ArticleBMC bioinformatics2010
GOAL: a software tool for assessing biological significance of genes groups.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 28 citations in OpenAlex.
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModern 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.
resultsWe 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.
conclusionWe 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.
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