. Most of the time, these “gene lists” are derived from tests that examine a single genetic variant or over/under-expression of a single gene at a time between two conditions (case vs. control, wild type vs mutant).

However, the prevailing view is that complex phenotypes are not the result of a single gene but reflect abnormalities in the entire cellular network that links tissues and organ systems. A better understanding of how genetic variants, gene expression, DNA binding, and DNA methylation at multiple loci throughout the genome work together to influence the presentation of a complex phenotype may lead to discovery and characterization of unknown biological processes.

This is the basis for “pathway analysis” or functional annotation – putting lists of genes into biological context. This is an incomplete list of these kinds of analysis the core can assist with.

Functional annotation

  • Gene ontology
  • KEGG (pathways)

Overrepresentation analysis

  • Gene ontology
  • KEGG (pathways)

Ingenuity Pathway Analysis

  • Canonical pathways
  • Biological networks
  • Upstream transcription factors that influence your gene’s expression