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快速RNA测序:流程实施及交互式结果可视化指南

QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization.

作者信息

He Wen, Zhao Shanrong, Zhang Chi, Vincent Michael S, Zhang Baohong

机构信息

Early Clinical Development, Pfizer Worldwide R&D, Cambridge, MA, USA.

Inflammation and Immunology Research Unit, Pfizer Worldwide R&D, Cambridge, MA, USA.

出版信息

Methods Mol Biol. 2018;1751:57-70. doi: 10.1007/978-1-4939-7710-9_4.

Abstract

Sequencing of transcribed RNA molecules (RNA-Seq) has been used wildly for studying cell transcriptomes in bulk or at the single-cell level (Wang et al., Nat Rev Genet, 10:57-63, 2009; Ozsolak and Milos, Nat Rev Genet, 12:87-98, 2011; Sandberg, Nat Methods, 11:22-24, 2014) and is becoming the de facto technology for investigating gene expression level changes in various biological conditions, on the time course, and under drug treatments. Furthermore, RNA-Seq data helped identify fusion genes that are related to certain cancers (Maher et al., Nature, 458:97-101, 2009). Differential gene expression before and after drug treatments provides insights to mechanism of action, pharmacodynamics of the drugs, and safety concerns (Dixit et al., Genomics, 107:178-188, 2016). Because each RNA-Seq run generates tens to hundreds of millions of short reads with size ranging from 50 to 200 bp, a tool that deciphers these short reads to an integrated and digestible analysis report is in high demand. QuickRNASeq (Zhao et al., BMC Genomics, 17:39-53, 2016) is an application for large-scale RNA-Seq data analysis and real-time interactive visualization of complex data sets. This application automates the use of several of the best open-source tools to efficiently generate user friendly, easy to share, and ready to publish report. Figures in this protocol illustrate some of the interactive plots produced by QuickRNASeq. The visualization features of the application have been further improved since its first publication in early 2016. The original QuickRNASeq publication (Zhao et al., BMC Genomics, 17:39-53, 2016) provided details of background, software selection, and implementation. Here, we outline the steps required to implement QuickRNASeq in user's own environment, as well as demonstrate some basic yet powerful utilities of the advanced interactive visualization modules in the report.

摘要

转录RNA分子测序(RNA测序)已被广泛用于研究群体细胞转录组或单细胞水平的转录组(Wang等人,《自然综述:遗传学》,10:57 - 63,2009年;Ozsolak和Milos,《自然综述:遗传学》,12:87 - 98,2011年;Sandberg,《自然方法》,11:22 - 24,2014年),并且正在成为研究各种生物学条件下、时间进程中和药物处理下基因表达水平变化的实际技术。此外,RNA测序数据有助于识别与某些癌症相关的融合基因(Maher等人,《自然》,458:97 - 101,2009年)。药物治疗前后的差异基因表达为药物的作用机制、药效学和安全性问题提供了见解(Dixit等人,《基因组学》,107:178 - 188,2016年)。由于每次RNA测序运行会生成数千万到数亿条长度在50到200碱基对之间的短读段,因此迫切需要一种能将这些短读段解读为综合且易于理解的分析报告的工具。QuickRNASeq(Zhao等人,《BMC基因组学》,17:39 - 53,2016年)是一个用于大规模RNA测序数据分析和复杂数据集实时交互式可视化的应用程序。该应用程序自动化使用了一些最佳的开源工具,以高效生成用户友好、易于共享且可供发表的报告。本方案中的图展示了QuickRNASeq生成的一些交互式图。自2016年初首次发布以来,该应用程序的可视化功能得到了进一步改进。QuickRNASeq的原始出版物(Zhao等人,《BMC基因组学》,17:39 - 53,2016年)提供了背景、软件选择和实现的详细信息。在这里,我们概述了在用户自己的环境中实施QuickRNASeq所需的步骤,并展示报告中高级交互式可视化模块的一些基本但强大的实用功能。

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