SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform.
Identifieur interne : 000775 ( PubMed/Checkpoint ); précédent : 000774; suivant : 000776SSAW: 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
Affiliations:
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<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>
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<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>
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<affiliations><list><country><li>République populaire de Chine</li>
<li>États-Unis</li>
</country>
<region><li>Iowa</li>
<li>Virginie-Occidentale</li>
</region>
<settlement><li>Iowa City</li>
</settlement>
<orgName><li>Université de l'Iowa</li>
</orgName>
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<tree><country name="République populaire de Chine"><noRegion><name sortKey="Lin, Jie" sort="Lin, Jie" uniqKey="Lin J" first="Jie" last="Lin">Jie Lin</name>
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<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>
<name sortKey="Jiang, Bing Hua" sort="Jiang, Bing Hua" uniqKey="Jiang B" first="Bing-Hua" last="Jiang">Bing-Hua Jiang</name>
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