12112703

Source:http://linkedlifedata.com/resource/pubmed/id/12112703

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0002045, umls-concept:C0205157, umls-concept:C0220825, umls-concept:C0597198, umls-concept:C1334043, umls-concept:C1511790, umls-concept:C1519249, umls-concept:C1707455
pubmed:issue	2
pubmed:dateCreated	2002-7-11
pubmed:abstractText	A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda approximately 1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties.
pubmed:grant	http://linkedlifedata.com/resource/pubmed/grant/LM0577
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/8700181
pubmed:citationSubset	IM
pubmed:chemical	http://linkedlifedata.com/resource/pubmed/chemical/Proteins
pubmed:status	MEDLINE
pubmed:month	Aug
pubmed:issn	1097-0134
pubmed:author	pubmed-author:GoldsteinRichard ARA, pubmed-author:KannMaricel GMG
pubmed:copyrightInfo	Copyright 2002 Wiley-Liss, Inc.
pubmed:issnType	Electronic
pubmed:day	1
pubmed:volume	48
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	367-76
pubmed:dateRevised	2007-11-14
pubmed:meshHeading	pubmed-meshheading:12112703-Algorithms, pubmed-meshheading:12112703-Proteins, pubmed-meshheading:12112703-Sensitivity and Specificity, pubmed-meshheading:12112703-Sequence Alignment, pubmed-meshheading:12112703-Sequence Analysis, Protein, pubmed-meshheading:12112703-Sequence Homology, Amino Acid
pubmed:year	2002
pubmed:articleTitle	Performance evaluation of a new algorithm for the detection of remote homologs with sequence comparison.
pubmed:affiliation	Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1055, USA.
pubmed:publicationType	Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Evaluation Studies