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.
Highlights
ResultsWe present Gene Ontology (GO)-Diff, a GO-based functional profiling approach towards high throughput Expressed Sequence Tag (EST)-based gene expression analysis and comparative transcriptomics
Large-scale sequencing efforts produced millions of Expressed Sequence Tags (ESTs) collectively representing differentiated biochemical and functional states
Exhausting EST sequencing projects provide a vast repository of EST information, which can be an alternative
Summary
Exhausting EST sequencing projects provide a vast repository of EST information, which can be an alternative. Pairwise comparative analyses of dbEST libraries of eight other tissues with those of oocyte and preimplantation embryos yielded many differentially represented GO terms related to DNA damage response (Table 3). With these cross-tissue examinations, we observed transcripts associated with such processes were highly repre-. To reduce false positives caused by biased GO annotation, we incorporated multiple GODiff results into meta-analysis using both GO associations in the background database and from BLAST search Even in the contexts of the high intra-species variation and even higher variation between species, GODiff can generate repeatable and reliable results
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