Record Details

Automatic prediction of non-coding RNA genes in prokaryotes based on compositional statistics

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Automatic prediction of non-coding RNA genes in prokaryotes based on compositional statistics
 
Creator Tong, Hao
Guo, Feng-Biao
Ye, Yuan-Nong
 
Subject Automatic gene prediction
Non-coding RNA genes
Sulfolobus solfataricus
E. coli
Nucleotide composition
Support vector machine
 
Description 416-421
Although non-coding RNA (ncRNA) genes do not encode
proteins, they play vital roles in cells by producing functionally important
RNAs. In this paper, we present a novel method for predicting ncRNA genes based
on compositional features extracted directly from gene sequences. Our method
consists of two Support Vector Machine (SVM) models — Codon model which uses
codon usage features derived from ncRNA genes and protein-coding genes and Kmer
model which utilizes features of nucleotide and dinucleotide frequency
extracted respectively from ncRNA genes and randomly chosen genome sequences.
The 10-fold cross-validation accuracy for the two models is found to be 92% and
91%, respectively. Thus, we could make an automatic prediction of ncRNA genes
in one genome without manual filtration of protein-coding genes. After applying
our method in Sulfolobus solfataricus
genome, 25 prediction results have been generated according to 25 cut-off
pairs. We have also applied the approach in E.
coli and found our results comparable to those of previous studies. In
general, our method enables automatic identification of ncRNA genes in newly
sequenced prokaryotic genomes. Datasets and program code used in this work are
available at http://cobi.uestc.edu.cn/resource/SS_ncRNA/
 
Date 2011-12-23T10:29:01Z
2011-12-23T10:29:01Z
2011-12
 
Type Article
 
Identifier 0975-0959 (Online); 0301-1208 (Print)
http://hdl.handle.net/123456789/13251
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source IJBB Vol.48(6) [December 2011]