Ariel S Schwartz1, Lior Pachter. 1. EECS, Computer Science Division, University of California Berkeley, CA 94720, USA. sariel@cs.berkeley.edu
Abstract
MOTIVATION: We introduce a novel approach to multiple alignment that is based on an algorithm for rapidly checking whether single matches are consistent with a partial multiple alignment. This leads to a sequence annealing algorithm, which is an incremental method for building multiple sequence alignments one match at a time. Our approach improves significantly on the standard progressive alignment approach to multiple alignment. RESULTS: The sequence annealing algorithm performs well on benchmark test sets of protein sequences. It is not only sensitive, but also specific, drastically reducing the number of incorrectly aligned residues in comparison to other programs. The method allows for adjustment of the sensitivity/specificity tradeoff and can be used to reliably identify homologous regions among protein sequences. AVAILABILITY: An implementation of the sequence annealing algorithm is available at http://bio.math.berkeley.edu/amap/
MOTIVATION: We introduce a novel approach to multiple alignment that is based on an algorithm for rapidly checking whether single matches are consistent with a partial multiple alignment. This leads to a sequence annealing algorithm, which is an incremental method for building multiple sequence alignments one match at a time. Our approach improves significantly on the standard progressive alignment approach to multiple alignment. RESULTS: The sequence annealing algorithm performs well on benchmark test sets of protein sequences. It is not only sensitive, but also specific, drastically reducing the number of incorrectly aligned residues in comparison to other programs. The method allows for adjustment of the sensitivity/specificity tradeoff and can be used to reliably identify homologous regions among protein sequences. AVAILABILITY: An implementation of the sequence annealing algorithm is available at http://bio.math.berkeley.edu/amap/
Authors: Benedict Paten; Dent Earl; Ngan Nguyen; Mark Diekhans; Daniel Zerbino; David Haussler Journal: Genome Res Date: 2011-06-10 Impact factor: 9.043
Authors: J Graham Ruby; Alexander Stark; Wendy K Johnston; Manolis Kellis; David P Bartel; Eric C Lai Journal: Genome Res Date: 2007-11-07 Impact factor: 9.043