Inst. f. Informatik   
Uni Leipzig

Bioinformatics Preprint 07-015

[PDF] [Supplemental Material] [Software]

SnoReport: Computational identification of snoRNAs with unknown targets

Jana Hertel, Ivo L. Hofacker, Peter F. Stadler


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 have so far explicitly used the fact that they predominantly target ribosomal RNAs and spliceosomal RNAs. The target U 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 wide-spread 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.
The snoReport software is implemented in ANSI C. The source code and supplemental material is available for download from
snoRNA, prediction, ncRNAs, RNA secondary structure, SVM

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