Skip to main content
Springer Nature Link
Log in

Finding Novel Transcripts in High-Resolution Genome-Wide Microarray Data Using the GenRate Model

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3500))

  • 1180 Accesses

Abstract

Genome-wide microarray designs containing millions to tens of millions of probes will soon become available for a variety of mammals, including mouse and human. These “tiling arrays” can potentially lead to significant advances in science and medicine, e.g., by indicating new genes and alternative primary and secondary transcripts. While bottom-up pattern matching techniques (e.g., hierarchical clustering) can be used to find gene structures in tiling data, we believe the many interacting hidden variables and complex noise patterns more naturally lead to an analysis based on generative models. We describe a generative model of tiling data and show how the iterative sum-product algorithm can be used to infer hybridization noise, probe sensitivity, new transcripts and alternative transcripts. We apply our method, called GenRate, to a new exon tiling data set from mouse chromosome 4 and show that it makes significantly more predictions than a previously described hierarchical clustering method at the same false positive rate. GenRate correctly predicts many known genes, and also predicts new gene structures. As new problems arise, additional hidden variables can be incorporated into the model in a principled fashion, so we believe that GenRate will prove to be a useful tool in the new era of genome-wide tiling microarray analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Frey, B.J., et al.: Genome-wide analysis of mouse transcription using exon-resolution microarrays and factor graphs. Under review

    Google Scholar 

  2. Storz, G.: An expanding universe of noncoding RNAs. Science 296, 1260–1263 (2002)

    Article  Google Scholar 

  3. Mironov, A.A., et al.: Frequent alternative splicing of human genes. Genome Research 9 (1999)

    Google Scholar 

  4. Maniatis, T., et al.: Alternative pre-mRNA splicing and proteome expansion in metazoans. Nature 418 (2002)

    Google Scholar 

  5. Burge, C., et al.: Splicing of precursors to mRNAs by the spliceosomes, 2nd edn. Cold Spring Harbor Laboratory Press, New York

    Google Scholar 

  6. Peng, W.T., et al.: A panoramic view of yeast noncoding RNA processing. Cell 113(7), 919–933 (2003)

    Article  Google Scholar 

  7. Zhang, W., et al.: The functional landscape of mouse gene expression. J. Biol. 3(21) (Epub. December 2004)

    Google Scholar 

  8. Pan, Q., et al.: Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform. Mol. Cell. 16, 929–941 (2004)

    Article  Google Scholar 

  9. Pan, Q., et al.: Alternative splicing of conserved exons is frequently species-specific in human and mouse. Trends in Genet. 21(2), 73–77 (2005)

    Article  Google Scholar 

  10. Shai, O., et al.: Spatial bias removal in microarray images. Univ. Toronto TR PSI-2003-21 (2003)

    Google Scholar 

  11. Hild, M., et al.: An integrated gene annotation and transcriptional profiling approach towards the full gene content of the Drosophila genome. Genome Biol. 5(1), R3 (2003) (Epub. December 22, 2003)

    Google Scholar 

  12. Hughes, T.R., et al.: Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat. Biotechnol. 19(4), 342–347 (2001)

    Article  Google Scholar 

  13. Kapranov, P., et al.: Large-scale transcriptional activity in chromosomes 21 and 22. Science 296(5569), 916–919 (2002)

    Article  Google Scholar 

  14. Schadt, E.E., et al.: A comprehensive transcript index of the human genome generated using microarrays and computational approaches. Genome Biology 5 (2004)

    Google Scholar 

  15. Stolc, V., et al.: A gene expression map for the euchromatic genome of drosophila melanogaster. To appear in Science (2004)

    Google Scholar 

  16. Nuwaysir, E.F., et al.: Gene expression analysis using oligonucleotide arrays produced by maskless photolithography. Gen. Res. 12(11), 1749–1755 (2002)

    Article  Google Scholar 

  17. Okazaki, Y., et al.: FANTOM Consortium: RIKEN Genome Exploration Research Group Phase I & II Team. Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420(6915), 563–573 (2002)

