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  4. De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity

De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity

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Author(s)
Haas, Brian J.
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Papanicolaou, Alexie
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Yassour, Moran
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Grabherr, Manfred G.
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Blood, Philip D.
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Bowden, Joshua
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Couger, Matthew Brian
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Eccles, David
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Li, Bo
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Lieber, Matthias
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Alternative Title
De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis
Date Issued
July 2013
Journal
Nature Protocols
Publisher
Nature Publishing Group
Citation
Haas, Brian J, Alexie Papanicolaou, Moran Yassour, Manfred Grabherr, Philip D Blood, Joshua Bowden, Matthew Brian Couger, et al. “De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis.” Nature Protocols 8, no. 8 (July 11, 2013): 1494-1512.
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Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
http://hdl.handle.net/1721.1/85637
DOI of Published Version
https://doi.org/10.1038/nprot.2013.084
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