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

This list will grow over the coming months. If you would like to become an early adopter and receive core services at reduced or no cost, please fill out a consult request form and email Stephen Turner for more information.