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pmid: 17237099
AbstractMotivation: 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 atContact: sariel@cs.berkeley.edu
Sequence Analysis, Protein, Molecular Sequence Data, Proteins, Amino Acid Sequence, Sequence Alignment, Algorithms, 004
Sequence Analysis, Protein, Molecular Sequence Data, Proteins, Amino Acid Sequence, Sequence Alignment, Algorithms, 004
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 67 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |