An introduction to sequence similarity ("Homology") searching

Current Protocols in Bioinformatics

Volumn , Issue SUPPL. 27, 2009, Pages

  Stormo, Gary D ^a  

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Dynamic programming; Homology; Multiple alignment; Sequence alignment; Sequence evolution; Sequence similarity; Similarity scoring matrices

Indexed keywords

BIOINFORMATICS; COMPUTER PROGRAM; DATA ANALYSIS; GENE STRUCTURE; GENETIC ANALYSIS; GENETIC IDENTIFICATION; INFORMATION PROCESSING DEVICE; INFORMATION RETRIEVAL; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; REVIEW; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; SEQUENCE ALIGNMENT;

EID: 70149097323     PISSN: 19343396     EISSN: 1934340X     Source Type: Journal    
DOI: 10.1002/0471250953.bi0301s27     Document Type: Review

References (8)

1. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403-410.
2. Eddy, S.R. 1996. Hidden Markov models. Curr. Opin. Struct. Biol. 6:361-365.
3. Fitch, W. and Smith, T. 1983. Optimal sequence alignments. Proc. Natl. Acad. Sci. U.S.A. 80:1382-1386.
4. Gribskov, M., McLachlan, A.D., and Eisenberg, D. 1987. Profile analysis: Detection of distantly related proteins. Proc. Natl. Acad. Sci. U.S.A. 84:4355-4358.
5. Karlin, S. and Altschul, S.F. 1990. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. U.S.A. 87:2264-2268.
6. Krogh, A., Brown, M., Mian, I.S., Sjölander, K., and Haussler, D. 1994. Hidden Markov models in computational biology. Applications to protein modeling. J. Mol. Biol. 235:1501-1531.
7. Lipman, D.J., Altschul, S.F., and Kececioglu, J.D. 1989. A tool for multiple sequence alignment. Proc. Natl. Acad. Sci. U.S.A. 86:4412-4415.
8. Smith, T.F. and Waterman, M.S. 1981. Identification of common molecular subsequences. J. Mol. Biol. 147:195-197.