Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

By Manfred G. Grabherr, Brian J. Haas, Moran Yassour, J. Levin, Dawn A Thompson, Ido Amit, Xian Adiconis, Lin Fan, R. Raychowdhury, Q. Zeng and 11 more. Nature Biotechnology, 2011

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Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0d28f6e79581f7b0c28e05eb3d3ad428cc95204d

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Citations in the corpus

DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields (2017, neutral): “, 2010), Trinity (Grabherr et al., 2011), SOAPdenovo-Trans (Xie et al.”
Enriching the ant tree of life: enhanced UCE bait set for genome‐scale phylogenetics of ants and other Hymenoptera (2017, methodology): “…files, we cleaned and trimmed raw reads using ILLUMIPROCESSOR (Faircloth 2013), which is a wrapper around TRIMMOMATIC (Lohse et al. 2012; Del Fabbro et al. 2013), and we assembled reads de novo using a wrapper (phyluce_assembly_assemblo_trinity) around TRINITY v2013-02-25 (Grabherr et al. 2011).”
A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (2012, methodology): “Additionally, we compared the results obtained with the ‘reference genome method’ with the de novo assembly using Trinity pipeline (26).”
Thermal and pCO2 Stress Elicit Divergent Transcriptomic Responses in a Resilient Coral (2016, methodology): “Collapsed files then served as input for RNAseq de novo assembly using Trinity (Grabherr et al., 2011) with 750 GB of system memory and 24 CPUs at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin.”
The Amborella Genome and the Evolution of Flowering Plants (2013, methodology): “Dueck, Clustering by passing messages between data points, Science 315, 972–976 (2007).”
Bandage: interactive visualization of de novo genome assemblies (2015, methodology): “Bandage facilitates interaction with de Bruijn graphs made by de novo assemblers such as Velvet (Zerbino et al., 2008), SPAdes (Bankevich et al., 2012) and Trinity (Grabherr et al., 2011).”
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).”
Symbiodinium Functional Diversity in the Coral Siderastrea siderea Is Influenced by Thermal Stress and Reef Environment, but Not Ocean Acidification (2018, methodology): “Transcriptome de novo assembly of over 770 million PE reads was completed using Trinity (Grabherr et al., 2011) and resulting contigs were assigned as either coral- or Symbiodinium-derived contigs using a translated blast search (tblastx) against host and symbiont specific databases (Davies et al., ”
Lessons from the cancer genome. (2013, positive): “These efforts have been aided by recent computational advances, such as algorithms that reconstruct transcriptomes without the need for an underlying reference genome (Grabherr et al., 2011).”
Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study (2011, background): “net) has produced excellent results in cancer studies, and newer tools now enable the de novo discovery of gene fusions without the need for a reference genome(30,31).”