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Annual International Conference on Research in Computational Molecular Biology

RECOMB 2006: Research in Computational Molecular Biology pp 160–174Cite as

CONTRAlign: Discriminative Training for Protein Sequence Alignment

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 3909)

Abstract

In this paper, we present CONTRAlign, an extensible and fully automatic framework for parameter learning and protein pairwise sequence alignment using pair conditional random fields. When learning a substitution matrix and gap penalties from as few as 20 example alignments, CONTRAlign achieves alignment accuracies competitive with available modern tools. As confirmed by rigorous cross-validated testing, CONTRAlign effectively leverages weak biological signals in sequence alignment: using CONTRAlign, we find that hydropathy-based features result in improvements of 5-6% in aligner accuracy for sequences with less than 20% identity, a signal that state-of-the-art hand-tuned aligners are unable to exploit effectively. Furthermore, when known secondary structure and solvent accessibility are available, such external information is naturally incorporated as additional features within the CONTRAlign framework, yielding additional improvements of up to 15-16% in alignment accuracy for low-identity sequences.

Keywords

  • Pairwise Alignment
  • Solvent Accessibility
  • Model Topology
  • Protein Sequence Alignment
  • Alignment Accuracy

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/11732990_15
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Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, 94305, USA

    Chuong B. Do, Samuel S. Gross & Serafim Batzoglou

Authors
  1. Chuong B. Do
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  2. Samuel S. Gross
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  3. Serafim Batzoglou
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology and Università di Padova,  

    Alberto Apostolico

  2. Topic Chairs, P.O. Box

    Concettina Guerra

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

    Sorin Istrail

  4. University of California, San Diego, USA

    Pavel A. Pevzner

  5. 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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© 2006 Springer-Verlag Berlin Heidelberg

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Do, C.B., Gross, S.S., Batzoglou, S. (2006). CONTRAlign: Discriminative Training for Protein Sequence Alignment. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_15

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  • DOI: https://doi.org/10.1007/11732990_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33295-4

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