Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features.

ABSTRACT:

Background

Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete.

Methods

In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direc

SUBMITTER: Luo L 

PROVIDER: S-EPMC4830532 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature