Using Gaussian model to improve biological sequence comparison
Identifieur interne : 002685 ( Main/Exploration ); précédent : 002684; suivant : 002686Using Gaussian model to improve biological sequence comparison
Auteurs : Qi Dai [République populaire de Chine] ; Xiaoqing Liu [République populaire de Chine] ; Lihua Li [République populaire de Chine] ; Yuhua Yao [République populaire de Chine] ; Bin Han [République populaire de Chine] ; Lei Zhu [République populaire de Chine]Source :
- Journal of Computational Chemistry [ 0192-8651 ] ; 2010-01-30.
English descriptors
- Teeft :
- 3word frequencies, Accession number, Background information, Best combination, Bioinformatics, Biological function, Biological sequence, Biological sequence analysis, Biological sequence comparison, Biological sequence comparison table, Biological sequences, Brief bioinform, Chem, Complete coronavirus genomes, Complete sequence, Comput, Comput chem, Computational, Computational chemistry, Computational chemistry figure, Computational load, Computational methods, Coronavirus, Coronaviruses, Cumulative distribution function, Cumulative frequency, Data sets, Density function, Different expression levels, Different threshold values, Distance matrix, Distribution function, Distribution information, Dnor, Empirical distribution function, Equal proportions, Euclidean distance mahalanobis distance, Evolutionary relations, Frequency vectors, Function prediction, Functional similarity, Gaussian, Gaussian distribution, Gaussian model, Genome, Hangzhou dianzi university, Hslipas, Hslipas sequence, Human mrna, Likelihood function, Lilliefors test, Lipase, Lipoprotein, Lipoprotein lipase, Main operations, Many methods, Maximum discrepancy, Measure dnor, More information, Mrna, Mrna sequence, Normal distribution, Normal probability plot, Normality, Novel method, Null hypothesis, Numerous methods, Other coronaviruses, Other measures, Parameter values, Pattern recogn, Phylogenetic, Phylogenetic analysis, Phylogenetic relationships, Phylogenetic tree, Phylogenetic tree construction, Population variance, Probability density function, Query sequence, Random sequences, Sars, Sars coronavirus, Separate tests, Sequence comparison, Sequence comparison method, Similar sequences, Similarity degree, Similarity measure names, Similarity measures, Similarity search, Standard deviation, Straight line, Test statistic, True positives, Variance, Wang, Wiley periodicals, Word length.
Abstract
One of the major tasks in biological sequence analysis is to compare biological sequences, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Numerous efficient methods have been developed for sequence comparison, but challenges remain. In this article, we proposed a novel method to compare biological sequences based on Gaussian model. Instead of comparing the frequencies of k‐words in biological sequences directly, we considered the k‐word frequency distribution under Gaussian model which gives the different expression levels of k‐words. The proposed method was tested by similarity search, evaluation on functionally related genes, and phylogenetic analysis. The performance of our method was further compared with alignment‐based and alignment‐free methods. The results demonstrate that Gaussian model provides more information about k‐word frequencies and improves the efficiency of sequence comparison. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010
Url:
DOI: 10.1002/jcc.21322
Affiliations:
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Chine</country><wicri:regionArea>College of Life Sciences, Zhejiang Sci‐Tech University, Hangzhou 310018</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName></affiliation></author><author><name sortKey="Han, Bin" sort="Han, Bin" uniqKey="Han B" first="Bin" last="Han">Bin Han</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute for Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName></affiliation></author><author><name sortKey="Zhu, Lei" sort="Zhu, Lei" uniqKey="Zhu L" first="Lei" last="Zhu">Lei Zhu</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute for Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Journal of Computational Chemistry</title><title level="j" type="alt">JOURNAL OF COMPUTATIONAL CHEMISTRY</title><idno type="ISSN">0192-8651</idno><idno type="eISSN">1096-987X</idno><imprint><biblScope unit="vol">31</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="351">351</biblScope><biblScope unit="page" to="361">361</biblScope><biblScope unit="page-count">11</biblScope><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2010-01-30">2010-01-30</date></imprint><idno type="ISSN">0192-8651</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0192-8651</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>3word frequencies</term><term>Accession number</term><term>Background information</term><term>Best