Chapter title |
Systematic Exploration of an Efficient Amino Acid Substitution Matrix: MIQS.
|
---|---|
Chapter number | 11 |
Book title |
Data Mining Techniques for the Life Sciences
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Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3572-7_11 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3570-3, 978-1-4939-3572-7
|
Authors |
Kentaro Tomii, Kazunori Yamada |
Editors |
Oliviero Carugo, Frank Eisenhaber |
Abstract |
Amino acid sequence comparisons to find similarities between proteins are fundamental sequence information analyses for inferring protein structure and function. In this study, we improve amino acid substitution matrices to identify distantly related proteins. We systematically sampled and benchmarked substitution matrices generated from the principal component analysis (PCA) subspace based on a set of typical existing matrices. Based on the benchmark results, we identified a region of highly sensitive matrices in the PCA subspace using kernel density estimation (KDE). Using the PCA subspace, we were able to deduce a novel sensitive matrix, called MIQS, which shows better detection performance for detecting distantly related proteins than those of existing matrices. This approach to derive an efficient amino acid substitution matrix might influence many fields of protein sequence analysis. MIQS is available at http://csas.cbrc.jp/Ssearch/ . |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 4 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 3 | 75% |
Student > Ph. D. Student | 1 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 1 | 25% |
Agricultural and Biological Sciences | 1 | 25% |
Unknown | 2 | 50% |