    Article  MathSciNet  Google Scholar 

  18. Kent, W.J.: BLAT–the BLAST-like alignment tool. Genome Res. 12(4), 656–664 (2002)

    MathSciNet  Google Scholar 

  19. Pen, S.G., et al.: Mining the human genome using microarrays of open reading frames. Nat. Genet. 26(3), 315–318 (2000)

    Article  MathSciNet  Google Scholar 

  20. Rinn, J.L., et al.: The transcriptional activity of human chromosome 22. Genes Dev. 17(4), 529–540 (2003)

    Article  Google Scholar 

  21. Shoemaker, D.D., et al.: Experimental annotation of the human genome using microarray technology. Nature 409(6822), 922–927 (2001)

    Article  Google Scholar 

  22. Friedman, N., et al.: Using Bayesian networks to analyze expression data. Jour. Comp. Biology 7 (2000)

    Google Scholar 

  23. Yamada, K., et al.: Empirical Analysis of Transcriptional Activity in the Arabidopsis Genome. Science 302 (2003)

    Google Scholar 

  24. Segal, E., et al.: Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics 19 (2003)

    Google Scholar 

  25. Frey, B.J., et al.: GenRate: A generative model that finds and scores new genes and exons in genomic microarray data. Proc. PSB (January 2005)

    Google Scholar 

  26. Huber, W., et al.: Variance stabilization applied to microarray data calibration and to quantification of differential expression. Bioinformatics 18 (2002)

    Google Scholar 

  27. Dempster, A.P., et al.: Maximum likelihood from incomplete data via the EM algorithm. Proc. Royal Stat. Soc. B-39 (1977)

    Google Scholar 

  28. Kschischang, F.R., et al.: Factor graphs and the sum-product algorithm. IEEE Trans. Infor. Theory 47 (2001)

    Google Scholar 

  29. Frey, B.J.: Extending factor graphs so as to unify directed and undirected graphical models. In: Proc. UAI (August 2003)

    Google Scholar 

  30. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 431 (2004)

    Google Scholar 

  31. Berman, P., et al.: Fast optimal genome tiling with applications to microarray design and homology search. JCB 11, 766–785 (2004)

    Google Scholar 

  32. Bertone, P., et al.: Global identification of human transcribed sequences with genome tiling arrays. Science 24 (December 2004)

    Google Scholar 

  33. Ostendorf, M.: IEEE Trans. Speech & Audio Proc. 4, 360 (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Elec. and Comp. Eng., Univ. of Toronto, Toronto, ON, M5S 3G4, Canada

    Brendan J. Frey, Quaid D. Morris & Mark Robinson

  2. Banting and Best Dep. Med. Res., Univ. of Toronto, Toronto, ON, M5G 1L6, Canada

    Quaid D. Morris, Mark Robinson & Timothy R. Hughes

Authors
  1. Brendan J. Frey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Quaid D. Morris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark Robinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Timothy R. Hughes
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Satoru Miyano

  2. Broad Institute of MIT and Harvard, 320 Charles Street, 02141-2023, Cambridge, MA, USA

    Jill Mesirov

  3. Computational Genomics Laboratory, Department of Bioengineering, Boston University, 44 Cummington St., 02215, Boston, MA, USA

    Simon Kasif

  4. Center for Molecular Biology and Computer Sciecne Department, Brown University, 115 Waterman St., 02912, Providence, RI, USA

    Sorin Istrail

  5. University of California, San Diego, USA

    Pavel A. Pevzner

  6. Department of Molecular and Computational Biology, University of Southern California, 1050 Childs Way, 90089-2910, Los Angeles, CA, USA

    Michael Waterman

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Frey, B.J., Morris, Q.D., Robinson, M., Hughes, T.R. (2005). Finding Novel Transcripts in High-Resolution Genome-Wide Microarray Data Using the GenRate Model. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2005. Lecture Notes in Computer Science(), vol 3500. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11415770_5

Download citation

  • DOI: https://doi.org/10.1007/11415770_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25866-7

  • Online ISBN: 978-3-540-31950-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics