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A Probabilistic Model for Sequence Alignment with Context-Sensitive Indels

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Research in Computational Molecular Biology (RECOMB 2011)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6577))

Abstract

Probabilistic approaches for sequence alignment are usually based on pair Hidden Markov Models (HMMs) or Stochastic Context Free Grammars (SCFGs). Recent studies have shown a significant correlation between the content of short indels and their flanking regions, which by definition cannot be modelled by the above two approaches. In this work, we present a context-sensitive indel model based on a pair Tree-Adjoining Grammar (TAG), along with accompanying algorithms for efficient alignment and parameter estimation. The increased precision and statistical power of this model is shown on simulated and real genomic data. As the cost of sequencing plummets, the usefulness of comparative analysis is becoming limited by alignment accuracy rather than data availability. Our results will therefore have an impact on any type of downstream comparative genomics analyses that rely on alignments. Fine-grained studies of small functional regions or disease markers, for example, could be significantly improved by our method. The implementation is available at http://www.mcb.mcgill.ca/~blanchem/software.html

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Author information

Authors and Affiliations

  1. McGill Centre for Bioinformatics, McGill University, Montreal, Quebec, Canada

    Glenn Hickey & Mathieu Blanchette

Authors
  1. Glenn Hickey
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  2. Mathieu Blanchette
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Editor information

Editors and Affiliations

  1. EBU3b, University of California San Diego, #4218, 9500 Gilman Drive, 92093-0404, La Jolla, CA, USA

    Vineet Bafna

  2. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    S. Cenk Sahinalp

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© 2011 Springer-Verlag Berlin Heidelberg

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Hickey, G., Blanchette, M. (2011). A Probabilistic Model for Sequence Alignment with Context-Sensitive Indels. In: Bafna, V., Sahinalp, S.C. (eds) Research in Computational Molecular Biology. RECOMB 2011. Lecture Notes in Computer Science(), vol 6577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20036-6_10

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  • DOI: https://doi.org/10.1007/978-3-642-20036-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20035-9

  • Online ISBN: 978-3-642-20036-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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