Gene structure-based splice variant deconvolution using a microarray platform - PubMed (original) (raw)
Comparative Study
Gene structure-based splice variant deconvolution using a microarray platform
Hui Wang et al. Bioinformatics. 2003.
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.
Similar articles
- Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.
Seo J, Bakay M, Chen YW, Hilmer S, Shneiderman B, Hoffman EP. Seo J, et al. Bioinformatics. 2004 Nov 1;20(16):2534-44. doi: 10.1093/bioinformatics/bth280. Epub 2004 Apr 29. Bioinformatics. 2004. PMID: 15117752 - Fast and accurate probe selection algorithm for large genomes.
Sung WK, Lee WH. Sung WK, et al. Proc IEEE Comput Soc Bioinform Conf. 2003;2:65-74. Proc IEEE Comput Soc Bioinform Conf. 2003. PMID: 16452780 - Predicting splice variant from DNA chip expression data.
Hu GK, Madore SJ, Moldover B, Jatkoe T, Balaban D, Thomas J, Wang Y. Hu GK, et al. Genome Res. 2001 Jul;11(7):1237-45. doi: 10.1101/gr.165501. Genome Res. 2001. PMID: 11435406 Free PMC article. - Bioinformatics analysis of alternative splicing.
Lee C, Wang Q. Lee C, et al. Brief Bioinform. 2005 Mar;6(1):23-33. doi: 10.1093/bib/6.1.23. Brief Bioinform. 2005. PMID: 15826354 Review. - There is no silver bullet--a guide to low-level data transforms and normalisation methods for microarray data.
Kreil DP, Russell RR. Kreil DP, et al. Brief Bioinform. 2005 Mar;6(1):86-97. doi: 10.1093/bib/6.1.86. Brief Bioinform. 2005. PMID: 15826359 Review.
Cited by
- Novornabreak: Local Assembly for Novel Splice Junction and Fusion Transcript Detection from RNA-Seq Data.
Tan Y, Mohanty V, Liang S, Dou J, Ma J, Kim KH, Bonder MJ, Shi X, Lee C; Human Genome Structural Variation Consortium; Chong Z, Chen K. Tan Y, et al. J Bioinform Syst Biol. 2023;6(2):74-81. doi: 10.26502/jbsb.5107050. Epub 2023 Apr 4. J Bioinform Syst Biol. 2023. PMID: 39301431 Free PMC article. - Alternative splicing: An important mechanism in stem cell biology.
Chen K, Dai X, Wu J. Chen K, et al. World J Stem Cells. 2015 Jan 26;7(1):1-10. doi: 10.4252/wjsc.v7.i1.1. World J Stem Cells. 2015. PMID: 25621101 Free PMC article. Review. - Review: Alternative Splicing (AS) of Genes As An Approach for Generating Protein Complexity.
Roy B, Haupt LM, Griffiths LR. Roy B, et al. Curr Genomics. 2013 May;14(3):182-94. doi: 10.2174/1389202911314030004. Curr Genomics. 2013. PMID: 24179441 Free PMC article. - MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples.
Behr J, Kahles A, Zhong Y, Sreedharan VT, Drewe P, Rätsch G. Behr J, et al. Bioinformatics. 2013 Oct 15;29(20):2529-38. doi: 10.1093/bioinformatics/btt442. Epub 2013 Aug 25. Bioinformatics. 2013. PMID: 23980025 Free PMC article. - Simultaneous isoform discovery and quantification from RNA-seq.
Hiller D, Wong WH. Hiller D, et al. Stat Biosci. 2013 May 1;5(1):100-118. doi: 10.1007/s12561-012-9069-2. Stat Biosci. 2013. PMID: 23888185 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Miscellaneous