De novo computational prediction of non-coding RNA genes in prokaryotic genomes.

ABSTRACT:

Motivation

The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.

Results

We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying window

SUBMITTER: Tran TT 

PROVIDER: S-EPMC2773258 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature