a computational pipeline for high- throughput discovery of cis-regulatory noncoding rna in prokaryotes对高吞吐量计算管道发现原核生物的基因非编码rna.pdfVIP

A Computational Pipeline for High- Throughput Discovery of cis-Regulatory Noncoding RNA in Prokaryotes 1* 2¤ 3 1,4 2,3,5 1,4 Zizhen Yao , Jeffrey Barrick , Zasha Weinberg , Shane Neph , Ronald Breaker , Martin Tompa , Walter L. Ruzzo1,4 1 Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America, 2 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America, 3 Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America, 4 Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America, 5 Howard Hughes Medical Institute, Yale University, New Haven, Connecticut, United States of America 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