Sequence-based heuristics for faster annotation of non-coding RNA families.
BACKGROUND
Non-coding RNAs (ncRNAs) are functional RNA molecules that do not code for proteins. Covariance Models (CMs) are a useful statistical tool to find new members of an ncRNA gene family in a large genome database, using both sequence and, importantly, RNA secondary structure information. Unfortunately, CM searches are extremely slow. Previously, we created rigorous filters, which provably sacrifice none of a CM's accuracy, while making searches significantly faster for virtually all ncRNA families. However, these rigorous filters make searches slower than heuristics could be.


RESULTS
In this paper we introduce profile HMM-based heuristic filters. We show that their accuracy is usually superior to heuristics based on BLAST. Moreover, we compared our heuristics with those used in tRNAscan-SE, whose heuristics incorporate a significant amount of work specific to tRNAs, where our heuristics are generic to any ncRNA. Performance was roughly comparable, so we expect that our heuristics provide a high-quality solution that--unlike family-specific solutions--can scale to hundreds of ncRNA families.


BACKGROUND
The source code is available under GNU Public License at the supplementary web site.
Weinberg
Zasha
Z
Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA. zasha@cs.washington.edu
Ruzzo
Walter L
WL
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009690:Nucleic Acid Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D012313:RNA / Q000737:chemistry; D012343:RNA, Transfer / Q000737:chemistry; D022661:RNA, Untranslated / Q000737:chemistry*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D012984:Software
D011506:Proteins; D022661:RNA, Untranslated; D012313:RNA; D012343:RNA, Transfer