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Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/97015
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| Title: | Improving the alignment quality of consistency based aligners with an evaluation function using synonymous protein words |
| Authors: | 張家銘
Chang, Jia-Ming
Hsu*, Wen-Lian
Sung*, Ting-Yi
Notredame, Cédric
Lin, Hsin-Nan |
| Contributors: | 資科系 |
| Date: | 2011-12 |
| Issue Date: | 2016-05-30 17:24:57 (UTC+8) |
| Abstract: | 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/. |
| Relation: | PLoS One, Vol.6, No.12, pp.e27872 |
| Data Type: | article |
| DOI 連結: | http://dx.doi.org/10.1371/journal.pone.0027872 |
| DOI: | 10.1371/journal.pone.0027872 |
| Appears in Collections: | [資訊科學系] 期刊論文
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Size | Format | |
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| Improving.pdf | | 387Kb | Adobe PDF2 | 606 | View/Open |
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