AgBase: a unified resource for functional analysis in agriculture

Abstract

Analysis of functional genomics (transcriptomics and proteomics) datasets is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation. To facilitate systems biology in these species we have established the curated, web-accessible, public resource ‘AgBase’ (). We have improved the structural annotation of agriculturally important genomes by experimentally confirming the in vivo expression of electronically predicted proteins and by proteogenomic mapping. Proteogenomic data are available from the AgBase proteogenomics link. We contribute Gene Ontology (GO) annotations and we provide a two tier system of GO annotations for users. The ‘GO Consortium’ gene association file contains the most rigorous GO annotations based solely on experimental data. The ‘Community’ gene association file contains GO annotations based on expert community knowledge (annotations based directly from author statements and submitted annotations from the community) and annotations for predicted proteins. We have developed two tools for proteomics analysis and these are freely available on request. A suite of tools for analyzing functional genomics datasets using the GO is available online at the AgBase site. We encourage and publicly acknowledge GO annotations from researchers and provide an online mechanism for agricultural researchers to submit requests for GO annotations.

Proteome and phosphoproteome analysis of chromatin associated proteins in rice (Oryza sativa)

“…Cellular component, which describes where the gene product can be found, was used in this study. GO annotations were obtained from GORetriever, a program available at AgBase [33] (http://www.agbase.msstate.edu/). Proteins without annotations at AgBase were searched in other databases, including NCBI, UniProt, and Gramene.…”

“…To help understand the distribution and function of the chromatin associated proteins, we obtained GO annotations of the identified proteins from the AgBase [33] (http:// www.agbase.msstate.edu/) and other databases such as NCBI, UniProt, Gramene and conducted GO analyses. As shown in Fig.…”

Redundancy Control in Pathway Databases (ReCiPa): An Application for Improving Gene-Set Enrichment Analysis in Omics Studies and “Big Data” Biology

“…Most of the current methodological research in this area has focused on the development of statistical and bioinformatics tools for the selection and prioritization of pathways of interest. The presence of redundancies in gene-sets is, however, a well-recognized fact and earlier attempts have been made to reduce such redundancies by either limiting the information content [as in GO Slim versions of Gene Ontology, which contain only a subset of the terms of the full ontology (McCarthy et al, 2007)], or by grouping and displaying statistically validated, overlapping functional annotations together [as in the DAVID analysis tool . In this aarticle, we present a complementary approach that preserves all available information in a pathway database but allows a restructuring of the database contents to reduce overlap prior to conducting gene-set enrichment analysis.…”

Standard genetic nomenclature.

“…A growing number of model organism and livestock animal species (including bovine) databases and working groups contribute annotation sets to the GO repository (McCarthy, 2007;Reese, 2010).…”

“…Continued efforts are made to improve the accuracy of the bovine GO annotations by transferring GO annotations from better annotated proteins in human and mouse based on Ensembl orthology. As of September 2012, GO annotation for bovine (McCarthy, 2007) are computationally derived (AgBase: see Table 24.1 for URL).…”