Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae

Characterization and accurate prediction of recombination hotspots and coldspots have crucial implications for the mechanism of recombination. Several models have predicted recombination hot/cold spots successfully, but there is still much room for improvement. We present a novel classifier in which k-mer frequency, physical and thermodynamic properties of DNA sequences are incorporated in the form of weighted features. Applying the classifier to recombination hot/cold ORFs in Saccharomyces cerevisiae, we achieved an accuracy of 90%, which is ~5% higher than existing methods, such as iRSpot-PseDNC, IDQD and Random Forest. The model also predicted non-ORF recombination hot/cold spots sequences in S. cerevisiae with high accuracy. A broad applicability of the model in the field of classification is expected.