TBI-p-2009-8

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Titel:
RNAsnoop: efficient target prediction for box H/ACA snoRNAs

Author(s):
Hakim Tafer, Stephanie Kehr, Jana Hertel, Ivo L. Hofacker, Peter F. Stadler

submitted to:
Bioinformatics 26: 610-616 (2010)

Abstract:
Motivation: all 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 of in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms. Results: implements a dynamic programming algorithm that computes thermodynamically optimal H/ACA-RNA interactions in an efficient scanning variant. Complemented by an 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


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