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Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae



Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae



Journal of Theoretical Biology 382: 15-22



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.

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Accession: 059222746

Download citation: RISBibTeXText

PMID: 26141645

DOI: 10.1016/j.jtbi.2015.06.030


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