Paper Info
Title
Revisiting amino acid substitution matrices for identifying distantly related proteins.
Abstract
Motivation: Although many amino acid substitution matrices have been developed, it has not been well understood which is the best for similarity searches, especially for remote homology detection. Therefore, we collected information related to existing matrices, condensed it and derived a novel matrix that can detect more remote homology than ever. Results: Using principal component analysis with existing matrices and benchmarks, we developed a novel matrix, which we designate as MIQS. The detection performance of MIQS is validated and compared with that of existing general purpose matrices using SSEARCH with optimized gap penalties for each matrix. Results show that MIQS is able to detect more remote homology than the existing matrices on an independent dataset. In addition, the performance of our developed matrix was superior to that of CS-BLAST, which was a novel similarity search method with no amino acid matrix. We also evaluated the alignment quality of matrices and methods, which revealed that MIQS shows higher alignment sensitivity than that with the existing matrix series and CS-BLAST. Fundamentally, these results are expected to constitute good proof of the availability and/or importance of amino acid matrices in sequence analysis. Moreover, with our developed matrix, sophisticated similarity search methods such as sequence-profile and profile-profile comparison methods can be improved further.
Year
DOI
Venue
2014
10.1093/bioinformatics/btt694
BIOINFORMATICS
Field
DocType
Volume
Sequence alignment,Data mining,General purpose,Amino acid,Computer science,Matrix (mathematics),Bioinformatics,Nearest neighbor search,Principal component analysis,Sequence analysis
Journal
30
Issue
ISSN
Citations 
3
1367-4803
11
PageRank 
References 
Authors
0.57
21
2
Name
Order
Citations
PageRank
Kazunori Yamada1264.77
Kentaro Tomii2635.44