Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

被引:9379
|
作者
Trapnell, Cole [1 ,2 ]
Roberts, Adam [3 ]
Goff, Loyal [1 ,2 ,4 ]
Pertea, Geo [5 ,6 ]
Kim, Daehwan [5 ,7 ]
Kelley, David R. [1 ,2 ]
Pimentel, Harold [3 ]
Salzberg, Steven L. [5 ,6 ]
Rinn, John L. [1 ,2 ]
Pachter, Lior [3 ,8 ,9 ]
机构
[1] Broad Inst MIT & Harvard, Cambridge, MA USA
[2] Harvard Univ, Dept Stem Cell & Regenerat Biol, Cambridge, MA 02138 USA
[3] Univ Calif Berkeley, Dept Comp Sci, Berkeley, CA 94720 USA
[4] MIT, Dept Elect Engn & Comp Sci, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA
[5] Johns Hopkins Univ, Sch Med, Dept Med, McKusick Nathans Inst Genet Med, Baltimore, MD 21205 USA
[6] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21205 USA
[7] Univ Maryland, Ctr Bioinformat & Computat Biol, College Pk, MD USA
[8] Univ Calif Berkeley, Dept Math, Berkeley, CA 94720 USA
[9] Univ Calif Berkeley, Dept Mol & Cell Biol, Berkeley, CA 94720 USA
基金
美国国家卫生研究院;
关键词
SPLICE JUNCTIONS; MESSENGER-RNA; IN-VIVO; IDENTIFICATION; REVEALS; QUANTIFICATION; ANNOTATION;
D O I
10.1038/nprot.2012.016
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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 similar to 1 h of hands-on time.
引用
收藏
页码:562 / 578
页数:17
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