Improving tRNAscan‐SE Annotation Results via Ensemble Classifiers
Identifieur interne : 001978 ( Main/Exploration ); précédent : 001977; suivant : 001979Improving tRNAscan‐SE Annotation Results via Ensemble Classifiers
Auteurs : Quan Zou [République populaire de Chine] ; Jiasheng Guo [République populaire de Chine] ; Ying Ju [République populaire de Chine] ; Meihong Wu [République populaire de Chine] ; Xiangxiang Zeng [République populaire de Chine] ; Zhiling Hong [République populaire de Chine]Source :
- Molecular Informatics [ 1868-1743 ] ; 2015-11.
Abstract
tRNAScan‐SE is a tRNA detection program that is widely used for tRNA annotation; however, the false positive rate of tRNAScan‐SE is unacceptable for large sequences. Here, we used a machine learning method to try to improve the tRNAScan‐SE results. A new predictor, tRNA‐Predict, was designed. We obtained real and pseudo‐tRNA sequences as training data sets using tRNAScan‐SE and constructed three different tRNA feature sets. We then set up an ensemble classifier, LibMutil, to predict tRNAs from the training data. The positive data set of 623 tRNA sequences was obtained from tRNAdb 2009 and the negative data set was the false positive tRNAs predicted by tRNAscan‐SE. Our in silico experiments revealed a prediction accuracy rate of 95.1 % for tRNA‐Predict using 10‐fold cross‐validation. tRNA‐Predict was developed to distinguish functional tRNAs from pseudo‐tRNAs rather than to predict tRNAs from a genome‐wide scan. However, tRNA‐Predict can work with the output of tRNAscan‐SE, which is a genome‐wide scanning method, to improve the tRNAscan‐SE annotation results. The tRNA‐Predict web server is accessible at http://datamining.xmu.edu.cn/∼gjs/tRNA‐Predict.
Url:
DOI: 10.1002/minf.201500031
Affiliations:
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Zeng</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen 361005</wicri:regionArea><wicri:noRegion>Xiamen 361005</wicri:noRegion></affiliation></author><author><name sortKey="Hong, Zhiling" sort="Hong, Zhiling" uniqKey="Hong Z" first="Zhiling" last="Hong">Zhiling Hong</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Software School, Xiamen University, Xiamen 361005</wicri:regionArea><wicri:noRegion>Xiamen 361005</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">République populaire de Chine</country></affiliation><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Correspondence address: Software School, Xiamen University, Xiamen 361005</wicri:regionArea><wicri:noRegion>Xiamen 361005</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Molecular Informatics</title><title level="j" type="alt">MOLECULAR INFORMATICS</title><idno type="ISSN">1868-1743</idno><idno type="eISSN">1868-1751</idno><imprint><biblScope unit="vol">34</biblScope><biblScope unit="issue">11‐12</biblScope><biblScope unit="page" from="761">761</biblScope><biblScope unit="page" to="770">770</biblScope><biblScope unit="page-count">10</biblScope><publisher>WILEY‐VCH Verlag</publisher><pubPlace>Weinheim</pubPlace><date type="published" when="2015-11">2015-11</date></imprint><idno type="ISSN">1868-1743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1868-1743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">tRNAScan‐SE is a tRNA detection program that is widely used for tRNA annotation; however, the false positive rate of tRNAScan‐SE is unacceptable for large sequences. Here, we used a machine learning method to try to improve the tRNAScan‐SE results. A new predictor, tRNA‐Predict, was designed. We obtained real and pseudo‐tRNA sequences as training data sets using tRNAScan‐SE and constructed three different tRNA feature sets. We then set up an ensemble classifier, LibMutil, to predict tRNAs from the training data. The positive data set of 623 tRNA sequences was obtained from tRNAdb 2009 and the negative data set was the false positive tRNAs predicted by tRNAscan‐SE. Our in silico experiments revealed a prediction accuracy rate of 95.1 % for tRNA‐Predict using 10‐fold cross‐validation. tRNA‐Predict was developed to distinguish functional tRNAs from pseudo‐tRNAs rather than to predict tRNAs from a genome‐wide scan. However, tRNA‐Predict can work with the output of tRNAscan‐SE, which is a genome‐wide scanning method, to improve the tRNAscan‐SE annotation results. The tRNA‐Predict web server is accessible at http://datamining.xmu.edu.cn/∼gjs/tRNA‐Predict.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Tianjin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Zou, Quan" sort="Zou, Quan" uniqKey="Zou Q" first="Quan" last="Zou">Quan Zou</name></noRegion><name sortKey="Guo, Jiasheng" sort="Guo, Jiasheng" uniqKey="Guo J" first="Jiasheng" last="Guo">Jiasheng Guo</name><name sortKey="Hong, Zhiling" sort="Hong, Zhiling" uniqKey="Hong Z" first="Zhiling" last="Hong">Zhiling Hong</name><name sortKey="Hong, Zhiling" sort="Hong, Zhiling" uniqKey="Hong Z" first="Zhiling" last="Hong">Zhiling Hong</name><name sortKey="Hong, Zhiling" sort="Hong, Zhiling" uniqKey="Hong Z" first="Zhiling" last="Hong">Zhiling Hong</name><name sortKey="Ju, Ying" sort="Ju, Ying" uniqKey="Ju Y" first="Ying" last="Ju">Ying Ju</name><name sortKey="Wu, Meihong" sort="Wu, Meihong" uniqKey="Wu M" first="Meihong" last="Wu">Meihong Wu</name><name sortKey="Zeng, Xiangxiang" sort="Zeng, Xiangxiang" uniqKey="Zeng X" first="Xiangxiang" last="Zeng">Xiangxiang Zeng</name><name sortKey="Zou, Quan" sort="Zou, Quan" uniqKey="Zou Q" first="Quan" last="Zou">Quan Zou</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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