Computational prediction of Caenorhabditis box H/ACA snoRNAs using genomic properties of their host genes

P. P S Wang, Ilya Ruvinsky*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Identification of small nucleolar RNAs (snoRNAs) in genomic sequences has been challenging due to the relative paucity of sequence features. Many current prediction algorithms rely on detection of snoRNA motifs complementary to target sites in snRNAs and rRNAs. However, recent discovery of snoRNAs without apparent targets requires development of alternative prediction methods. We present an approach that combines rule-based filters and a Bayesian Classifier to identify a class of snoRNAs (H/ACA) without requiring target sequence information. It takes advantage of unique attributes of their genomic organization and improved species-specific motif characterization to predict snoRNAs that may otherwise be difficult to discover. Searches in the genomes of Caenorhabditis elegans and the closely related Caenorhabditis briggsae suggest that our method performs well compared to recent benchmark algorithms. Our results illustrate the benefits of training gene discovery engines on features restricted to particular phylogenetic groups and the utility of incorporating diverse data types in gene prediction. Published by Cold Spring Harbor Laboratory Press.

Original languageEnglish (US)
Pages (from-to)290-298
Number of pages9
Issue number2
StatePublished - Feb 2010


  • Computational prediction
  • H/ACA snoRNA
  • Naive Bayesian Classifier
  • Nested genes

ASJC Scopus subject areas

  • Molecular Biology


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