ArticleBMC bioinformatics2006
GO-Diff: mining functional differentiation between EST-based transcriptomes.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Comprehensive literature review and statistical considerations for microarray meta-analysis.Nucleic acids research · 2012Review
- Bio301: A Web-Based EST Annotation Pipeline That Facilitates Functional Comparison Studies.ISRN bioinformatics · 2012Article
- Massively parallel pyrosequencing-based transcriptome analyses of small brown planthopper (Laodelphax striatellus), a vector insect transmitting rice stripe virus (RSV).BMC genomics · 2010Article
- Meta Analysis of Gene Expression Data within and Across Species.Current genomics · 2008Article
- ESTs analyses of Lampetra japonica liver and comparation transcriptome with the jawed vertebrates.Science in China. Series C, Life sciences · 2008Article
- Comparative analysis of differentially expressed genes in normal and white spot syndrome virus infected Penaeus monodon.BMC genomics · 2007Article
- From genes to functional classes in the study of biological systems.BMC bioinformatics · 2007Article
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5 authors.
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Abstract
backgroundLarge-scale sequencing efforts produced millions of Expressed Sequence Tags (ESTs) collectively representing differentiated biochemical and functional states. Analysis of these EST libraries reveals differential gene expressions, and therefore EST data sets constitute valuable resources for comparative transcriptomics. To translate differentially expressed genes into a better understanding of the underlying biological phenomena, existing microarray analysis approaches usually involve the integration of gene expression with Gene Ontology (GO) databases to derive comparable functional profiles. However, methods are not available yet to process EST-derived transcription maps to enable GO-based global functional profiling for comparative transcriptomics in a high throughput manner.
resultsHere we present GO-Diff, a GO-based functional profiling approach towards high throughput EST-based gene expression analysis and comparative transcriptomics. Utilizing holistic gene expression information, the software converts EST frequencies into EST Coverage Ratios of GO Terms. The ratios are then tested for statistical significances to uncover differentially represented GO terms between the compared transcriptomes, and functional differences are thus inferred. We demonstrated the validity and the utility of this software by identifying differentially represented GO terms in three application cases: intra-species comparison; meta-analysis to test a specific hypothesis; inter-species comparison. GO-Diff findings were consistent with previous knowledge and provided new clues for further discoveries. A comprehensive test on the GO-Diff results using series of comparisons between EST libraries of human and mouse tissues showed acceptable levels of consistency: 61% for human-human; 69% for mouse-mouse; 47% for human-mouse.
conclusionGO-Diff is the first software integrating EST profiles with GO knowledge databases to mine functional differentiation between biological systems, e.g. tissues of the same species or the same tissue cross species. With rapid accumulation of EST resources in the public domain and expanding sequencing effort in individual laboratories, GO-Diff is useful as a screening tool before undertaking serious expression studies.
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