Journal
BRIEFINGS IN BIOINFORMATICS
Volume 21, Issue 5, Pages 1568-1580Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz123
Keywords
recombination; hotspots; machine learning; sequence analysis; web server; prediction model
Funding
- National Nature Scientific Foundation of China [61772119, 31771471, 61861036]
- Science Strength Promotion Programme of UESTC
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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.
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