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阐明编辑组学:用于 RNA 编辑检测的生物信息学方法。

Elucidating the editome: bioinformatics approaches for RNA editing detection.

机构信息

University of Bari, Italy.

出版信息

Brief Bioinform. 2019 Mar 22;20(2):436-447. doi: 10.1093/bib/bbx129.

Abstract

RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or the processing of noncoding RNAs. In recent years, the detection of editing sites has been improved through the availability of high-throughput RNA sequencing (RNA-Seq) technologies. Accurate bioinformatics pipelines are essential for the analysis of next-generation sequencing (NGS) data to ensure the correct identification of edited sites. Several pipelines, using various read mappers and variant callers with a wide range of adjustable parameters, are available for the detection of RNA editing events. In this review, we discuss some of the most recent and popular tools and provide guidelines for RNA-Seq data generation and analysis for the detection of RNA editing in massive transcriptome data. Using simulated and real data sets, we provide an overview of their behavior, emphasizing the fact that the RNA editing detection in NGS data sets remains a challenging task.

摘要

RNA 编辑是一种广泛存在的共转录后机制,通过特定的核苷酸修饰影响初级 RNA,在包括基因表达调控和/或非编码 RNA 加工在内的分子过程中发挥相关作用。近年来,随着高通量 RNA 测序(RNA-Seq)技术的出现,编辑位点的检测得到了改善。准确的生物信息学管道对于分析下一代测序(NGS)数据至关重要,以确保正确识别编辑位点。有几个使用各种读码器和变体调用程序的管道,具有广泛的可调参数,可用于检测 RNA 编辑事件。在这篇综述中,我们讨论了一些最新和最流行的工具,并为大规模转录组数据中 RNA 编辑的 RNA-Seq 数据生成和分析提供了指导。使用模拟和真实数据集,我们概述了它们的行为,强调了这样一个事实,即在 NGS 数据集的 RNA 编辑检测仍然是一项具有挑战性的任务。

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