Designing filters for fast-known NcRNA identification

Yanni Sun, Jeremy Buhler, Cheng Yuan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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-SSP-based 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 SSP-based 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:

Original languageEnglish
Article number6081849
Pages (from-to)774-787
Number of pages14
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number3
StatePublished - 2012


  • Algorithms for data and knowledge
  • Bioinformatics (genome or protein)
  • Feature extraction or construction
  • Formal languages


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