Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Nature Protocols volume 7, pages 562–578 (2012)Cite this article

Subjects

A Corrigendum to this article was published on 25 September 2014

This article has been updated

Abstract

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Software components used in this protocol.
Figure 2: An overview of the Tuxedo protocol.
Figure 3: Merging sample assemblies with a reference transcriptome annotation.
Figure 4: Analyzing groups of transcripts identifies differentially regulated genes.
Figure 5: CummeRbund helps users rapidly explore their expression data and create publication-ready plots of differentially expressed and regulated genes.
Figure 6: CummeRbund plots of the expression level distribution for all genes in simulated experimental conditions C1 and C2.
Figure 7: CummeRbund scatter plots highlight general similarities and specific outliers between conditions C1 and C2.
Figure 8
Figure 9: Differential analysis results for regucalcin.
Figure 10: Differential analysis results for Rala.

Accession codes

Accessions

Gene Expression Omnibus

Change history

  • 07 August 2014

     In the version of this article initially published, the computer script in Box 1 sections B and C, and in Procedure Step 1, contained errors: the last section of the final three lines of the script had ‘C1’ where it should have been ‘C2’, as follows: C1_R1_2.fq should have been C2_R1_2.fq C1_R2_2.fq should have been C2_R2_2.fq C1_R3_2.fq should have been C2_R3_2.fq Users are also directed to an official release version of Cufflinks (version 1.3.0) that produces nearly identical results to those shown in the manuscript, which were produced by Cufflinks 1.2.1 (an unofficial and undocumented development build that was the latest build available when the manuscript was originally written). The script in Procedure Step 16 and the data in Table 5 have been updated to reflect the output of version 1.3.0. The errors have been corrected in the HTML and PDF versions of the article.

References

  1. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  Google Scholar 

  2. Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5, 613–619 (2008).

    Article  CAS  Google Scholar 

  3. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).

    Article  CAS  Google Scholar 

  4. Mardis, E.R. The impact of next-generation sequencing technology on genetics. Trends Genet. 24, 133–141 (2008).

    CAS  PubMed  Google Scholar 

  5. Adams, M.D. et al. Sequence identification of 2,375 human brain genes. Nature 355, 632–634 (1992).

    Article  CAS  Google Scholar 

  6. Cabili, M.N. et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 25, 1915–1927 (2011).

    Article  CAS  Google Scholar 

  7. Jiang, H. & Wong, W.H. Statistical inferences for isoform expression in RNA-seq. Bioinformatics 25, 1026–1032 (2009).

    Article  CAS  Google Scholar 

  8. Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  Google Scholar 

  9. Mortimer, S.A. & Weeks, K.M. A fast-acting reagent for accurate analysis of RNA secondary and tertiary structure by SHAPE chemistry. J. Am. Chem. Soc. 129, 4144–4145 (2007).

    Article  CAS  Google Scholar 

  10. Li, B., Ruotti, V., Stewart, R.M., Thomson, J.A. & Dewey, C.N. RNA-seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500 (2010).

    Article  Google Scholar 

  11. Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    Article  CAS  Google Scholar 

  12. Garber, M., Grabherr, M.G., Guttman, M. & Trapnell, C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods 8, 469–477 (2011).

    Article  CAS  Google Scholar 

  13. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  Google Scholar 

  14. Lister, R. et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 470, 68–73 (2011).

    Article  Google Scholar 

  15. Graveley, B.R. et al. The developmental transcriptome of Drosophila melanogaster. Nature 471, 473–479 (2011).

    Article  CAS  Google Scholar 

  16. Twine, N.A., Janitz, K., Wilkins, M.R. & Janitz, M. Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer's disease. PLoS ONE 6, e16266 (2011).

    Article  CAS  Google Scholar 

  17. Mizuno, H. et al. Massive parallel sequencing of mRNA in identification of unannotated salinity stress-inducible transcripts in rice (Oryza sativa L.). BMC Genomics 11, 683 (2010).

    Article  CAS  Google Scholar 

  18. Goecks, J., Nekrutenko, A. & Taylor, J. Galaxy Team Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010).

    Article  Google Scholar 

  19. Wu, T.D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

    Article  CAS  Google Scholar 

  20. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).

    Article  Google Scholar 

  21. Au, K.F., Jiang, H., Lin, L., Xing, Y. & Wong, W.H. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res. 38, 4570–4578 (2010).

    Article  CAS  Google Scholar 

  22. Guttman, M. et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol. 28, 503–510 (2010).

    Article  CAS  Google Scholar 

  23. Griffith, M. et al. Alternative expression analysis by RNA sequencing. Nat. Methods 7, 843–847 (2010).

    Article  CAS  Google Scholar 

  24. Katz, Y., Wang, E.T., Airoldi, E.M. & Burge, C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

    Article  CAS  Google Scholar 

  25. Nicolae, M., Mangul, S., Măndoiu, I.I. & Zelikovsky, A. Estimation of alternative splicing isoform frequencies from RNA-seq data. Algorithms Mol. Biol. 6, 9 (2011).

