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Bioinformatics Advance Access originally published online on September 25, 2007
Bioinformatics 2008 24(2):158-164; doi:10.1093/bioinformatics/btm464
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

SnoReport: computational identification of snoRNAs with unknown targets

Jana Hertel 1,2,*, Ivo L. Hofacker 1 and Peter F. Stadler 1–4

1Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria, 2Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, 3Fraunhofer Institut für Zelltherapie und Immunologie Deutscher Platz 5e, 04103 Leipzig, Germany and 4Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA

*To whom correspondence should be addressed.


  Abstract

Summary: Unlike tRNAs and microRNAs, both classes of snoRNAs, which direct two distinct types of chemical modifications of uracil residues, have proved to be surprisingly difficult to find in genomic sequences. Most computational approaches so far have explicitly used the fact that snoRNAs predominantly target ribosomal RNAs and spliceosomal RNAs. The target is specified by a short stretch of sequence complementarity between the snoRNA and its target. This sequence complementarity to known targets crucially contributes to sensitivity and specificity of snoRNA gene finding algorithms.

The discovery of ‘orphan’ snoRNAs, which either have no known target, or which target ordinary protein-coding mRNAs, however, begs the question whether this class of ‘housekeeping’ non-coding RNAs is much more widespread and might have a diverse set of regulatory functions. In order to approach this question, we present here a combination of RNA secondary structure prediction and machine learning that is designed to recognize the two major classes of snoRNAs, box C/D and box H/ACA snoRNAs, among ncRNA candidate sequences. The snoReport approach deliberately avoids any usage of target information. We find that the combination of the conserved sequence boxes and secondary structure constraints as a pre-filter with SVM classifiers based on a small set of structural descriptors are sufficient for a reliable identification of snoRNAs.

Tests of snoReport on data from several recent experimental surveys show that the approach is feasible; the application to a dataset from a large-scale comparative genomics survey for ncRNAs suggests that there are likely hundreds of previously undescribed ‘orphan’ snoRNAs still hidden in the human genome.

Availability: The snoReport software is implemented in ANSI C. The source code is available under the GNU Public License at http://www.bioinf.uni-leipzig.de/Software/snoReport.

Supplementary Material is available at http://www.bioinf.uni-leipzig.de/Publications/SUPPLEMENTS/07-015

Contact: jana{at}bioinf.uni-leipzig.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Limsoon Wong

Received on June 13, 2007; revised on August 20, 2007; accepted on September 8, 2007

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