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This article is part of the supplement: Third Annual MCBIOS Conference. Bioinformatics: A Calculated Discovery

Open Access Open Badges Proceedings

GOFFA: Gene Ontology For Functional Analysis – A FDA Gene Ontology Tool for Analysis of Genomic and Proteomic Data

Hongmei Sun1, Hong Fang1, Tao Chen2, Roger Perkins1 and Weida Tong2*

Author affiliations

1 Z-tech Corporation, 3900 NCTR Road, Jefferson, Arkansas, 72079 USA

2 National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas, 72079 USA

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Citation and License

BMC Bioinformatics 2006, 7(Suppl 2):S23  doi:10.1186/1471-2105-7-S2-S23

Published: 26 September 2006



Gene Ontology (GO) characterizes and categorizes the functions of genes and their products according to biological processes, molecular functions and cellular components, facilitating interpretation of data from high-throughput genomics and proteomics technologies. The most effective use of GO information is achieved when its rich and hierarchical complexity is retained and the information is distilled to the biological functions that are most germane to the phenomenon being investigated.


Here we present a FDA GO tool named Gene Ontology for Functional Analysis (GOFFA). GOFFA first ranks GO terms in the order of prevalence for a list of selected genes or proteins, and then it allows the user to interactively select GO terms according to their significance and specific biological complexity within the hierarchical structure. GOFFA provides five interactive functions (Tree view, Terms View, Genes View, GO Path and GO TreePrune) to analyze the GO data. Among the five functions, GO Path and GO TreePrune are unique. The GO Path simultaneously displays the ranks that order GOFFA Tree Paths based on statistical analysis. The GO TreePrune provides a visual display of a reduced GO term set based on a user's statistical cut-offs. Therefore, the GOFFA visual display can provide an intuitive depiction of the most likely relevant biological functions.


With GOFFA, the user can dynamically interact with the GO data to interpret gene expression results in the context of biological plausibility, which can lead to new discoveries or identify new hypotheses.


GOFFA is available through ArrayTrack software webcite.