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

By Brian J. Haas, A. Papanicolaou, Moran Yassour, Manfred G. Grabherr, Philip D. Blood, Joshua C. Bowden, M. B. Couger, David A. Eccles, Bo Li, Matthias Lieber and 13 more. Nature Protocols, 2013

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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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/04204ae9db4cc440594832a23b55192414ce7a7a

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Citations in the corpus

FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome (2017, background): “Following RNA sequencing, the computational reconstruction of transcripts models either by genome-guided (11,12) or de novo assembly (13) usually produces tens of thousands of known and novel transcript models.”
Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury. (2015, neutral): “To compare expression levels of different genes across samples, we performed an additional TMM (trimmed mean of M values) normalization on FPKM (fragments per kilobase of transcript per million mapped reads) using Trinity (Haas et al., 2013) (http:// trinityrnaseq.”
Orthology Inference in Nonmodel Organisms Using Transcriptomes and Low-Coverage Genomes: Improving Accuracy and Matrix Occupancy for Phylogenomics (2014, methodology): “The remaining reads were assembled using Trinity version 20140413 with default settings (Grabherr et al. 2011), and translated using TransDecoder version rel16JAN2014 assisted by pfam domain information (Haas et al. 2013).”
The khmer software package: enabling efficient nucleotide sequence analysis (2015, methodology): “The output of normalize-by-median.py can be assembled using a de novo assembler such as Velvet 10 , IDBA 11 , Trinity 12 or SPAdes 13 .”
A complete Cannabis chromosome assembly and adaptive admixture for elevated cannabidiol (CBD) content (2018, methodology): “142 RNAseq libraries found on the Sequence Read Archive were aligned to the reference with GSnap (35) and assembled into transcripts with Stringtie (36).”
Complexity of the Alternative Splicing Landscape in Plants[C][W][OPEN] (2013, background): “These studies suggest the functional significance of AS as a result of tandem acceptor in plants.”
De novo assembly of bacterial transcriptomes from RNA-seq data (2015, methodology): “To provide points of comparison, two leading de novo transcriptome assemblers, Trinity [8,25] and SOAPdenovo2 [10,26], were executed on the same data, and their results are compared with those of Rockhopper 2.”
Identification of protein coding regions in RNA transcripts (2014, methodology): “A recent ab initio tool, TransDecoder, a companion of the de novo transcriptome assembler Trinity (6), generates the training set by a simple automatic procedure that identifies long open reading frames (ORFs) in the assembled transcripts.”
Applications of next-generation sequencing to the study of biological invasions (2015, methodology): “Another approach to study transcriptomes is to sequence and assemble the focal species’ transcriptome de novo (Haas et al., 2013).”
A survey of best practices for RNA-seq data analysis (2016, methodology): “Therefore, in silico reduction of the number of reads is recommended for deeply sequenced samples [33].”
Corset: enabling differential gene expression analysis for de novo assembled transcriptomes (2014, methodology): “However, few studies have compared or even suggested a path for obtaining gene-level counts from transcriptome assemblies [18,19] and only a single automated pipeline has thus far been implemented to address this need [20]; it is provided by Trinity to run RSEM [21] followed by edgeR [22] or DESeq [”
Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, methodology): “1, which provides the best overall performance in Hoff et al. (2016).”