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使用RAPID对小RNA数据集进行自动化分析。

Automated analysis of small RNA datasets with RAPID.

作者信息

Karunanithi Sivarajan, Simon Martin, Schulz Marcel H

机构信息

Cluster of Excellence for Multimodal Computing and Interaction, and Department for Computational Biology & Applied Algorithms, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.

Graduate School of Computer Science, Saarland Informatics Campus, Universität des Saarlandes, Saarbrücken, Germany.

出版信息

PeerJ. 2019 Apr 10;7:e6710. doi: 10.7717/peerj.6710. eCollection 2019.

Abstract

UNLABELLED

Understanding the role of short-interfering RNA (siRNA) in diverse biological processes is of current interest and often approached through small RNA sequencing. However, analysis of these datasets is difficult due to the complexity of biological RNA processing pathways, which differ between species. Several properties like strand specificity, length distribution, and distribution of soft-clipped bases are few parameters known to guide researchers in understanding the role of siRNAs. We present RAPID, a generic eukaryotic siRNA analysis pipeline, which captures information inherent in the datasets and automatically produces numerous visualizations as user-friendly HTML reports, covering multiple categories required for siRNA analysis. RAPID also facilitates an automated comparison of multiple datasets, with one of the normalization techniques dedicated for siRNA knockdown analysis, and integrates differential expression analysis using DESeq2.

AVAILABILITY AND IMPLEMENTATION

RAPID is available under MIT license at https://github.com/SchulzLab/RAPID. We recommend using it as a conda environment available from https://anaconda.org/bioconda/rapid.

摘要

未标记

了解小干扰RNA(siRNA)在多种生物过程中的作用是当前研究的热点,通常通过小RNA测序来进行研究。然而,由于生物RNA加工途径的复杂性,不同物种之间存在差异,对这些数据集的分析具有一定难度。诸如链特异性、长度分布和软剪辑碱基分布等几个属性是指导研究人员理解siRNA作用的少数参数。我们提出了RAPID,这是一种通用的真核生物siRNA分析流程,它能够捕捉数据集中固有的信息,并自动生成大量可视化结果,以用户友好的HTML报告形式呈现,涵盖了siRNA分析所需的多个类别。RAPID还便于对多个数据集进行自动比较,其中一种归一化技术专门用于siRNA敲低分析,并集成了使用DESeq2进行的差异表达分析。

可用性和实现方式

RAPID根据麻省理工学院许可协议可从https://github.com/SchulzLab/RAPID获取。我们建议将其作为可从https://anaconda.org/bioconda/rapid获取的conda环境使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f7a/6462184/936a3b22818e/peerj-07-6710-g001.jpg

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