Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique

DNA replication is a key step to maintain the continuity of genetic information between parental generation and offspring. The initiation site of DNA replication, also called origin of replication (ORI), plays an extremely important role in the basic biochemical process. Thus, rapidly and effectivel...

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Published inBioinformatics (Oxford, England) Vol. 35; no. 12; pp. 2075 - 2083
Main Authors Dao, Fu-Ying, Lv, Hao, Wang, Fang, Feng, Chao-Qin, Ding, Hui, Chen, Wei, Lin, Hao
Format Journal Article
LanguageEnglish
Published England 01.06.2019
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Summary:DNA replication is a key step to maintain the continuity of genetic information between parental generation and offspring. The initiation site of DNA replication, also called origin of replication (ORI), plays an extremely important role in the basic biochemical process. Thus, rapidly and effectively identifying the location of ORI in genome will provide key clues for genome analysis. Although biochemical experiments could provide detailed information for ORI, it requires high experimental cost and long experimental period. As good complements to experimental techniques, computational methods could overcome these disadvantages. Thus, in this study, we developed a predictor called iORI-PseKNC2.0 to identify ORIs in the Saccharomyces cerevisiae genome based on sequence information. The PseKNC including 90 physicochemical properties was proposed to formulate ORI and non-ORI samples. In order to improve the accuracy, a two-step feature selection was proposed to exclude redundant and noise information. As a result, the overall success rate of 88.53% was achieved in the 5-fold cross-validation test by using support vector machine. Based on the proposed model, a user-friendly webserver was established and can be freely accessed at http://lin-group.cn/server/iORI-PseKNC2.0. The webserver will provide more convenience to most of wet-experimental scholars.
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ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/bty943