title: RF-DYMHC: detecting the yeast meiotic recombination hotspots and coldspots by random forest model using gapped dinucleotide composition features.
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.
authors.affiliation: State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
authors.forename: Peng
authors.initials: P
authors.lastname: Jiang
authors.forename: Haonan
authors.initials: H
authors.lastname: Wu
authors.forename: Jiawei
authors.initials: J
authors.lastname: Wei
authors.forename: Fei
authors.initials: F
authors.lastname: Sang
authors.forename: Xiao
authors.initials: X
authors.lastname: Sun
authors.forename: Zuhong
authors.initials: Z
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D003062:Codon / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D016681:Genome, Fungal / Q000235:genetics; D008540:Meiosis / Q000235:genetics*; D009711:Nucleotides / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods; D011995:Recombination, Genetic / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000166:cytology* / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
chemical_list: D003062:Codon; D009711:Nucleotides