RNAsnoop: efficient target prediction for H/ACA snoRNAs.
BACKGROUND
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.


BACKGROUND
The C source code of RNAsnoop is freely available at http://www.tbi.univie.ac.at/ -htafer/RNAsnoop
Tafer
Hakim
H
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria. htafer@tbi.univie.ac.at
Kehr
Stephanie
S
Hertel
Jana
J
Hofacker
Ivo L
IL
Stadler
Peter F
PF
D000465:Algorithms; D001665:Binding Sites; D023281:Genomics / Q000379:methods*; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012335:RNA, Ribosomal / Q000737:chemistry; D020537:RNA, Small Nucleolar / Q000737:chemistry*; D017423:Sequence Analysis, RNA; D012984:Software
D012335:RNA, Ribosomal; D020537:RNA, Small Nucleolar