SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform.
Identifieur interne : 000914 ( PubMed/Curation ); précédent : 000913; suivant : 000915SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform.
Auteurs : Jie Lin [République populaire de Chine] ; Jing Wei [République populaire de Chine] ; Donald Adjeroh [États-Unis] ; Bing-Hua Jiang [États-Unis] ; Yue Jiang [République populaire de Chine]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2018.
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Abstract
Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.
DOI: 10.1186/s12859-018-2155-9
PubMed: 29720081
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wicri:level="1"><nlm:affiliation>College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108</wicri:regionArea></affiliation></author><author><name sortKey="Adjeroh, Donald" sort="Adjeroh, Donald" uniqKey="Adjeroh D" first="Donald" last="Adjeroh">Donald Adjeroh</name><affiliation wicri:level="1"><nlm:affiliation>Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, WV, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, WV</wicri:regionArea></affiliation></author><author><name sortKey="Jiang, Bing Hua" sort="Jiang, Bing Hua" uniqKey="Jiang B" first="Bing-Hua" last="Jiang">Bing-Hua Jiang</name><affiliation wicri:level="1"><nlm:affiliation>Department of Pathology, University of Iowa, Iowa city, 52242, Iowa, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathology, University of Iowa, Iowa city, 52242, Iowa</wicri:regionArea></affiliation></author><author><name sortKey="Jiang, Yue" sort="Jiang, Yue" uniqKey="Jiang Y" first="Yue" last="Jiang">Yue Jiang</name><affiliation wicri:level="1"><nlm:affiliation>College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China. yueljiang@163.com.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Computational Biology (methods)</term><term>Humans</term><term>Sequence Analysis, DNA (methods)</term><term>Sequence Analysis, Protein (methods)</term><term>Sequence Homology</term><term>Wavelet Analysis</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de regroupements</term><term>Analyse de séquence d'ADN ()</term><term>Analyse de séquence de protéine ()</term><term>Analyse en ondelettes</term><term>Biologie informatique ()</term><term>Humains</term><term>Similitude de séquences</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Sequence Analysis, DNA</term><term>Sequence Analysis, Protein</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Humans</term><term>Sequence Homology</term><term>Wavelet Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de regroupements</term><term>Analyse de séquence d'ADN</term><term>Analyse de séquence de protéine</term><term>Analyse en ondelettes</term><term>Biologie informatique</term><term>Humains</term><term>Similitude de séquences</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">29720081</PMID><DateCompleted><Year>2018</Year><Month>11</Month><Day>06</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>14</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>1</Issue><PubDate><Year>2018</Year><Month>05</Month><Day>02</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform.</ArticleTitle><Pagination><MedlinePgn>165</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-018-2155-9</ELocationID><Abstract><AbstractText Label="BACKGROUND">Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.</AbstractText><AbstractText Label="RESULTS">A new alignment-free sequence similarity analysis method, called SSAW is proposed. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). It extracts k-mers from a sequence, then maps each k-mer to a complex number field. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification.</AbstractText><AbstractText Label="CONCLUSIONS">Using two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. The running time was significantly better in most cases. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lin</LastName><ForeName>Jie</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wei</LastName><ForeName>Jing</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Adjeroh</LastName><ForeName>Donald</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, WV, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Bing-Hua</ForeName><Initials>BH</Initials><AffiliationInfo><Affiliation>Department of Pathology, University of Iowa, Iowa city, 52242, Iowa, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Yue</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China. yueljiang@163.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>P30 CA086862</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>61472082</GrantID><Agency>Chinese National Natural Science Foundation</Agency><Country>International</Country></Grant><Grant><GrantID>2014J01220</GrantID><Agency>Natural Science Foundation of Fujian Province of China</Agency><Country>International</Country></Grant><Grant><GrantID>IRTL1702</GrantID><Agency>Scientific Research Innovation Team Construction Program of Fujian Normal University</Agency><Country>International</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>05</Month><Day>02</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020539" MajorTopicYN="N">Sequence Analysis, Protein</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017385" MajorTopicYN="Y">Sequence Homology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058067" MajorTopicYN="Y">Wavelet Analysis</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Complex numbers</Keyword><Keyword MajorTopicYN="Y">Frequency domain</Keyword><Keyword 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