Algorithms in Bioinformatics

Volume 4175 of the series Lecture Notes in Computer Science pp 126-137

Procrastination Leads to Efficient Filtration for Local Multiple Alignment

  • Aaron E. DarlingAffiliated withDepartment of Computer Science, University of Wisconsin
  • , Todd J. TreangenAffiliated withDepartment of Computer Science, Technical University of Catalonia
  • , Louxin ZhangAffiliated withDepartment of Mathematics, National University of Singapore
  • , Carla KuikenAffiliated withT-10 Theoretical Biology Division, Los Alamos National Laboratory
  • , Xavier MesseguerAffiliated withDepartment of Computer Science, Technical University of Catalonia
  • , Nicole T. PernaAffiliated withDepartment of Animal Health and Biomedical Sciences, Genome Center, University of Wisconsin

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We describe an efficient local multiple alignment filtration heuristic for identification of conserved regions in one or more DNA sequences. The method incorporates several novel ideas: (1) palindromic spaced seed patterns to match both DNA strands simultaneously, (2) seed extension (chaining) in order of decreasing multiplicity, and (3) procrastination when low multiplicity matches are encountered. The resulting local multiple alignments may have nucleotide substitutions and internal gaps as large as w characters in any occurrence of the motif. The algorithm consumes \(\mathcal{O}(wN)\) memory and \(\mathcal{O}(wN \log wN)\) time where N is the sequence length. We score the significance of multiple alignments using entropy-based motif scoring methods. We demonstrate the performance of our filtration method on Alu-repeat rich segments of the human genome and a large set of Hepatitis C virus genomes. The GPL implementation of our algorithm in C++ is called procrastAligner and is freely available from