A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae. (21st October 2019)

• Record Type: Journal Article
• Authors: Yang, Hui; Yang, Wuritu; Dao, Fu-Ying; Lv, Hao; Ding, Hui; Chen, Wei; Lin, Hao
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 5(2020)
• Page Start: 1568
• Page End: 1580
• Publication Date: 2019-10-21
• Subjects: recombination -- hotspots -- machine learning -- sequence analysis -- web server -- prediction model; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz123
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae . These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae . The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot.