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Trinity

Trinity 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 Homepage

Available Modules

module load Trinity/2.8.4-gimkl-2017a.lua

Trinity, developed at the Broad Institute and the Hebrew University of Jerusalem, performs de novo reconstruction of transcriptomes from RNA-seq data. It 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.

General documentation for running Trinity can be found on their GitHub page here.

Running Trinity on NeSI

The recommended approach for running Trinity on NeSI is to split the run into two separate job submissions. The first submission will run Trinity Phase 1 (read clustering) and the second submission will run Trinity Phase 2 (assembling read clusters). We have observed faster run times and reduced core hour usage when applying this approach to benchmark data, compared to running both phases in one multithreaded job (see the Benchmarks section below).

File system considerations

You should run Trinity within your nobackup project directory, which has no limit on disk space usage but does have a file count quota. Trinity creates a large number of files, particularly in the "read_partitions" directory, thus it is important that you Contact our Support Team before running Trinity on NeSI, as we may need to increase your default file count quota.

Quality Control

We must stress the importance of read QC prior to running the assembly otherwise it is likely to fail or take a very long time to complete.

Running Trinity Phase 1

Trinity Phase 1 can be broken into three main components:

  • Initial in silico normalisation step and kmer counting
  • Inchworm
  • Chrysalis

So far we have found no reason to run each component individually since they have been observed to require similar resources. This phase typically has high memory requirements and supports multithreading in places.

The following Slurm script is a template for running Trinity Phase 1

Note  :

  • --cpus-per-task and --mem defined in the following example are just place holders. 
  • Use a subset of your sample, run a test first to find the suitable/required amount of CPUs and memory for your dataset
#!/bin/bash -e
#SBATCH --job-name=trinity-phase1
#SBATCH --account=nesi12345   # your NeSI project code
#SBATCH --time=30:00:00       # maximum run time
#SBATCH --ntasks=1            # always 1
#SBATCH --cpus-per-task=16    # number of threads to use for Trinity
#SBATCH --mem=220G            # maximum memory available to Trinity
#SBATCH --hint=nomultithread  # disable hyper-threading

# load a Trinity module
module load Trinity/2.14.0-gimkl-2022a

# run trinity, stop before phase 2
srun Trinity --no_distributed_trinity_exec \
  --CPU ${SLURM_CPUS_PER_TASK} --max_memory 200G \
  [your_other_trinity_options]

The extra Trinity arguments are:

  • --no_distributed_trinity_exec tells Trinity to stop before running Phase 2
  • --CPU ${SLURM_CPUS_PER_TASK} tells Trinity to use the number of CPUs specified by the sbatch option --cpus-per-task (i.e. you only need to update it in one place if you change it)
  • --max_memory should be the same (or maybe slightly lower, so you have a small buffer) than the value specified with the sbatch option --mem
  • [your_other_trinity_options] should be replaced with the other trinity options you would usually use, e.g. --seqType fq, etc.

Running Trinity Phase 2

Upstream documentation for running Trinity Phase 2 in parallel can be found here.

Trinity Phase 2 performs all the mini-assemblies in parallel. This phase consists of a large number (e.g. tens or hundreds of thousands) of commands that can be executed in parallel, each having independent inputs and outputs (embarrassingly parallel).

By default, Phase 2 will be run in the same way as Phase 1, i.e. using multiple threads on a single compute node to work through the list of Phase 2 commands that need to be run. However, these commands are very I/O intensive and can easily saturate the I/O bandwidth of a single node. By utilising Trinity's "grid mode" we can distribute these commands across many compute nodes, accessing a higher total I/O bandwidth than is possible from a single node, which appears to improve performance considerably. We have installed HPC GridRunner (recommended in the Trinity documentation) to achieve this.

HPC GridRunner runs a master process, submitted as its own Slurm job, that works by splitting the Phase 2 commands into batches of a certain size (specified by the user) and submits these batches of commands to the Slurm queue as separate jobs (referred to here as sub-jobs). The user can configure how many sub-jobs are allowed to be in the Slurm queue at any given time (so as not to overload the queue). Thus when a sub-job finishes, HPC GridRunner will submit another, maintaining the requested number of sub-jobs in the queue at any given time, until all commands have been run.

An example configuration script for HPC GridRunner follows. This script was tested and worked with our benchmark case but some parameters may need to be adjusted, such as memory and time requirements. Note the Trinity documentation suggested each command will need a maximum of 1 GB memory, however we observed some commands spiking above 4 GB. This could vary depending on your inputs.

