Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions.

Pengmian Feng,Nan Liu,Ning Jiang

doi:10.1155/2014/740506

Abstract

DNase I hypersensitive sites (DHS) associated with a wide variety of regulatory DNA elements. Knowledge about the locations of DHS is helpful for deciphering the function of noncoding genomic regions. With the acceleration of genome sequences in the postgenomic age, it is highly desired to develop cost-effective computational methods to identify DHS. In the present work, a support vector machine based model was proposed to identify DHS by using the pseudo dinucleotide composition. In the jackknife test, the proposed model obtained an accuracy of 83%, which is competitive with that of the existing method. This result suggests that the proposed model may become a useful tool for DHS identifications.

Highlights

DNase I hypersensitive sites (DHS) are regions of chromatin which are sensitive to cleavage by the DNase I enzyme
By analyzing the dinucleotide composition of DHS and non-DHS sequences, we found that the frequency of CC, CG, GC, and GG is higher in DHS sequences, while the frequency of the remaining dinucleotides is higher in non-DHS (Figure 1)
This is selfevident as to why the pseudo dinucleotide composition was used for the current case

Summary

Introduction

DNase I hypersensitive sites (DHS) are regions of chromatin which are sensitive to cleavage by the DNase I enzyme. Since the discovery of DHSs in 1980s [1], they have been used as markers of regulatory DNA regions. These specific regions are generally nucleosome-free and associate with a wide variety of genomic regulatory elements, such as promoters, enhancers, insulators, silencers, and suppressors [2,3,4]. Mapping of DHS has become an effective approach for discovering functional DNA elements from the noncoding sequences. The traditional Southern blotting technique is a gold-standard approach for identifying DHS, obtaining information from Southern blot approach is a tricky, timeconsuming, and inaccurate task [5]. Computational models will be an important complement to experimental techniques for identifying DHS

Methods

Results

Conclusion