|Title||Sequence-based heuristics for faster annotation of non-coding RNA families. |
|Publication Type||Journal Article |
|Year of Publication||2006 |
|Authors||Weinberg Z, Ruzzo WL |
|Journal||Bioinformatics (Oxford, England) |
|Date or Month Published||2006 Jan 1 |
|Keywords||Algorithms, 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 |
|Abstract||MOTIVATION: 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. |
|Alternate Journal||Bioinformatics |
|Citation Key||1880 |
|PubMed ID||16267089 |