Plant & Animal Genomes XV Conference

January 13-17, 2007
Town & Country Convention Center
San Diego, CA

AgBase: Enabling Functional Genomics Analysis In Ruminants.

Fiona M McCarthy*^1,3 , Nan Wang*^2,3 , G. Bryce Magee^2,3 , Teresia J Buza^1,3 , Divyaswetha Peddinti^1,3 , Shane C Burgess**^1,3,4 , Susan M Bridges**^2,3

¹  Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, PO Box 6100, Mississippi State, MS, 39762

²  Department of Computer Science and Engineering, Bagley College of Engineering, Mississippi State University

³  Institute for Digital Biology, Mississippi State University, MS 39762, USA

⁴  Mississippi Agricultural and Forestry Experiment Station, Mississippi State University, MS 39762, USA

* These authors contributed equally to this work.
** These authors contributed equally to this work.
Analysis of functional genomics (transcriptomics and proteomics) datasets is hindered in agricultural species because agricultural sequences have poor structural and functional annotation. To facilitate analysis of functional genomics datasets we have established the curated, web-accessible, public resource “AgBase” (www.agbase.msstate.edu). We have improved the structural annotation of the bovine genome by experimentally confirming the in vivo expression of electronically predicted proteins and by proteogenomic mapping. The de facto standard for functional annotation is Gene Ontology (GO) and we have developed a two-tier system of GO annotations to allow users to submit their own GO annotations or request GO annotation of genes of interest to them. The AgBase download page provides a “GO Consortium” gene association file containing only fully quality-checked annotations that are submitted to the central GO database and a more comprehensive “Community” gene association file containing additional GO annotations including GO annotations for electronically predicted proteins, comprehensive annotations based on sequence homology and annotations from community researchers that have not yet been quality checked. This system gives researchers the initial breadth required for functional modeling, leading to experiments that test the function of these gene products, which leads to higher quality GO annotations. We envision that as the overall annotation quality improves, the GO annotations in the community gene association file will be superseded. By improving structural annotations, adding GO functional annotations, enabling researchers to request additional GO annotations and providing tools for using the GO we assist researchers who wish to model function in their datasets.