Analysis of Isoform Expression from Splicing Array Using Multiple Comparisons

  • T. Murlidharan NairEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 802)


There is a high prevalence of alternatively spliced genes (isoforms) in the human genome. Studies toward understanding aberrantly spliced genes and their association with diseases have lead researchers to profile the expression of alternatively spliced products. High-throughput profiling of isoforms has been done using microarray technology. Expression of isoforms reflects regulation both at transcriptional and posttranscriptional levels. This chapter details the methods to perform exhaustive comparison of isoforms using the R statistical framework.

Key words

mRNA isoforms Multiple comparisons 



TMN would like to thank IUSB for research funding.


  1. 1.
    Matlin AJ, Clark F, Smith CW (2005) Understanding alternative splicing: towards a cellular code. Nat Rev Mol Cell Biol 6:386–398.PubMedCrossRefGoogle Scholar
  2. 2.
    Kim N, Lee C (2008) Bioinformatics detection of alternative splicing. Methods Mol Biol 452:179–197.PubMedCrossRefGoogle Scholar
  3. 3.
    Ferreira EN, Galante PA, Carraro DM et al (2007) Alternative splicing: a bioinformatics perspective. Mol Biosyst 3:473–477.PubMedCrossRefGoogle Scholar
  4. 4.
    Chacko E, Ranganathan S (2009) Comprehensive splicing graph analysis of alternative splicing patterns in chicken, compared to human and mouse. BMC Genomics 10:S5.PubMedCrossRefGoogle Scholar
  5. 5.
    Lee C, Wang Q (2005) Bioinformatics analysis of alternative splicing. Brief Bioinform 6:23–33.PubMedCrossRefGoogle Scholar
  6. 6.
    Li HR, Wang-Rodriguez J, Nair TM et al (2006) Two-dimensional transcriptome profiling: identification of messenger RNA isoform signatures in prostate cancer from archived paraffin-embedded cancer specimens. Cancer Res 66:4079–4088.PubMedCrossRefGoogle Scholar
  7. 7.
    Blencowe BJ (2006) Alternative splicing: new insights from global analyses. Cell 126:37–47.PubMedCrossRefGoogle Scholar
  8. 8.
    Johnson JM, Castle J, Garrett-Engele P et al (2003) Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science 302:2141–2144.PubMedCrossRefGoogle Scholar
  9. 9.
    Pando MP, Kotraiah V, McGowan K et al (2006) Alternative isoform discrimination by the next generation of expression profiling microarrays. Expert Opin Ther Targets 10:613–625.PubMedCrossRefGoogle Scholar
  10. 10.
    Pandit S, Wang D, Fu XD (2008) Functional integration of transcriptional and RNA processing machineries. Curr Opin Cell Biol 20:260–265.PubMedCrossRefGoogle Scholar
  11. 11.
    Hardiman G (2004) Microarray platforms – comparisons and contrasts. Pharmacogenomics 5:487–502.PubMedCrossRefGoogle Scholar
  12. 12.
    Lee NH, Saeed AI (2007) Microarrays: an overview. Methods Mol Biol 353:265–300.PubMedGoogle Scholar
  13. 13.
    Yeakley JM, Fan JB, Doucet D et al (2002) Profiling alternative splicing on fiber-optic arrays. Nat Biotechnol 20:353–358.PubMedCrossRefGoogle Scholar
  14. 14.
    Fan JB, Yeakley JM, Bibikova M et al (2004) A versatile assay for high-throughput gene expression profiling on universal array matrices. Genome Res 14:878–885.PubMedCrossRefGoogle Scholar
  15. 15.
  16. 16.
  17. 17.
    Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biom J 50:346–363.PubMedCrossRefGoogle Scholar
  18. 18.
    Nair TM (2009) On selecting mRNA isoform features for profiling prostate cancer. Comput Biol Chem 33:421–428.PubMedCrossRefGoogle Scholar
  19. 19.
    Bemmo A, Benovoy D, Kwan T et al (2008) Gene expression and isoform variation analysis using Affymetrix Exon Arrays. BMC Genomics 9:529.PubMedCrossRefGoogle Scholar
  20. 20.
    Bemmo A, Dias C, Rose AA et al (2010) Exon-level transcriptome profiling in murine breast cancer reveals splicing changes specific to tumors with different metastatic abilities. PLoS ONE 5: e11981.PubMedCrossRefGoogle Scholar
  21. 21.
    Bolstad BM, Irizarry RA, Astrand M et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193.PubMedCrossRefGoogle Scholar
  22. 22.
    Zeller G, Henz SR, Laubinger S et al (2008) Transcript normalization and segmentation of tiling array data. Pac Symp Biocomput: 527–538.Google Scholar
  23. 23.
    Haldermans P, Shkedy Z, Van Sanden S et al (2007) Using linear mixed models for normalization of cDNA microarrays. Stat Appl Genet Mol Biol 6:Article 19.Google Scholar
  24. 24.
    Cleveland WS (1979) Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association 74:829–836.CrossRefGoogle Scholar
  25. 25.
    Hothorn T, Bretz F, Westfall P et al (2008) Multcomp: Simultaneous Inference for General Linear Hypotheses. URL
  26. 26.
    Vera G, Jansen RC, Suppi RL (2008) R/parallel – speeding up bioinformatics analysis with R. BMC Bioinformatics 9:390.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Departments of Biological Sciences, Computer Science/InformaticsIndiana University South BendBloomingtonUSA

Personalised recommendations