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{"title":"Selecting the Right Similarity-Scoring Matrix","authors":"William R. Pearson","doi":"10.1002/0471250953.bi0305s43","DOIUrl":null,"url":null,"abstract":"<p>Protein sequence similarity searching programs like BLASTP, SSEARCH, and FASTA use scoring matrices that are designed to identify distant evolutionary relationships (BLOSUM62 for BLAST, BLOSUM50 for SSEARCH and FASTA). Different similarity scoring matrices are most effective at different evolutionary distances. “Deep” scoring matrices like BLOSUM62 and BLOSUM50 target alignments with 20% to 30% identity, while “shallow” scoring matrices (e.g., VTML10 to VTML80) target alignments that share 90% to 50% identity, reflecting much less evolutionary change. While “deep” matrices provide very sensitive similarity searches, they also require longer sequence alignments and can sometimes produce alignment overextension into nonhomologous regions. Shallower scoring matrices are more effective when searching for short protein domains, or when the goal is to limit the scope of the search to sequences that are likely to be orthologous between recently diverged organisms. Likewise, in DNA searches, the match and mismatch parameters set evolutionary look-back times and domain boundaries. In this unit, we will discuss the theoretical foundations that drive practical choices of protein and DNA similarity scoring matrices and gap penalties. Deep scoring matrices (BLOSUM62 and BLOSUM50) should be used for sensitive searches with full-length protein sequences, but short domains or restricted evolutionary look-back require shallower scoring matrices. <i>Curr. Protoc. Bioinform</i>. 43:3.5.1-3.5.9. © 2013 by John Wiley & Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"43 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/0471250953.bi0305s43","citationCount":"113","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current protocols in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/0471250953.bi0305s43","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
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Abstract
Protein sequence similarity searching programs like BLASTP, SSEARCH, and FASTA use scoring matrices that are designed to identify distant evolutionary relationships (BLOSUM62 for BLAST, BLOSUM50 for SSEARCH and FASTA). Different similarity scoring matrices are most effective at different evolutionary distances. “Deep” scoring matrices like BLOSUM62 and BLOSUM50 target alignments with 20% to 30% identity, while “shallow” scoring matrices (e.g., VTML10 to VTML80) target alignments that share 90% to 50% identity, reflecting much less evolutionary change. While “deep” matrices provide very sensitive similarity searches, they also require longer sequence alignments and can sometimes produce alignment overextension into nonhomologous regions. Shallower scoring matrices are more effective when searching for short protein domains, or when the goal is to limit the scope of the search to sequences that are likely to be orthologous between recently diverged organisms. Likewise, in DNA searches, the match and mismatch parameters set evolutionary look-back times and domain boundaries. In this unit, we will discuss the theoretical foundations that drive practical choices of protein and DNA similarity scoring matrices and gap penalties. Deep scoring matrices (BLOSUM62 and BLOSUM50) should be used for sensitive searches with full-length protein sequences, but short domains or restricted evolutionary look-back require shallower scoring matrices. Curr. Protoc. Bioinform . 43:3.5.1-3.5.9. © 2013 by John Wiley & Sons, Inc.
选择正确的相似度评分矩阵
蛋白质序列相似性搜索程序,如BLASTP、SSEARCH和FASTA,使用评分矩阵来识别远距离进化关系(BLASTP为BLOSUM62, SSEARCH和FASTA为BLOSUM50)。不同的相似性评分矩阵在不同的进化距离下最有效。像BLOSUM62和BLOSUM50这样的“深度”评分矩阵的目标序列具有20%到30%的同同性,而“浅”评分矩阵(例如,VTML10到VTML80)的目标序列具有90%到50%的同同性,反映的进化变化要少得多。虽然“深度”矩阵提供了非常敏感的相似性搜索,但它们也需要更长的序列比对,并且有时会产生比对过度延伸到非同源区域。当搜索短蛋白结构域时,或者当目标是将搜索范围限制在最近分化的生物体之间可能是同源的序列时,较浅的评分矩阵更有效。同样,在DNA搜索中,匹配和不匹配参数设置了进化回顾时间和域边界。在本单元中,我们将讨论驱动蛋白质和DNA相似性评分矩阵和间隙处罚的实际选择的理论基础。深度评分矩阵(BLOSUM62和BLOSUM50)应该用于全长蛋白序列的敏感搜索,但短结构域或受限的进化回顾需要较浅的评分矩阵。咕咕叫。Protoc。Bioinform 43:3.5.1-3.5.9。©2013 by John Wiley &儿子,Inc。
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