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.
Highlights
Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes
In this paper we introduce a Gene Ontology AnaLyzer, GOAL, which is a software tool designed for the biological evaluation of groups of genes
The first dataset is a group of co-expressed gene from the timeseries gene expression data of the Saccharomyces cerevisiae amino acid (AA) starvation dataset [33]
Summary
Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes Computational biology tools such as clustering are used to group together genes based on their similarity in expression profiles. A prevailing procedure in functional characterization is to search the annotation of genes in respective group through Gene Ontology (GO) [6,7,8], KEGG pathways [9], REACTOME pathways [10], transcription factor (TF) gene association data [11], and/or gene networks. These post processing steps allow identifying biological functions which are highly represented in the given group of genes
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