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Bioinformatics. 2010 Mar 1;26(5):610-6. doi: 10.1093/bioinformatics/btp680. Epub 2009 Dec 16.

RNAsnoop: efficient target prediction for H/ACA snoRNAs.

Author information

  • 1Institute for Theoretical Chemistry, University of Vienna, W√§hringerstrasse 17, A-1090 Vienna, Austria. htafer@tbi.univie.ac.at

Abstract

MOTIVATION:

Small nucleolar RNAs are an abundant class of non-coding RNAs that guide chemical modifications of rRNAs, snRNAs and some mRNAs. In the case of many 'orphan' snoRNAs, the targeted nucleotides remain unknown, however. The box H/ACA subclass determines uridine residues that are to be converted into pseudouridines via specific complementary binding in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms.

RESULTS:

RNAsnoop implements a dynamic programming algorithm that computes thermodynamically optimal H/ACA-RNA interactions in an efficient scanning variant. Complemented by an support vector machine (SVM)-based machine learning approach to distinguish true binding sites from spurious solutions and a system to evaluate comparative information, it presents an efficient and reliable tool for the prediction of H/ACA snoRNA target sites. We apply RNAsnoop to identify the snoRNAs that are responsible for several of the remaining 'orphan' pseudouridine modifications in human rRNAs, and we assign a target to one of the five orphan H/ACA snoRNAs in Drosophila.

AVAILABILITY:

The C source code of RNAsnoop is freely available at http://www.tbi.univie.ac.at/ -htafer/RNAsnoop

PMID:
20015949
[PubMed - indexed for MEDLINE]
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