Conda
See the ‘activating the conda environment’ section below to access this
software.
trinity-2.15.1 RNA-Seq De novo Assembly Using Trinity
Quick Guide for the Impatient
Trinity assembles transcript sequences from Illumina RNA-Seq data.
Assemble RNA-Seq data like so:
Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G
Find assembled transcripts as: ‘trinity_out_dir/Trinity.fasta’
Use the documentation links in the right-sidebar to navigate this
documentation, and contact our Google group for technical
support.
Intro to Trinity
Trinity, developed at the Broad Institute and
the Hebrew University of Jerusalem, represents a
novel method for the efficient and robust de novo reconstruction of
transcriptomes from RNA-seq data. Trinity combines three independent software
modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process
large volumes of RNA-seq reads. Trinity partitions the sequence data into many
individual de Bruijn graphs, each representing the transcriptional complexity
at a given gene or locus, and then processes each graph independently to
extract full-length splicing isoforms and to tease apart transcripts derived
from paralogous genes. Briefly, the process works like so:
-
Inchworm assembles the RNA-seq data into the unique sequences of
transcripts, often generating full-length transcripts for a dominant
isoform, but then reports just the unique portions of alternatively spliced
transcripts.
-
Chrysalis clusters the Inchworm contigs into clusters and constructs
complete de Bruijn graphs for each cluster. Each cluster represents the
full transcriptonal complexity for a given gene (or sets of genes that share
sequences in common). Chrysalis then partitions the full read set among
these disjoint graphs.
-
Butterfly then processes the individual graphs in parallel, tracing the
paths that reads and pairs of reads take within the graph, ultimately
reporting full-length transcripts for alternatively spliced isoforms, and
teasing apart transcripts that corresponds to paralogous genes.
Trinity Publications
Trinity was published in Nature
Biotechnology. Our
protocol for transcriptome assembly and downstream analysis is published in
Nature Protocols,
although we always have the most current instructional material available here
at the Trinity website.
Activating the conda environment
Check out a node with qrsh and run:
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bash
source /local/cluster/conda-envs/envs/trinity-2.15.1/activate.sh
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Using conda activate trinity
If your conda is set up as
here,
you can run conda activate trinity instead of the source line above as well.
Running over SGE
And then run your commands as usual. To use over SGE, include the source line
above in a shell script prior to your commands, e.g.
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$ cat run_trinity.sh
#!/usr/bin/env bash
source /local/cluster/conda-envs/envs/trinity-2.15.1/activate.sh
Trinity ...
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And then run hqsub 'bash ./run_trinity.sh' -r sge.trinity ....
Using hqsub –conda
You can also run:
hqsub 'conda activate trinity; Trinity ...' -r sge.trinity --conda ...
Without having to write a separate shell script.
Location and version
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$ which Trinity
/local/cluster/conda-envs/envs/trinity/bin/Trinity
$ Trinity --version
Trinity version: Trinity-v2.15.1
-currently using the latest production release of Trinity.
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help message
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$ Trinity --help
###############################################################################
#
______ ____ ____ ____ ____ ______ __ __
| || \ | || \ | || || | |
| || D ) | | | _ | | | | || | |
|_| |_|| / | | | | | | | |_| |_|| ~ |
| | | \ | | | | | | | | | |___, |
| | | . \ | | | | | | | | | | |
|__| |__|\_||____||__|__||____| |__| |____/
Trinity-v2.15.1
#
#
# Required:
#
# --seqType <string> :type of reads: ('fa' or 'fq')
#
# --max_memory <string> :suggested max memory to use by Trinity where limiting can be enabled. (jellyfish, sorting, etc)
# provided in Gb of RAM, ie. '--max_memory 10G'
#
# If paired reads:
# --left <string> :left reads, one or more file names (separated by commas, no spaces)
# --right <string> :right reads, one or more file names (separated by commas, no spaces)
#
# Or, if unpaired reads:
# --single <string> :single reads, one or more file names, comma-delimited (note, if single file contains pairs, can use flag: --run_as_paired )
#
# Or,
# --samples_file <string> tab-delimited text file indicating biological replicate relationships.
# ex.
# cond_A cond_A_rep1 A_rep1_left.fq A_rep1_right.fq
# cond_A cond_A_rep2 A_rep2_left.fq A_rep2_right.fq
# cond_B cond_B_rep1 B_rep1_left.fq B_rep1_right.fq
# cond_B cond_B_rep2 B_rep2_left.fq B_rep2_right.fq
#
# # if single-end instead of paired-end, then leave the 4th column above empty.
#
####################################
## Misc: #########################
#
# --SS_lib_type <string> :Strand-specific RNA-Seq read orientation.
# if paired: RF or FR,
# if single: F or R. (dUTP method = RF)
# See web documentation.
#
# --CPU <int> :number of CPUs to use, default: 2
# --min_contig_length <int> :minimum assembled contig length to report
# (def=200, must be >= 100)
#
# --long_reads <string> :fasta file containing error-corrected or circular consensus (CCS) pac bio reads
# (** note: experimental parameter **, this functionality continues to be under development)
#
# --genome_guided_bam <string> :genome guided mode, provide path to coordinate-sorted bam file.
# (see genome-guided param section under --show_full_usage_info)
#
# --long_reads_bam <string> :long reads to include for genome-guided Trinity
# (bam file consists of error-corrected or circular consensus (CCS) pac bio read aligned to the genome)
#
# --jaccard_clip :option, set if you have paired reads and
# you expect high gene density with UTR
# overlap (use FASTQ input file format
# for reads).
# (note: jaccard_clip is an expensive
# operation, so avoid using it unless
# necessary due to finding excessive fusion
# transcripts w/o it.)
#
# --trimmomatic :run Trimmomatic to quality trim reads
# see '--quality_trimming_params' under full usage info for tailored settings.
#
# --output <string> :name of directory for output (will be
# created if it doesn't already exist)
# default( your current working directory: "/nfs3/Sharpton_Lab/prod/projects/davised/EMC2/trinity_out_dir"
# note: must include 'trinity' in the name as a safety precaution! )
#
# --full_cleanup :only retain the Trinity fasta file, rename as ${output_dir}.Trinity.fasta
#
# --cite :show the Trinity literature citation
#
# --verbose :provide additional job status info during the run.
#
# --version :reports Trinity version (Trinity-v2.15.1) and exits.
#
# --show_full_usage_info :show the many many more options available for running Trinity (expert usage).
#
#
###############################################################################
#
# *Note, a typical Trinity command might be:
#
# Trinity --seqType fq --max_memory 50G --left reads_1.fq --right reads_2.fq --CPU 6
#
# (if you have multiple samples, use --samples_file ... see above for details)
#
# and for Genome-guided Trinity, provide a coordinate-sorted bam:
#
# Trinity --genome_guided_bam rnaseq_alignments.csorted.bam --max_memory50G
# --genome_guided_max_intron 10000 --CPU 6
#
# see: /local/cluster/conda-envs/envs/trinity-2.15.1/opt/trinity-2.15.1/sample_data/test_Trinity_Assembly/
# for sample data and 'runMe.sh' for example Trinity execution
#
# For more details, visit: http://trinityrnaseq.github.io
#
###############################################################################
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software ref: https://github.com/trinityrnaseq/trinityrnaseq
software ref: https://github.com/trinityrnaseq/trinityrnaseq/wiki
research ref: https://doi.org/10.1038/nbt.1883
