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Gapped Extension for Local Multiple Alignment of Interspersed DNA Repeats

  • Todd J. Treangen
  • Aaron E. Darling
  • Mark A. Ragan
  • Xavier Messeguer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4983)

Abstract

The identification of homologous DNA is a fundamental building block of comparative genomic and molecular evolution studies. To date, pairwise local sequence alignment methods have been the prevailing technique to identify homologous nucleotides. However, existing methods that identify and align all homologous nucleotides in one or more genomes have suffered poor scalability and limited accuracy. We propose a novel method that couples a gapped extension heuristic with a previously described efficient filtration method for local multiple alignment. During gapped extension, we use the MUSCLE implementation of progressive multiple alignment with iterative refinement. The resulting gapped extensions potentially contain alignments of unrelated sequence. We detect and remove such undesirable alignments using a hidden Markov model to predict the posterior probability of homology. The HMM emission frequencies for nucleotide substitutions can be derived from any strand/species-symmetric nucleotide substitution matrix, and we have developed a method to adapt an arbitrary substitution matrix (i.e. HOXD) to organisms with different G+C content. We evaluate the performance of our method and previous approaches on a hybrid dataset of real genomic DNA with simulated interspersed repeats. Our method outperforms existing methods in terms of sensitivity, positive predictive value, and localizing boundaries of homology. The described methods have been implemented in the free, open-source procrastAligner software, available from: http://alggen.lsi.upc.es/recerca/align/ procrastination

Keywords

Positive Predictive Value Hide Markov Model Input Sequence Pairwise Alignment Gapped Extension 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Todd J. Treangen
    • 1
  • Aaron E. Darling
    • 2
  • Mark A. Ragan
    • 2
  • Xavier Messeguer
    • 1
  1. 1.Dept. of Computer SciencePolytechnic University of CataloniaBarcelonaSpain
  2. 2.ARC Centre of Excellence in Bioinformatics, and Institute for Molecular BioscienceThe University of QueenslandBrisbaneAustralia

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