GOAL : A software tool for assessing biological significance of genes groups

Background: Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then used to group together genes based on their similarity in expression profiles. Genes in each group are probably functionally related. The functional relevance among the genes in each group is usually characterized by utilizing available biological knowledge in public databases such as Gene Ontology (GO), KEGG pathways, association between a transcription factor (TF) and its target genes, and/or gene networks. Results: We developed GOAL: Gene Ontology AnaLyzer, a software tool specifically designed for the functional evaluation of gene groups. GOAL implements and supports efficient and statistically rigorous functional interpretations of gene groups through its integration with available GO, TF-gene association data, and association with KEGG pathways. In order to facilitate more specific functional characterization of a gene group, we implement three GO-tree search strategies rather than one as in most existing GO analysis tools. Furthermore, GOAL offers flexibility in deployment. It can be used as a standalone tool, a plug-in to other computational biology tools, or a web server application. Conclusion: We developed a functional evaluation software tool, GOAL, to perform functional characterization of a gene group. GOAL offers three GO-tree search strategies and combines its strength in function integration, portability and visualization, and its flexibility in deployment. Furthermore, GOAL can be used to evaluate and compare gene groups as the output from computational biology tools such as clustering algorithms.Des techniques exp\ue9rimentales modernes \ue0 haut d\ue9bit comme les puces \ue0 ADN donnent souvent lieu \ue0 de longues listes de g\ue8nes. On a alors recours \ue0 des outils bio-informatiques comme le regroupement (clustering) pour classer les g\ue8nes en fonction de leur similitude dans leur profil d\u2019expression. Les g\ue8nes de chaque groupe sont probablement li\ue9s de mani\ue8re fonctionnelle. La caract\ue9risation de la pertinence fonctionnelle parmi les g\ue8nes de chaque groupe s\u2019effectue habituellement en utilisant soit les connaissances biologiques accessibles dans des bases de donn\ue9es publiques comme la Gene Ontology (GO), soit la KEGG PATHWAY, soit l\u2019association entre un facteur de transcription et ses g\ue8nes cibles, ou les r\ue9seaux de g\ue8nes.Peer reviewed: YesNRC publication: Ye