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Nat Protoc. 2013 Aug;8(8):1494-512. doi: 10.1038/nprot.2013.084. Epub 2013 Jul 11.

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

Author information

1
Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA, 02142, USA.
2
CSIRO Ecosystem Sciences, Black Mountain Labs, Canberra, ACT 2601, Australia.
3
The Selim and Rachel Benin School of Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
4
Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
5
Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
6
CSIRO Information Management & Technology, 306 Carmody Rd, St Lucia QLD 4067, Australia.
7
Department of Microbiology and Molecular Genetics, Oklahoma State University, USA.
8
Genomics Research Centre, Griffith University, Gold Coast Campus, Qld 4222, Australia.
9
Department of Computer Sciences, University of Wisconsin, Madison, WI, 53706, USA.
10
Technische Universität Dresden, Dresden, Saxony 01062, Germany.
11
University of California, Berkeley and California Institute for Quantitative Biosciences Berkeley, CA 94720, USA.
12
Institute for Genome Sciences, Baltimore, MD, 21201, USA.
13
Department of Plant Systems Biology, VIB, Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent B-9052, Belgium.
14
Parco Tecnologico Padano, Loc. Cascina Codazza, 26900 Lodi, Italy.
15
Corn Insects and Crop Genetics Research Unit, United States Department of Agriculture--Agricultural Research Service, Ames, IA 50011, USA.
16
Genomics facility, Purdue University, West Lafayette, IN, 47907, USA.
17
GWT-TUD GmbH, Blasewitzer Strasse 43, Dresden, Saxony 01307, Germany.
18
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, USA.
19
Indiana University, 2709 East 10th Street, Bloomington, IN 47408,USA.
20
Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02140.
#
Contributed equally

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.

PMID:
23845962
PMCID:
PMC3875132
DOI:
10.1038/nprot.2013.084
[Indexed for MEDLINE]
Free PMC Article

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