期刊
NATURE PROTOCOLS
卷 8, 期 8, 页码 1494-1512出版社
NATURE PORTFOLIO
DOI: 10.1038/nprot.2013.084
关键词
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资金
- National Institute of Allergy and Infectious Diseases (NIAID)
- US National Institutes of Health (NIH)
- Department of Health and Human Services (DHHS) [HHSN272200900018C]
- Howard Hughes Medical Institute (HHMI)
- NIH PIONEER award
- Center for Excellence in Genome Science from the National Human Genome Research Institute (NHGRI) [5P50HG006193-02]
- Klarman Cell Observatory at the Broad Institute
- CSIRO Office of the Chief Executive (OCE)
- National Science Foundation (NSF) [OCI-1053575]
- NIH [1R01HG005232-01A1]
- J. Thomson's MacArthur Professorship
- Morgridge Institute for Research support for Computation and Informatics in Biology and Medicine
- Bundesministerium fur Bildung und Forschung via the project 'NGSgoesHPC'
- Fund for Scientific Research, Flanders (Fonds Wetenschappelijk Onderzoek (FWO) Vlaanderen), Belgium
- NSF [ABI-1062432, CNS-0521433]
- Indiana METACyt Initiative
- Lilly Endowment, Inc.
- CSIRO eResearch Accelerated Computing Project
- Clore Foundation
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1062432] Funding Source: National Science Foundation
- Division Of Ocean Sciences
- Directorate For Geosciences [1046371] Funding Source: National Science Foundation
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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