TY - JOUR
T1 - De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis
AU - Haas, Brian J.
AU - Papanicolaou, Alexie
AU - Yassour, Moran
AU - Grabherr, Manfred
AU - Blood, Philip D.
AU - Bowden, Joshua
AU - Couger, Matthew Brian
AU - Eccles, David
AU - Li, Bo
AU - Lieber, Matthias
AU - Macmanes, Matthew D.
AU - Ott, Michael
AU - Orvis, Joshua
AU - Pochet, Nathalie
AU - Strozzi, Francesco
AU - Weeks, Nathan
AU - Westerman, Rick
AU - William, Thomas
AU - Dewey, Colin N.
AU - Henschel, Robert
AU - Leduc, Richard D.
AU - Friedman, Nir
AU - Regev, Aviv
PY - 2013/8
Y1 - 2013/8
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84880266648
U2 - 10.1038/nprot.2013.084
DO - 10.1038/nprot.2013.084
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C2 - 23845962
AN - SCOPUS:84880266648
SN - 1754-2189
VL - 8
SP - 1494
EP - 1512
JO - Nature Protocols
JF - Nature Protocols
IS - 8
ER -
