Abstract
Characterization and identification of recombination hotspots provide important insights into the mechanism of recombination and genome evolution. In contrast with existing sequence-based models for predicting recombination hotspots which were defined in a ORF-based manner, here, we first defined recombination hot/cold spots based on public high-resolution Spo11-oligo-seq data, then characterized them in terms of DNA sequence and epigenetic marks, and finally presented classifiers to identify hotspots. We found that, in addition to some previously discovered DNA-based features like GC-skew, recombination hotspots in yeast can also be characterized by some remarkable features associated with DNA physical properties and shape. More importantly, by using DNA-based features and several epigenetic marks, we built several classifiers to discriminate hotspots from coldspots, and found that SVM classifier performs the best with an accuracy of ∼92%, which is also the highest among the models in comparison. Feature importance analysis combined with prediction results show that epigenetic marks and variation of sequence-based features along the hotspots contribute dominantly to hotspot identification. By using incremental feature selection method, an optimal feature subset that consists of much less features was obtained without sacrificing prediction accuracy.