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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Comparative analysis of protein-coding and long non-coding transcripts based on RNA sequence features
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Comparative analysis of protein-coding and long non-coding transcripts based on RNA sequence features

机译：基于RNA序列特征的蛋白质编码和长编码转录物的对比分析

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摘要

RNA plays an important role in the intracellular cell life and in the organism in general. Besides the well-established protein coding RNAs (messenger RNAs, mRNAs), long non-coding RNAs (lncRNAs) have gained the attention of recent researchers. Although lncRNAs have been classified as non-coding, some authors reported the presence of corresponding sequences in ribosome profiling data (Ribo-seq). Ribo-seq technology is a powerful experimental tool utilized to characterize RNA translation in cell with focus on initiation (harringtonine, lactimidomycin) and elongation (cycloheximide). By exploiting translation starts obtained from the Ribo-seq experiment, we developed a novel position weight matrix model for the prediction of translation starts. This model allowed us to achieve 96% accuracy of discrimination between human mRNAs and lncRNAs. When the same model was used for the prediction of putative ORFs in RNAs, we discovered that the majority of lncRNAs contained only small ORFs (= 300 nt) in contrast to mRNAs.
机译：RNA在细胞内和生物体中起着重要作用。除了成熟的蛋白质编码RNA（信使RNA、mRNAs）外，长非编码RNA（lncRNAs）也引起了最近研究人员的注意。尽管lncRNAs被归类为非编码，但一些作者报告了核糖体分析数据（核糖序列）中存在相应的序列。Ribo-seq技术是一种强大的实验工具，用于表征细胞中RNA的翻译，重点是起始（三尖杉酯碱、乳亚胺霉素）和延伸（放线菌酮）。通过利用核糖序列实验获得的翻译起始点，我们开发了一种新的位置权重矩阵模型，用于预测翻译起始点。该模型使我们能够实现人类mRNAs和lncRNAs之间96%的区分准确率。当同样的模型用于预测RNA中假定的ORF时，我们发现与mRNAs相比，大多数lncRNAs只包含较小的ORF（；=300 nt）。

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