TitleSequence-based heuristics for faster annotation of non-coding RNA families.
Publication TypeJournal Article
Year of Publication2006
AuthorsWeinberg Z, Ruzzo WL
JournalBioinformatics (Oxford, England)
Volume22
Issue1
Pagination35-9
Date or Month Published2006 Jan 1
ISSN1367-4803
KeywordsAlgorithms, Computational Biology, Genome, Humans, Markov Chains, Models, Statistical, Nucleic Acid Conformation, Proteins, Protein Structure, Secondary, RNA, RNA, Transfer, RNA, Untranslated, ROC Curve, Sensitivity and Specificity, Sequence Alignment, Software
AbstractMOTIVATION: 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. AVAILABILITY: The source code is available under GNU Public License at the supplementary web site.
DOI10.1093/bioinformatics/bti743
Downloadshttp://www.ncbi.nlm.nih.gov/pubmed/16267089?dopt=Abstract
Alternate JournalBioinformatics
Citation Key1880
PubMed ID16267089