Gene structure-based splice variant deconvolution using a microarry platform (original) (raw)

Hui Wang, Earl Hubbell, Jing-shan Hu, Gangwu Mei, Melissa Cline, Gang Lu, Tyson Clark, Michael A. Siani-Rose, Manuel Ares, David C. Kulp, David Haussler, Gene structure-based splice variant deconvolution using a microarry platform, Bioinformatics, Volume 19, Issue suppl_1, July 2003, Pages i315–i322, https://doi.org/10.1093/bioinformatics/btg1044
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Abstract

Motivation: Alternative splicing allows a single gene to generate multiple mRNAs, which can be translated into functionally and structurally diverse proteins. One gene can have multiple variants coexisting at different concentrations. Estimating the relative abundance of each variant is important for the study of underlying biological function. Microarrays are standard tools that measure gene expression. But most design and analysis has not accounted for splice variants. Thus splice variant-specific chip designs and analysis algorithms are needed for accurate gene expression profiling.

Results: Inspired by Li and Wong (2001), we developed a gene structure-based algorithm to determine the relative abundance of known splice variants. Probe intensities are modeled across multiple experiments using gene structures as constraints. Model parameters are obtained through a maximum likelihood estimation (MLE) process/framework. The algorithm produces the relative concentration of each variant, as well as an affinity term associated with each probe. Validation of the algorithm is performed by a set of controlled spike experiments as well as endogenous tissue samples using a human splice variant array.

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