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Using Multiple Alignments to Improve Gene Prediction

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Research in Computational Molecular Biology (RECOMB 2005)

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

Abstract

The multiple species de novo gene prediction problem can be stated as follows: given an alignment of genomic sequences from two or more organisms, predict the location and structure of all protein-coding genes in one or more of the sequences. Here, we present a new system, N-SCAN (a.k.a. TWINSCAN 3.0), for addressing this problem. N-SCAN has the ability to model dependencies between the aligned sequences, context-dependent substitution rates, and insertions and deletions in the sequences. An implementation of N-SCAN was created and used to generate predictions for the entire human genome. An analysis of the predictions reveals that N-SCAN’s predictive accuracy in human exceeds that of all previously published whole-genome de novo gene predictors. In addition, predictions were generated for the genome of the fruit fly Drosophila melanogaster to demonstrate the applicability of N-SCAN to invertebrate gene prediction.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Washington University, St. Louis, MO, 63130, USA

    Samuel S. Gross & Michael R. Brent

  2. Computer Science Department, Stanford University, Stanford, CA, 94305, USA

    Samuel S. Gross

Authors
  1. Samuel S. Gross
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  2. Michael R. Brent
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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

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© 2005 Springer-Verlag Berlin Heidelberg

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Gross, S.S., Brent, M.R. (2005). Using Multiple Alignments to Improve Gene Prediction. 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_29

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  • DOI: https://doi.org/10.1007/11415770_29

  • 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)

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