title: Designing filters for fast-known NcRNA identification.
abstract: Detecting members of known noncoding RNA (ncRNA) families in genomic DNA is an important part of sequence annotation. However, the most widely used tool for modeling ncRNA families, the covariance model (CM), incurs a high-computational cost when used for genome-wide search. This cost can be reduced by using a filter to exclude sequences that are unlikely to contain the ncRNA of interest, applying the CM only where it is likely to match strongly. Despite recent advances, designing an efficient filter that can detect ncRNA instances lacking strong conservation while excluding most irrelevant sequences remains challenging. In this work, we design three types of filters based on multiple secondary structure profiles (SSPs). An SSP augments a regular profile (i.e., a position weight matrix) with secondary structure information but can still be efficiently scanned against long sequences. Multi-SSPbased filters combine evidence from multiple SSP matches and can achieve high sensitivity and specificity. Our SSP-based filters are extensively tested in BRAliBase III data set, Rfam 9.0, and a published soil metagenomic data set. In addition, we compare the SSPbased filters with several other ncRNA search tools including Infernal (with profile HMMs as filters), ERPIN, and tRNAscan-SE. Our experiments demonstrate that carefully designed SSP filters can achieve significant speedup over unfiltered CM search while maintaining high sensitivity for various ncRNA families. The designed filters and filter-scanning programs are available at our website: www.cse.msu.edu/~yannisun/ssp/.
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824, USA. yannisun@msu.edu
authors.forename: Yanni
authors.initials: Y
authors.lastname: Sun
authors.forename: Jeremy
authors.initials: J
authors.lastname: Buhler
authors.forename: Cheng
authors.initials: C
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D004247:DNA / Q000737:chemistry; D016208:Databases, Factual; D016678:Genome; D008390:Markov Chains; D009690:Nucleic Acid Conformation; D022661:RNA, Untranslated / Q000737:chemistry*; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA
chemical_list: D022661:RNA, Untranslated; D004247:DNA