| Literature DB >> 31633777 |
Hui Yang1, Wuritu Yang2, Fu-Ying Dao1, Hao Lv1, Hui Ding1, Wei Chen3, Hao Lin4.
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.Entities:
Keywords: hotspots; machine learning; prediction model; recombination; sequence analysis; web server
Year: 2019 PMID: 31633777 DOI: 10.1093/bib/bbz123
Source DB: PubMed Journal: Brief Bioinform ISSN: 1467-5463 Impact factor: 11.622