    Article  Google Scholar 

  26. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  Google Scholar 

  27. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

    Article  Google Scholar 

  28. Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010).

    Article  Google Scholar 

  29. Grabherr, M.G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    Article  CAS  Google Scholar 

  30. Robertson, G. et al. De novo assembly and analysis of RNA-seq data. Nat. Methods 7, 909–912 (2010).

    Article  CAS  Google Scholar 

  31. Johnson, D.S., Mortazavi, A., Myers, R.M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

    Article  CAS  Google Scholar 

  32. Ingolia, N.T., Ghaemmaghami, S., Newman, J.R.S. & Weissman, J.S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    Article  CAS  Google Scholar 

  33. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  34. Ferragina, P. & Manzini, G. An experimental study of a compressed index. Information Sci. 135, 13–28 (2001).

    Article  Google Scholar 

  35. Roberts, A., Pimentel, H., Trapnell, C. & Pachter, L. Identification of novel transcripts in annotated genomes using RNA-seq. Bioinformatics 27, 2325–2329 (2011).

    Article  CAS  Google Scholar 

  36. Li, J., Jiang, H. & Wong, W.H. Modeling non-uniformity in short-read rates in RNA-seq data. Genome Biol. 11, R50 (2010).

    Article  Google Scholar 

  37. Hansen, K.D., Brenner, S.E. & Dudoit, S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 38, e131 (2010).

    Article  Google Scholar 

  38. Roberts, A., Trapnell, C., Donaghey, J., Rinn, J.L. & Pachter, L. Improving RNA-seq expression estimates by correcting for fragment bias. Genome Biol. 12, R22 (2011).

    Article  CAS  Google Scholar 

  39. Levin, J.Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat. Methods 7, 709–715 (2010).

    Article  CAS  Google Scholar 

  40. Hansen, K.D., Wu, Z., Irizarry, R.A. & Leek, J.T. Sequencing technology does not eliminate biological variability. Nat. Biotechnol. 29, 572–573 (2011).

    Article  CAS  Google Scholar 

  41. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Use R) p 224 (Springer, 2009).

  42. Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  Google Scholar 

  43. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  44. Schatz, M.C., Langmead, B. & Salzberg, S.L. Cloud computing and the DNA data race. Nat. Biotechnol. 28, 691–693 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to D. Hendrickson, M. Cabili and B. Langmead for helpful technical discussions. The TopHat and Cufflinks projects are supported by US National Institutes of Health grants R01-HG006102 (to S.L.S.) and R01-HG006129-01 (to L.P.). C.T. is a Damon Runyon Cancer Foundation Fellow. L.G. is a National Science Foundation Postdoctoral Fellow. A.R. is a National Science Foundation Graduate Research Fellow. J.L.R. is a Damon Runyon-Rachleff, Searle, and Smith Family Scholar, and is supported by Director's New Innovator Awards (1DP2OD00667-01). This work was funded in part by the Center of Excellence in Genome Science from the US National Human Genome Research Institute (J.L.R.). J.L.R. is an investigator of the Merkin Foundation for Stem Cell Research at the Broad Institute.

Author information

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Cole Trapnell, Loyal Goff, David R Kelley & John L Rinn

  2. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    Cole Trapnell, Loyal Goff, David R Kelley & John L Rinn

  3. Department of Computer Science, University of California, Berkeley, California, USA

    Adam Roberts, Harold Pimentel & Lior Pachter

  4. Department of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Loyal Goff

  5. Department of Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Geo Pertea, Daehwan Kim & Steven L Salzberg

  6. Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA

    Geo Pertea & Steven L Salzberg

  7. Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA

    Daehwan Kim

  8. Department of Mathematics, University of California, Berkeley, California, USA

    Lior Pachter

  9. Department of Molecular and Cell Biology, University of California, Berkeley, California, USA

    Lior Pachter

Authors
  1. Cole Trapnell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adam Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Loyal Goff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Geo Pertea
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daehwan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David R Kelley
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Harold Pimentel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Steven L Salzberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. John L Rinn
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lior Pachter
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.T. is the lead developer for the TopHat and Cufflinks projects. L.G. designed and wrote CummeRbund. D.K., H.P. and G.P. are developers of TopHat. A.R. and G.P. are developers of Cufflinks and its accompanying utilities. C.T. developed the protocol, generated the example experiment and performed the analysis. L.P., S.L.S. and C.T. conceived the TopHat and Cufflinks software projects. C.T., D.R.K. and J.L.R. wrote the manuscript.

Corresponding author

Correspondence to Cole Trapnell.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Trapnell, C., Roberts, A., Goff, L. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7, 562–578 (2012). https://doi.org/10.1038/nprot.2012.016

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2012.016

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing