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題名 Improving the Alignment Quality of Consistency Based Aligners with an Evaluation Function Using Synonymous Protein Words
作者 Lin, Hsin-Nan;Notredame, Cédric;Chang, Jia-Ming;Sung, Ting-Yi;Hsu, Wen-Lian
張家銘
貢獻者 資科系
日期 2011
上傳時間 27-四月-2016 15:30:13 (UTC+8)
摘要 Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.
關聯 PLoS One, 6(12), e27872
資料類型 article
DOI http://dx.doi.org/10.1371/journal.pone.0027872
dc.contributor 資科系
dc.creator (作者) Lin, Hsin-Nan;Notredame, Cédric;Chang, Jia-Ming;Sung, Ting-Yi;Hsu, Wen-Lian
dc.creator (作者) 張家銘zh_TW
dc.date (日期) 2011
dc.date.accessioned 27-四月-2016 15:30:13 (UTC+8)-
dc.date.available 27-四月-2016 15:30:13 (UTC+8)-
dc.date.issued (上傳時間) 27-四月-2016 15:30:13 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/86640-
dc.description.abstract (摘要) Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.
dc.format.extent 396529 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) PLoS One, 6(12), e27872
dc.title (題名) Improving the Alignment Quality of Consistency Based Aligners with an Evaluation Function Using Synonymous Protein Words
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1371/journal.pone.0027872
dc.doi.uri (DOI) http://dx.doi.org/10.1371/journal.pone.0027872