ArticleNucleic acids research2007
RF-DYMHC: detecting the yeast meiotic recombination hotspots and coldspots by random forest model using gapped dinucleotide composition features.
Article in Nucleic acids research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots inFrontiers in genetics · 2021Article
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance.Scientific reports · 2016Article
- Recombination spot identification Based on gapped k-mers.Scientific reports · 2016Article
- Review
- SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis.BMC bioinformatics · 2014Article
- Analysis of biological features associated with meiotic recombination hot and cold spots in Saccharomyces cerevisiae.PloS one · 2011Article
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Authors and funding
6 authors.
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Abstract
In the yeast, meiotic recombination is initiated by double-strand DNA breaks (DSBs) which occur at relatively high frequencies in some genomic regions (hotspots) and relatively low frequencies in others (coldspots). Although observations concerning individual hot/cold spots have given clues as to the mechanism of recombination initiation, the prediction of hot/cold spots from DNA sequence information is a challenging task. In this article, we introduce a random forest (RF) prediction model to detect recombination hot/cold spots from yeast genome. The out-of-bag (OOB) estimation of the model indicated that the RF classifier achieved high prediction performance with 82.05% total accuracy and 0.638 Mattew's correlation coefficient (MCC) value. Compared with an alternative machine-learning algorithm, support vector machine (SVM), the RF method outperforms it in both sensitivity and specificity. The prediction model is implemented as a web server (RF-DYMHC) and it is freely available at http://www.bioinf.seu.edu.cn/Recombination/rf_dymhc.htm. Given a yeast genome and prediction parameters (RI-value and non-overlapping window scan size), the program reports the predicted hot/cold spots and marks them in color.
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