SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform
Identifieur interne : 000820 ( Main/Exploration ); précédent : 000819; suivant : 000821SSAW: 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.
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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.
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DOI: 10.1186/s12859-018-2155-9
PubMed: 29720081
PubMed Central: 5930706
Affiliations:
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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</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Fuzhou</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC Bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2018">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"><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><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Zielezinski, A" uniqKey="Zielezinski A">A Zielezinski</name></author><author><name sortKey="Vinga, S" uniqKey="Vinga S">S Vinga</name></author><author><name sortKey="Almeida, J" uniqKey="Almeida J">J Almeida</name></author><author><name sortKey="Karlowski, Wm" uniqKey="Karlowski W">WM Karlowski</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vinga, S" uniqKey="Vinga S">S Vinga</name></author><author><name sortKey="Almeida, J" uniqKey="Almeida J">J Almeida</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Pratas, D" uniqKey="Pratas D">D Pratas</name></author><author><name sortKey="Silva, R M" uniqKey="Silva R">R. 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X">X Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lonard, M" uniqKey="Lonard M">M Lonard</name></author><author><name sortKey="Mouchard, L" uniqKey="Mouchard L">L Mouchard</name></author><author><name sortKey="Salson, M" uniqKey="Salson M">M Salson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fowler, J E" uniqKey="Fowler J">J. E Fowler</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yang, X" uniqKey="Yang X">X Yang</name></author><author><name sortKey="Chockalingam, Sp" uniqKey="Chockalingam S">SP Chockalingam</name></author><author><name sortKey="Aluru, S" uniqKey="Aluru S">S Aluru</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Needleman, S B" uniqKey="Needleman S">S. B Needleman</name></author><author><name sortKey="Wunsch, C D" uniqKey="Wunsch C">C. D Wunsch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wagner, R A" uniqKey="Wagner R">R. A Wagner</name></author><author><name sortKey="Fischer, M J" uniqKey="Fischer M">M. J Fischer</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><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>Pour manipuler ce document sous Unix (Dilib)
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