Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 3, Issue 7, Pages 1212-1223Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.0030126
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Funding
- NCRR NIH HHS [S10 RR019895, RR19895] Funding Source: Medline
- NHGRI NIH HHS [R01 HG02602, R01 HG002602] Funding Source: Medline
- NIDDK NIH HHS [R33 DK07027] Funding Source: Medline
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Noncoding 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.
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