SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform
Identifieur interne : 000473 ( Pmc/Checkpoint ); précédent : 000472; suivant : 000474SSAW: 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 ; Yue Jiang [République populaire de Chine]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2018.
Abstract
Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.
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
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.
Url:
DOI: 10.1186/s12859-018-2155-9
PubMed: 29720081
PubMed Central: 5930706
Affiliations:
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.</p>
</sec>
<sec><title>Results</title>
<p>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 <italic>k</italic>
-mers from a sequence, then maps each <italic>k</italic>
-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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id>
<journal-title-group><journal-title>BMC Bioinformatics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2105</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">29720081</article-id>
<article-id pub-id-type="pmc">5930706</article-id>
<article-id pub-id-type="publisher-id">2155</article-id>
<article-id pub-id-type="doi">10.1186/s12859-018-2155-9</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Lin</surname>
<given-names>Jie</given-names>
</name>
<address><email>linjie891@163.com</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wei</surname>
<given-names>Jing</given-names>
</name>
<address><email>JingWeixy2012@163.com</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Adjeroh</surname>
<given-names>Donald</given-names>
</name>
<address><email>Donald.Adjeroh@mail.wvu.edu</email>
</address>
<xref ref-type="aff" rid="Aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Jiang</surname>
<given-names>Bing-Hua</given-names>
</name>
<address><email>binghjiang@yahoo.com</email>
</address>
<xref ref-type="aff" rid="Aff3">3</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Jiang</surname>
<given-names>Yue</given-names>
</name>
<address><email>yueljiang@163.com</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<aff id="Aff1"><label>1</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9271 2478</institution-id>
<institution-id institution-id-type="GRID">grid.411503.2</institution-id>
<institution>College of Mathematics and Informatics, Fujian Normal University,</institution>
</institution-wrap>
Fuzhou, 350108 People’s Republic of China</aff>
<aff id="Aff2"><label>2</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2156 6140</institution-id>
<institution-id institution-id-type="GRID">grid.268154.c</institution-id>
<institution>Lane Department of Computer Science and Electrical Engineering, West Virginia University,</institution>
</institution-wrap>
Morgantown, 26506 WV USA</aff>
<aff id="Aff3"><label>3</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1936 8294</institution-id>
<institution-id institution-id-type="GRID">grid.214572.7</institution-id>
<institution>Department of Pathology, University of Iowa,</institution>
</institution-wrap>
Iowa city, 52242 Iowa USA</aff>
</contrib-group>
<pub-date pub-type="epub"><day>2</day>
<month>5</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>2</day>
<month>5</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="collection"><year>2018</year>
</pub-date>
<volume>19</volume>
<elocation-id>165</elocation-id>
<history><date date-type="received"><day>19</day>
<month>11</month>
<year>2017</year>
</date>
<date date-type="accepted"><day>11</day>
<month>4</month>
<year>2018</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s) 2018</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.</p>
</sec>
<sec><title>Results</title>
<p>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 <italic>k</italic>
-mers from a sequence, then maps each <italic>k</italic>
-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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd><italic>k</italic>
-mers</kwd>
<kwd>Wavelet transform</kwd>
<kwd>Complex numbers</kwd>
<kwd>Sequence similarity</kwd>
<kwd>Frequency domain</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution>Chinese National Natural Science Foundation</institution>
</funding-source>
<award-id>61472082</award-id>
</award-group>
</funding-group>
<funding-group><award-group><funding-source><institution>Natural Science Foundation of Fujian Province of China</institution>
</funding-source>
<award-id>2014J01220</award-id>
</award-group>
</funding-group>
<funding-group><award-group><funding-source><institution>Scientific Research Innovation Team Construction Program of Fujian Normal University </institution>
</funding-source>
<award-id>IRTL1702</award-id>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2018</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>République populaire de Chine</li>
<li>États-Unis</li>
</country>
<region><li>Virginie-Occidentale</li>
</region>
</list>
<tree><noCountry><name sortKey="Jiang, Bing Hua" sort="Jiang, Bing Hua" uniqKey="Jiang B" first="Bing-Hua" last="Jiang">Bing-Hua Jiang</name>
</noCountry>
<country name="République populaire de Chine"><noRegion><name sortKey="Lin, Jie" sort="Lin, Jie" uniqKey="Lin J" first="Jie" last="Lin">Jie Lin</name>
</noRegion>
<name sortKey="Jiang, Yue" sort="Jiang, Yue" uniqKey="Jiang Y" first="Yue" last="Jiang">Yue Jiang</name>
<name sortKey="Wei, Jing" sort="Wei, Jing" uniqKey="Wei J" first="Jing" last="Wei">Jing Wei</name>
</country>
<country name="États-Unis"><region name="Virginie-Occidentale"><name sortKey="Adjeroh, Donald" sort="Adjeroh, Donald" uniqKey="Adjeroh D" first="Donald" last="Adjeroh">Donald Adjeroh</name>
</region>
</country>
</tree>
</affiliations>
</record>
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