[GRID]
# grid type:
gridtype=SLURM

# template for a grid submission
# make sure:
#     --partition is chosen appropriately for the resource requirements 
#       (here we choose either large or bigmem, whichever is available first)
#     --ntasks and --cpus-per-task should always be 1
#     --mem may need to be adjusted
#     --time may need to adjusted
#       (must be enough time for a batch of commands to finish)
#     --account should be your NeSI project code
#     add other sbatch options as required
cmd=sbatch --partition=large,bigmem --mem=5G --ntasks=1 --cpus-per-task=1 --time=01:00:00 --account=nesi12345

# note -e error.file -o out.file are set internally, so dont set them in the above cmd.

#############################################################################
# settings below configure the Trinity job submission system, not tied to the grid itself.
#############################################################################

# number of grid submissions to be maintained at steady state by the Trinity submission system
max_nodes=100

# number of commands that are batched into a single grid submission job.
cmds_per_node=100

The important details are:

  • cmds_per_node is the size of each batch of commands, i.e. here each Slurm sub-job runs 100 commands and then exits
  • max_nodes is the number of sub-jobs that can be in the queue at any given time (each sub-job is single threaded, i.e. it uses just one core)
  • name this file SLURM.conf in the directory you will submit the job from
  • memory usage may be low enough that the sub-jobs can be run on either the large or bigmem partitions, which should improve throughput compared to bigmem alone

A template Slurm submission script for Trinity Phase 2 is shown below:

#!/bin/bash -e
#SBATCH --job-name=trinity-phase2grid
#SBATCH --account=nesi12345  # your NeSI project code
#SBATCH --time=30:00:00      # enough time for all sub-jobs to complete
#SBATCH --ntasks=1           # always 1 - this is the master process
#SBATCH --cpus-per-task=1    # always 1
#SBATCH --mem=20G            # memory requirements for master process
#SBATCH --partition=bigmem   # submit to an appropriate partition
#SBATCH --hint=nomultithread

# load Trinity and HPC GridRunner
module load Trinity/2.14.0-gimkl-2022a
module load HpcGridRunner/20210803

# run Trinity - this will be the master HPC GridRunner process that handles
#   submitting sub-jobs (batches of commands) to the Slurm queue
srun Trinity --CPU ${SLURM_CPUS_PER_TASK} --max_memory 20G \
  --grid_exec "hpc_cmds_GridRunner.pl --grid_conf ${SLURM_SUBMIT_DIR}/SLURM.conf -c" \
  [your_other_trinity_options]
  • This assumes that you named the HPC GridRunner configuration script SLURM.conf and placed it in the same directory that you submit this job from
  • The options --CPU and --max_memory aren't used by Trinity in "grid mode" but are still required to be set (i.e. it shouldn't matter what you set them to)

Benchmarks

Here we provide details of a number of Trinity assemblies that have been carried out on NeSI, in order to give a rough idea of how Trinity can perform on NeSI and an indication of its resource requirements.

Timings mentioned here should be taken as indicative only and, even if assembling the same sample again, would be expected to vary significantly depending on various factors, such as the load on the system and project fair share scores and priorities.

Test sample

We ran a small test job of 8 million paired reads. Although much smaller than usual this allowed us to quickly see the effect of making changes to the workflow and ran quickly enough that we could compare the default way of running Trinity to splitting the Trinity run into two phases and using "grid mode".

Phase 1 took less than 1 hour to complete, using 8 cores and 10 GB memory. For Phase 2 we requested 8 GB memory for the master process and 4 GB memory and 1 hour wall time for the sub-jobs. There were 195,741 mini-assemblies to run in Phase 2.

The table below summarises the timings for Phase 2, comparing the default, single node way to run Phase 2, to using Trinity's "grid mode".

Type of run Number of cores / grid specification Run time (hrs:mins:secs) Approximate core hour cost
Single node (default) 16 cores 24:09:36 387
Grid max_nodes=20; cmds_per_node=500 07:59:58 168
Grid max_nodes=40; cmds_per_node=500 04:10:45 171
Grid max_nodes=60; cmds_per_node=500 02:36:58 160
Grid max_nodes=80; cmds_per_node=500 02:14:46 182

This shows that performance is much better with Trinity's "grid mode". Not only are the run times significantly lower but the total number of core hours used is also much lower.

Marine sediment sample 1

This benchmark concerns the assembly of a marine sediment sample, containing two distinct microbial populations, from two distinct geographical locations, of approximately 286 million paired reads. The assembly was performed using the two-phase Trinity workflow discussed above, using those submission scripts as templates.

Phase 1 ran on 18 threads with 220 GB memory on the bigmem partition and took approximately 15 hours to complete.

For Phase 2, the master process ran on a single core with 20 GB memory on the bigmem partition. HPC GridRunner was configured with both cmds_per_node and max_nodes set to 100, with the sub-jobs running on either large or bigmem partitions and requesting 5 GB memory and 1 hour wall time each. The number of commands (mini-assemblies) that needed to be run during this phase was 2,020,460. Phase 2 took approximately 19 hours to complete (elapsed time) and cost around 1,800 core hours.

Marine sediment sample 2

This sediment sample, containing 303 million reads, was all from the same location and was taken from a treatment experiment. The assembly was performed using the two-phase Trinity workflow discussed above, using those submission scripts as templates.

There were 4,136,295 mini-assemblies to run in Phase 2. The master process requested 30 GB memory on the bigmem partition and HPC GridRunner was configured with both cmds_per_node and max_nodes set to 100. The sub-jobs ran on either the large or bigmem partitions and required 1 hour wall time and 5 GB memory each. Phase 2 took approximately 32 hours to complete (elapsed time) and cost around 3,100 core hours.