Journal Article

RNAsnoop: efficient target prediction for H/ACA snoRNAs

Hakim Tafer, Stephanie Kehr, Jana Hertel, Ivo L. Hofacker and Peter F. Stadler

in Bioinformatics

Volume 26, issue 5, pages 610-616
Published in print March 2010 | ISSN: 1367-4803
Published online December 2009 | e-ISSN: 1460-2059 | DOI:
RNAsnoop: efficient target prediction for H/ACA snoRNAs

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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∼htafer/RNAsnoop


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4956 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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