SplicerAV: a tool for mining microarray expression data for changes in RNA processing

BMC Bioinformatics

Methodology Article

Michaela A Dinan¹  James L Pearson²  Mariano A Garcia-Blanco³  Mark Dewhirst⁴  Timothy J Robinson⁵ 

[1] Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, USA;Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, USA;Center for RNA Biology, Duke University Medical Center, Durham, USA;Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, USA;Department of Medicine, Duke University Medical Center, Durham, USA;Center for RNA Biology, Duke University Medical Center, Durham, USA;Department of Radiation Oncology, Duke University Medical Center, Durham, USA;Molecular Cancer Biology Program, Duke University Medical Center, Durham, USA;

关键词: Epidermal Growth Factor Receptor;    Gaussian Mixture Model;    Epidermal Growth Factor Receptor Expression;    Alternative Processing;    Splice Index;   

DOI  :  10.1186/1471-2105-11-108

 received in 2009-09-23, accepted in 2010-02-25,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundOver the past two decades more than fifty thousand unique clinical and biological samples have been assayed using the Affymetrix HG-U133 and HG-U95 GeneChip microarray platforms. This substantial repository has been used extensively to characterize changes in gene expression between biological samples, but has not been previously mined en masse for changes in mRNA processing. We explored the possibility of using HG-U133 microarray data to identify changes in alternative mRNA processing in several available archival datasets.ResultsData from these and other gene expression microarrays can now be mined for changes in transcript isoform abundance using a program described here, SplicerAV. Using in vivo and in vitro breast cancer microarray datasets, SplicerAV was able to perform both gene and isoform specific expression profiling within the same microarray dataset. Our reanalysis of Affymetrix U133 plus 2.0 data generated by in vitro over-expression of HRAS, E2F3, beta-catenin (CTNNB1), SRC, and MYC identified several hundred oncogene-induced mRNA isoform changes, one of which recognized a previously unknown mechanism of EGFR family activation. Using clinical data, SplicerAV predicted 241 isoform changes between low and high grade breast tumors; with changes enriched among genes coding for guanyl-nucleotide exchange factors, metalloprotease inhibitors, and mRNA processing factors. Isoform changes in 15 genes were associated with aggressive cancer across the three breast cancer datasets.ConclusionsUsing SplicerAV, we identified several hundred previously uncharacterized isoform changes induced by in vitro oncogene over-expression and revealed a previously unknown mechanism of EGFR activation in human mammary epithelial cells. We analyzed Affymetrix GeneChip data from over 400 human breast tumors in three independent studies, making this the largest clinical dataset analyzed for en masse changes in alternative mRNA processing. The capacity to detect RNA isoform changes in archival microarray data using SplicerAV allowed us to carry out the first analysis of isoform specific mRNA changes directly associated with cancer survival.

【 授权许可】

Unknown   
© Robinson et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.