TitleA computational pipeline for high- throughput discovery of cis-regulatory noncoding RNA in prokaryotes.
Publication TypeJournal Article
Year of Publication2007
AuthorsYao Z, Barrick J, Weinberg Z, Neph S, Breaker R, Tompa M, Ruzzo WL
JournalPLoS computational biology
Date or Month Published2007 Jul
KeywordsArtificial Intelligence, Base Sequence, Computational Biology, Conserved Sequence, Databases, Nucleic Acid, Genes, Regulator, Genome, Bacterial, Molecular Sequence Data, Nucleic Acid Conformation, Pattern Recognition, Automated, RNA, Bacterial, RNA, Untranslated, Sequence Homology, Nucleic Acid
AbstractNoncoding RNAs (ncRNAs) are important functional RNAs that do not code for proteins. We present a highly efficient computational pipeline for discovering cis-regulatory ncRNA motifs de novo. The pipeline differs from previous methods in that it is structure-oriented, does not require a multiple-sequence alignment as input, and is capable of detecting RNA motifs with low sequence conservation. We also integrate RNA motif prediction with RNA homolog search, which improves the quality of the RNA motifs significantly. Here, we report the results of applying this pipeline to Firmicute bacteria. Our top-ranking motifs include most known Firmicute elements found in the RNA family database (Rfam). Comparing our motif models with Rfam's hand-curated motif models, we achieve high accuracy in both membership prediction and base-pair-level secondary structure prediction (at least 75% average sensitivity and specificity on both tasks). Of the ncRNA candidates not in Rfam, we find compelling evidence that some of them are functional, and analyze several potential ribosomal protein leaders in depth.
Downloadshttp://www.ncbi.nlm.nih.gov/pubmed/17616982?dopt=Abstract Abstract
Alternate JournalPLoS Comput. Biol.
Citation Key1877
PubMed ID17616982