Yao Zizhen, Barrick Jeffrey, Weinberg Zasha, Neph Shane, Breaker Ronald, Tompa Martin, Ruzzo Walter L
Department of Computer Science and Engineering, University of Washington, Seattle, Washington, USA.
PLoS Comput Biol. 2007 Jul;3(7):e126. doi: 10.1371/journal.pcbi.0030126.
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.
非编码RNA（ncRNA）是一类重要的功能性RNA，它们不编码蛋白质。我们提出了一种高效的计算流程，用于从头发现顺式调控ncRNA基序。该流程与以往方法的不同之处在于，它以结构为导向，不需要多序列比对作为输入，并且能够检测序列保守性较低的RNA基序。我们还将RNA基序预测与RNA同源搜索相结合，这显著提高了RNA基序的质量。在此，我们报告了将此流程应用于厚壁菌门细菌的结果。我们排名靠前的基序包括RNA家族数据库（Rfam）中发现的大多数已知厚壁菌门元件。将我们的基序模型与Rfam精心策划的基序模型进行比较，我们在成员预测和碱基对水平的二级结构预测方面都取得了很高的准确率（两项任务的平均灵敏度和特异性至少为75%）。在Rfam中未出现的ncRNA候选物中，我们发现了一些令人信服的证据表明其中一些具有功能，并深入分析了几个潜在的核糖体蛋白前导序列。