combination</term><term>Bioinformatics</term><term>Biological function</term><term>Biological sequence</term><term>Biological sequence analysis</term><term>Biological sequence comparison</term><term>Biological sequence comparison table</term><term>Biological sequences</term><term>Brief bioinform</term><term>Chem</term><term>Complete coronavirus genomes</term><term>Complete sequence</term><term>Comput</term><term>Comput chem</term><term>Computational</term><term>Computational chemistry</term><term>Computational chemistry figure</term><term>Computational load</term><term>Computational methods</term><term>Coronavirus</term><term>Coronaviruses</term><term>Cumulative distribution function</term><term>Cumulative frequency</term><term>Data sets</term><term>Density function</term><term>Different expression levels</term><term>Different threshold values</term><term>Distance matrix</term><term>Distribution function</term><term>Distribution information</term><term>Dnor</term><term>Empirical distribution function</term><term>Equal proportions</term><term>Euclidean distance mahalanobis distance</term><term>Evolutionary relations</term><term>Frequency vectors</term><term>Function prediction</term><term>Functional similarity</term><term>Gaussian</term><term>Gaussian distribution</term><term>Gaussian model</term><term>Genome</term><term>Hangzhou dianzi university</term><term>Hslipas</term><term>Hslipas sequence</term><term>Human mrna</term><term>Likelihood function</term><term>Lilliefors test</term><term>Lipase</term><term>Lipoprotein</term><term>Lipoprotein lipase</term><term>Main operations</term><term>Many methods</term><term>Maximum discrepancy</term><term>Measure dnor</term><term>More information</term><term>Mrna</term><term>Mrna sequence</term><term>Normal distribution</term><term>Normal probability plot</term><term>Normality</term><term>Novel method</term><term>Null hypothesis</term><term>Numerous methods</term><term>Other coronaviruses</term><term>Other measures</term><term>Parameter values</term><term>Pattern recogn</term><term>Phylogenetic</term><term>Phylogenetic analysis</term><term>Phylogenetic relationships</term><term>Phylogenetic tree</term><term>Phylogenetic tree construction</term><term>Population variance</term><term>Probability density function</term><term>Query sequence</term><term>Random sequences</term><term>Sars</term><term>Sars coronavirus</term><term>Separate tests</term><term>Sequence comparison</term><term>Sequence comparison method</term><term>Similar sequences</term><term>Similarity degree</term><term>Similarity measure names</term><term>Similarity measures</term><term>Similarity search</term><term>Standard deviation</term><term>Straight line</term><term>Test statistic</term><term>True positives</term><term>Variance</term><term>Wang</term><term>Wiley periodicals</term><term>Word length</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">One of the major tasks in biological sequence analysis is to compare biological sequences, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Numerous efficient methods have been developed for sequence comparison, but challenges remain. In this article, we proposed a novel method to compare biological sequences based on Gaussian model. Instead of comparing the frequencies of k‐words in biological sequences directly, we considered the k‐word frequency distribution under Gaussian model which gives the different expression levels of k‐words. The proposed method was tested by similarity search, evaluation on functionally related genes, and phylogenetic analysis. The performance of our method was further compared with alignment‐based and alignment‐free methods. The results demonstrate that Gaussian model provides more information about k‐word frequencies and improves the efficiency of sequence comparison. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Zhejiang</li></region><settlement><li>Hangzhou</li></settlement></list><tree><country name="République populaire de Chine"><region name="Zhejiang"><name sortKey="Dai, Qi" sort="Dai, Qi" uniqKey="Dai Q" first="Qi" last="Dai">Qi Dai</name></region><name sortKey="Han, Bin" sort="Han, Bin" uniqKey="Han B" first="Bin" last="Han">Bin Han</name><name sortKey="Li, Lihua" sort="Li, Lihua" uniqKey="Li L" first="Lihua" last="Li">Lihua Li</name><name sortKey="Li, Lihua" sort="Li, Lihua" uniqKey="Li L" first="Lihua" last="Li">Lihua Li</name><name sortKey="Li, Lihua" sort="Li, Lihua" uniqKey="Li L" first="Lihua" last="Li">Lihua Li</name><name sortKey="Liu, Xiaoqing" sort="Liu, Xiaoqing" uniqKey="Liu X" first="Xiaoqing" last="Liu">Xiaoqing Liu</name><name sortKey="Yao, Yuhua" sort="Yao, Yuhua" uniqKey="Yao Y" first="Yuhua" last="Yao">Yuhua Yao</name><name sortKey="Zhu, Lei" sort="Zhu, Lei" uniqKey="Zhu L" first="Lei" last="Zhu">Lei Zhu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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