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      Consensus sequences improve PSI-BLAST through mimicking profile–profile alignments

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      1 , 2 , * , 1 , 2 , 3
      Nucleic Acids Research
      Oxford University Press

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          Abstract

          Sequence alignments may be the most fundamental computational resource for molecular biology. The best methods that identify sequence relatedness through profile–profile comparisons are much slower and more complex than sequence–sequence and sequence–profile comparisons such as, respectively, BLAST and PSI-BLAST. Families of related genes and gene products (proteins) can be represented by consensus sequences that list the nucleic/amino acid most frequent at each sequence position in that family. Here, we propose a novel approach for consensus-sequence-based comparisons. This approach improved searches and alignments as a standard add-on to PSI-BLAST without any changes of code. Improvements were particularly significant for more difficult tasks such as the identification of distant structural relations between proteins and their corresponding alignments. Despite the fact that the improvements were higher for more divergent relations, they were consistent even at high accuracy/low error rates for non-trivially related proteins. The improvements were very easy to achieve; no parameter used by PSI-BLAST was altered and no single line of code changed. Furthermore, the consensus sequence add-on required relatively little additional CPU time. We discuss how advanced users of PSI-BLAST can immediately benefit from using consensus sequences on their local computers. We have also made the method available through the Internet ( http://www.rostlab.org/services/consensus/).

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          Most cited references38

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          Identification of common molecular subsequences.

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            Amino acid substitution matrices from protein blocks.

            Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
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              Improved tools for biological sequence comparison.

              We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                April 2007
                16 March 2007
                16 March 2007
                : 35
                : 7
                : 2238-2246
                Affiliations
                1Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA, 2Columbia University Center for Computational Biology and Bioinformatics (C2B2), 1130 St. Nicholas Ave. Rm. 801, New York, NY 10032, USA and 3NorthEast Structural Genomics Consortium (NESG), Columbia University, 1130 St. Nicholas Ave. Rm. 802, New York, NY 10032, USA
                Author notes
                *To whom correspondence should be addressed. +1 212 851 4669+1 212 851 5176 dsp23@ 123456columbia.edu
                Article
                10.1093/nar/gkm107
                1874647
                17369271
                8177945f-015f-4cf2-ae06-25ef58762f20
                © 2007 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 December 2006
                : 5 February 2007
                : 6 February 2007
                Categories
                Computational Biology

                Genetics
                Genetics

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