Molecular Profiling of RNA Tumors Using High-Throughput RNA Sequencing: From Raw Data to Systems Level Analyses
Molecular Profiling of RNA Tumors Using High-Throughput RNA Sequencing: From Raw Data to Systems Level Analyses
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DOI：
10.1007/978-1-4939-9004-7_13
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Hardiman, Gary
中科院分区：
文献类型：
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作者：
da Silveira, Willian A.;Hazard, E. Starr;Hardiman, Gary
RNAseq是一种强大的技术，能够在健康和疾病状态下实现转录组的全球概况。在本章中，我们回顾了分析RNA测序生成的数据的管道，从原始数据到系统级分析。我们首先概述了从FASTQ文件生成映射读段的工作流程，包括FASTQ的质量控制、读段的过滤和修剪以及读段与基因组的比对。然后，我们比较和对比三种流行的选择，以确定差异表达（DE）的转录本（Tuxedo Pipeline，DESeq 2和Limma/voom）。最后，我们研究了四种工具集，从DE基因列表中推断生物学意义（基因卡，人类蛋白质图谱，GSEA和Topp-Gene）。我们强调需要提出一个简明的科学问题，并清楚地了解这些方法的优势和局限性。
RNAseq is a powerful technique enabling global profiles of transcriptomes in healthy and diseased states. In this chapter we review pipelines to analyze the data generated by sequencing RNA, from raw data to a system level analysis. We first give an overview of workflow to generate mapped reads from FASTQ files, including quality control of FASTQ, filtering and trimming of reads, and alignment of reads to a genome. Then, we compare and contrast three popular options to determine differentially expressed (DE) transcripts (The Tuxedo Pipeline, DESeq2, and Limma/voom). Finally, we examine four tool sets to extrapolate biological meaning from the list of DE genes (Genecards, The Human Protein Atlas, GSEA, and Topp-Gene). We emphasize the need to ask a concise scientific question and to clearly under stand the strengths and limitations of the methods.