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De novo computational prediction of non-coding RNA genes in prokaryotic genomes

机译:从头计算预测原核基因组中非编码RNA基因

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摘要

>Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.>Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation.>Availability: The source code and results are available at .>Contact: >Supplementary information: are available at Bioinformatics online.
机译:>动机:非编码RNA(ncRNA)基因的计算鉴定是计算生物学中最重要且最具挑战性的问题之一。现有的ncRNA基因预测方法主要依赖于同源性信息,因此将其应用仅限于具有已知同源性的ncRNA基因。>结果:我们使用从序列和与诱饵相比,已知ncRNA基因的结构。利用这些功能,我们训练了基于神经网络的分类器,并将其应用于大肠杆菌和Sulfolobus solfataricus,用于ncRNA的全基因组预测。我们的方法用于鉴定在大肠杆菌中具有ncRNA基因潜能的窗口的平均预测灵敏度和特异性分别为68%和70%。通过组合不同大小的窗口并使用位置过滤策略,我们预测了601个候选ncRNA,并回收了大肠杆菌中已知ncRNA的41%。我们通过Northern印迹分析实验研究了六种新候选基因,发现了三种候选基因的表达:一种代表潜在的新ncRNA,一种与稳定的mRNA衰变中间体相关,一种是涉及核糖核酸调控的潜在核糖开关或转录衰减子的情况。细胞分裂。通常,我们的方法可以识别部分或完全测序的微生物基因组中的顺式和反式ncRNA,而无需同源性或结构保守性。>可用性:源代码和结果可在。< strong>联系方式: >补充信息:可从生物信息学在线获得。

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