Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition

Abstract

DNase I Hypersensitive sites (DHS) are the regions that are sensitive to cleavage by the DNase I enzyme. Knowledge regarding these sites is helpful for decryption of the functions of non-coding genomic regions. Various biological processes need its intervention. Traditional techniques are laborious and time-consuming to predict DHS sites. Particularly, with the avalanche of DNA sequences generated in the post-genomic era, the development of computational approaches is highly essential to precisely and timely predict DHS sites in DNA sequences. The existing feature encoding schemes such as pseudo dinucleotide composition, pseudo trinucleotide composition etc. cannot effectively express features from DHS sequences. In the current study, we proposed a new computational technique to predict DHS sites which uses Un-biased Pseudo Trinucleotide Composition (Unb-PseTNC) strategy to extract nominal descriptors from the DHS benchmark dataset and avoid biasness among the classes during the classification phase. Several classification algorithm including Support vector machine (SVM), probabilistic neural network and k-nearest neighbor are employed to classify extracted features. It was observed that SVM in conjunction with Unb-PseTNC outperforms other techniques. By comparing with other existing predictors, it was perceived that our proposed method achieved higher prediction rates by applying rigorous jackknife test. This indicates that the proposed model will become a useful tool to predict DHS sites and can also be utilized for in-depth study of DNA and genome analysis.