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De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

Nature Protocols volume 8, pages 1494–1512 (2013)Cite this article

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Abstract

De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

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Figure 1: Overview of Trinity assembly and analysis pipeline.
Figure 2: Effects of in silico fragment normalization of RNA-seq data on Trinity full-length transcript reconstruction.
Figure 3: Transcriptome and genome representations of alternatively spliced transcripts.
Figure 4: Strand-specific library types.
Figure 5: Full-length transcript reconstruction by Trinity in different organisms, sequencing depths and parameters.
Figure 6: Evaluating paired-read support via the Jaccard similarity coefficient.
Figure 7: De novo transcriptome assembly and analysis workflow.
Figure 8: Abundance estimation via expectation maximization by RSEM.
Figure 9: Pairwise comparisons of transcript abundance.
Figure 10: Comparisons of transcriptional profiles across samples.

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Acknowledgements

We are grateful to D. Jaffe and S. Young for access to additional computing resources, to Z. Chen for help in R-scripting, to L. Gaffney for help with figure illustrations, to C. Titus Brown for essential discussions and inspiration related to digital normalization strategies, to G. Marcais and C. Kingsford for supporting the use of their Jellyfish software in Trinity and to B. Walenz for supporting our earlier use of Meryl. We are grateful to our users and their feedback, in particular J. Wortman and P. Bain for comments on earlier drafts of the manuscript. This project has been funded in part (B.J.H.) with Federal funds from the National Institute of Allergy and Infectious Diseases (NIAID), US National Institutes of Health (NIH), Department of Health and Human Services (DHHS), under contract no. HHSN272200900018C. Work was supported by Howard Hughes Medical Institute (HHMI), a NIH PIONEER award, a Center for Excellence in Genome Science grant no. 5P50HG006193-02 from the National Human Genome Research Institute (NHGRI) and the Klarman Cell Observatory at the Broad Institute (A.R.). A.P. was supported by the CSIRO Office of the Chief Executive (OCE). M.Y. was supported by the Clore Foundation. P.B. was supported by the National Science Foundation (NSF) grant no. OCI-1053575 for the Extreme Science and Engineering Discovery Environment (XSEDE) project. B.L. and C.D. were partially supported by NIH grant no.1R01HG005232-01A1. In addition, B.L. was partially funded by J. Thomson's MacArthur Professorship and by the Morgridge Institute for Research support for Computation and Informatics in Biology and Medicine. M.L. was supported by the Bundesministerium für Bildung und Forschung via the project 'NGSgoesHPC'. N.P. was funded by the Fund for Scientific Research, Flanders (Fonds Wetenschappelijk Onderzoek (FWO) Vlaanderen), Belgium. R.H. and R.D.L. were funded by the NSF under grant nos. ABI-1062432 and CNS-0521433 to Indiana University, and by Indiana METACyt Initiative, which is supported in part by Lilly Endowment, Inc. J.B. was supported through a CSIRO eResearch Accelerated Computing Project. Any opinions, findings and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of any of the funding bodies and institutions including the National Science Foundation, the National Center for Genome Analysis Support and Indiana University.

Author information

Author notes

  1. Brian J Haas and Alexie Papanicolaou: These authors contributed equally to this work.

Authors and Affiliations

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

    Brian J Haas, Moran Yassour, Nathalie Pochet & Aviv Regev

  2. Commonwealth Scientific and Industrial Research Organisation (CSIRO) Ecosystem Sciences, Black Mountain Laboratories, Canberra, Australian Capital Territory, Australia

    Alexie Papanicolaou & Michael Ott

  3. The Selim and Rachel Benin School of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel

    Moran Yassour & Nir Friedman

  4. Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden

    Manfred Grabherr

  5. Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Philip D Blood

  6. CSIRO Information Management & Technology, St. Lucia, Queensland, Australia

    Joshua Bowden

  7. Department of Microbiology and Molecular Genetics, Oklahoma State University, Stillwater, Oklahoma, USA

    Matthew Brian Couger

  8. Genomics Research Centre, Griffith University, Gold Coast Campus, Gold Coast, Queensland, Australia

    David Eccles

  9. Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin, USA

    Bo Li & Colin N Dewey

  10. Center for Information Services and High-performance Computing (ZIH), Technische Universität Dresden, Dresden, Germany

    Matthias Lieber

  11. California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, California, USA

    Matthew D MacManes

  12. Institute for Genome Sciences, Baltimore, Maryland, USA

    Joshua Orvis

  13. Department of Plant Systems Biology, Department of Plant Biotechnology and Bioinformatics, Vlaams Instituut voor Biotechnologie (VIB), Ghent University, Ghent, Belgium

    Nathalie Pochet

  14. Parco Tecnologico Padano, Località Cascina Codazza, Lodi, Italy

    Francesco Strozzi

  15. United States Department of Agriculture–Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, Iowa, USA

    Nathan Weeks

  16. Genomics facility, Purdue University, West Lafayette, Indiana, USA

    Rick Westerman

  17. GWT-TUD GmbH, Saxony, Germany

    Thomas William

  18. Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA

    Colin N Dewey

  19. Research Technologies Division, University Information Technology Services, Indiana University, Bloomington, Indiana, USA

    Robert Henschel & Richard D LeDuc

  20. Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Aviv Regev

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  1. Brian J Haas
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Contributions

B.J.H. is the current lead developer of Trinity and is additionally responsible for the development of the companion in silico normalization and TransDecoder utilities described herein. M.Y. contributed to Butterfly software enhancements, generating figures and to the manuscript text. B.L. and C.N.D. developed RSEM and are responsible for enhancements related to improved Trinity support. B.J.H. and A.P. wrote the initial draft of the manuscript. A.R. is the Principal Investigator. All authors contributed to Trinity development and/or writing of the final manuscript, and all authors approved the final text.

Corresponding authors

Correspondence to Brian J Haas or Aviv Regev.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Note

Supplementary materials for de novo transcript sequence reconstruction from RNA-seq: reference generation and analysis with Trinity. (PDF 699 kb)

Supplementary Figure 1

Defining minimum edge thresholds during initial Butterfly graph pruning. (PDF 554 kb)

Supplementary Figure 2

Butterfly's minimum support requirement for path extension during transcript reconstruction. (PDF 551 kb)

Supplementary Figure 3

Merging of insufficiently different path sequences. (PDF 530 kb)

Supplementary Figure 4

Enforcing path restrictions via triplet locking. (PDF 536 kb)

Supplementary Figure 5

Restrictions on the number of paths to be extended at each node. (PDF 540 kb)

Supplementary Figure 6

Evaluating assembly completeness for the S. pombe transcriptome. (PDF 636 kb)

Supplementary Figure 7

Evaluating assembly completeness for the mouse dendritic cell transcriptome. (PDF 584 kb)

Supplementary Figure 8

Correlation of expression values between reference transcripts and Trinity transcript components according to percent length agreement in S. pombe. (PDF 551 kb)

Supplementary Figure 9

Agreement between expression profiles calculated based on reference transcripts and trinity components at different S. pombe samples. (PDF 584 kb)

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Haas, B., Papanicolaou, A., Yassour, M. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8, 1494–1512 (2013). https://doi.org/10.1038/nprot.2013.084

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