Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.
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DOI：
10.1038/nprot.2012.016
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发表时间：
2012-03-01
期刊：
影响因子：
14.8
通讯作者：
中科院分区：
文献类型：
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高通量cDNA测序（RNA - seq）的最新进展能够在单次检测中揭示新基因和剪接变体，并对全基因组的表达进行定量。RNA - seq实验数据的数量和复杂性使得可扩展、快速且有数学原理的分析软件成为必需。TopHat和Cufflinks是用于基因发现以及对高通量mRNA测序（RNA - seq）数据进行全面表达分析的免费开源软件工具。它们共同使生物学家能够识别新基因以及已知基因的新剪接变体，并且能够比较两种或多种条件下基因和转录本的表达。本实验方案详细描述了如何使用TopHat和Cufflinks进行此类分析。它还涵盖了几种有助于数据管理的辅助工具和实用程序，包括CummeRbund，一种用于可视化RNA - seq分析结果的工具。尽管该流程假定具备基本的信息学技能，但这些工具对RNA - seq分析几乎没有背景要求，适用于新手和专家。该实验方案从原始测序读段开始，生成转录组组装、差异表达和调控的基因及转录本列表，以及可供发表的高质量分析结果可视化图表。该实验方案的执行时间取决于转录组测序数据量和可用的计算资源，但对于典型实验，计算机处理时间不到1天，实际操作时间约为1小时。
Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ~1 h of hands-on time.
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影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
影响因子：
48
作者：
Katz, Yarden;Wang, Eric T.;Airoldi, Edoardo M.;Burge, Christopher B.
通讯作者：
Burge, Christopher B.
影响因子：
48
作者：
Cloonan, Nicole;Forrest, Alistair R. R.;Grimmond, Sean M.
通讯作者：
Grimmond, Sean M.
影响因子：
10.5
作者：
Cabili, Moran N.;Trapnell, Cole;Rinn, John L.
通讯作者：
Rinn, John L.
影响因子：
46.9
作者：
通讯作者：
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