ExonMiner: Web service for analysis of GeneChip Exon array data

• Main Authors: Imoto Seiya, Saito Ayumu, Nagasaki Masao, Yoshida Ryo, Numata Kazuyuki, Miyano Satoru
• Format: Article
• Language: English
• Published: BMC 2008-11-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/9/494

<p>Abstract</p> <p>Background</p> <p>Some splicing isoform-specific transcriptional regulations are related to disease. Therefore, detection of disease specific splice variations is the first step for finding disease specific transcriptional regulations. Affymetrix Human Exon 1.0 ST Array can measure exon-level expression profiles that are suitable to find differentially expressed exons in genome-wide scale. However, exon array produces massive datasets that are more than we can handle and analyze on personal computer.</p> <p>Results</p> <p>We have developed ExonMiner that is the first all-in-one web service for analysis of exon array data to detect transcripts that have significantly different splicing patterns in two cells, e.g. normal and cancer cells. ExonMiner can perform the following analyses: (1) data normalization, (2) statistical analysis based on two-way ANOVA, (3) finding transcripts with significantly different splice patterns, (4) efficient visualization based on heatmaps and barplots, and (5) meta-analysis to detect exon level biomarkers. We implemented ExonMiner on a supercomputer system in order to perform genome-wide analysis for more than 300,000 transcripts in exon array data, which has the potential to reveal the aberrant splice variations in cancer cells as exon level biomarkers.</p> <p>Conclusion</p> <p>ExonMiner is well suited for analysis of exon array data and does not require any installation of software except for internet browsers. What all users need to do is to access the ExonMiner URL <url>http://ae.hgc.jp/exonminer</url>. Users can analyze full dataset of exon array data within hours by high-level statistical analysis with sound theoretical basis that finds aberrant splice variants as biomarkers.</p>