Pair HMM Based Gap Statistics for Re-evaluation of Indels in Alignments with Affine Gap Penalties

  • Alexander Schönhuth
  • Raheleh Salari
  • S. Cenk Sahinalp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)

Abstract

Although computationally aligning sequence is a crucial step in the vast majority of comparative genomics studies our understanding of alignment biases still needs to be improved. To infer true structural or homologous regions computational alignments need further evaluation. It has been shown that the accuracy of aligned positions can drop substantially in particular around gaps. Here we focus on re-evaluation of score-based alignments with affine gap penalty costs. We exploit their relationships with pair hidden Markov models and develop efficient algorithms by which to identify gaps which are significant in terms of length and multiplicity. We evaluate our statistics with respect to the well-established structural alignments from SABmark and find that indel reliability substantially increases with their significance in particular in worst-case twilight zone alignments. This points out that our statistics can reliably complement other methods which mostly focus on the reliability of match positions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Schönhuth
    • 1
  • Raheleh Salari
    • 2
  • S. Cenk Sahinalp
    • 2
  1. 1.Department of MathematicsUniversity of California at BerkeleyUSA
  2. 2.School of Computing ScienceSimon Fraser UniversityBurnaby

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