Trinity is used for 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 at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived.
The Trinity module at CSC also includes TransDecoder and Trinonate tools (in Taito only) to anlyze the results of a Trinity run.
Version on CSC's Servers
Puhti: 2.8.5 Taito: 2.3.2 , 2.4.0, 2.5.1, 2.6.5, 2.6.6
In Puhti, Trinity is set up with command:
module load biokit
The biokit module sets up a set of commonly used bioinformatics tools.
Trinity should be used using interactively in Taito-shell or preferably through the batch job system. Below is a sample batch job file for Trinity.
#!/bin/bash #SBATCH --job-name=trinity #SBATCH --output=output_%j.txt #SBATCH --error=errors_%j.txt #SBATCH --time=48:00:00 #SBATCH --ntasks=1 #SBATCH --nodes=1 #SBATCH --cpus-per-task=6 #SBATCH --mem=24000 #SBATCH --account=project_1234567 # # module load trinity Trinity --seqType fq --max_memory 22G --left reads.left.fq --right \ reads.right.fq --SS_lib_type RF --CPU $SLURM_CPUS_PER_TASK \ --output trinity_run_out --grid_exec sbatch_commandlist
The command script above reserves 6 computing cores from one node for the job. The maximal run time of the sample job here is 48 hours.
About 4 GB of memory is reserved for each core so the total memory reservation is 6 * 4 GB= 24 GB. In Puhti you must use batch job option
--account= to define the project to be used. You should replace project_1234567 used in the example, with your own project. You can check your
projects with command:
In the actual Trinity command the number, of computing cores to be used (--CPU) is set using environment variable: $SLURM_CPUS_PER_TASK. This variable contains the value set the --cpus-per-task SLURM option.
In Puhti you can also use distributed computing to speed up the trinity job. When definition:
is added to the command, some phases of the analysis tasks are executed as a set of parallel subjobs. For large Trinity tasks the settings of the sbatch_commandlist tool are too limited. In these cases replace sbatch_commandlist with sbatch_commandlist_trinity.
When Trinity is executed with --grid_exec option in generates large amount of temporary files and it is very likely, that you will exceed the default limit of 100 000 files. Thus it is advisable to apply for a larger file number quota for Puhti scratch before submitting large Trinity jobs. You can send the request to firstname.lastname@example.org.
When the batch job file is ready, it can be submitted to the batch queue system with command:
More information about running batch jobs, can be found from the chapter three of the Taito user guide.
Please check the Trinity site to get hints for estimating the required resources,
Using autoTrinotate in Taito
In taito you can analyse the results of your Trinity job with autoTrininotate.
You must first create Trinotate-SQLite database that will be used to store the results of your autoTrinotate.
This database is created with command:
(Note that the command above is different as what is used in the Trinotate home page as the command has been slightly modified for Taito)
After these settings you can launch autoTrinotate with command:
$TRINOTATE_HOME/auto/autoTrinotate.pl --Trinotate_sqlite my_db_name.sqlite --transcripts transcripts.fasta --gene_to_trans_map gene_to_trans_map --conf $TRINOTATE_HOME/auto/conf.txt --CPU 4
Note that autoTrinotate analysis can require much resources so you should execute the command in Taito-shell or as batch job.
AutoTrioritontate produces a SQLite database file that can be further analyzed with command: