Based on a discussion in this GitHub Issue, I’ve initiated some tissue-specific transcriptome assemblies with our current geoduck data.

Job was run on Mox and rsynced to my folder on Gannet.

FastA index files were generated separately via samtools faidx Trinity.fasta (didn’t think about it at the time so did not add to SBATCH script).

SBATCH script:

20190215_geo_trinity_heart.sh (GitHub)


#!/bin/bash
## Job Name
#SBATCH --job-name=trinity_20190215
## Allocation Definition
#SBATCH --account=coenv
#SBATCH --partition=coenv
## Resources
## Nodes
#SBATCH --nodes=2
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=5-00:00:00
## Memory per node
#SBATCH --mem=120G
##turn on e-mail notification
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samwhite@uw.edu
## Specify the working directory for this job
#SBATCH --workdir=/gscratch/scrubbed/samwhite/outputs/20190215_trinity_geoduck_heart_RNAseq

# Load Python Mox module for Python module availability

module load intel-python3_2017

# Document programs in PATH (primarily for program version ID)

date >> system_path.log
echo "" >> system_path.log
echo "System PATH for $SLURM_JOB_ID" >> system_path.log
echo "" >> system_path.log
printf "%0.s-" {1..10} >> system_path.log
echo ${PATH} | tr : \\n >> system_path.log

data_dir=/gscratch/scrubbed/samwhite/data/P_generosa/RNAseq
trinity_dir=/gscratch/srlab/programs/Trinity-v2.8.3
assembly_stats=assembly_stats.txt

# Run Trinity
${trinity_dir}/Trinity \
--trimmomatic \
--seqType fq \
--max_memory 120G \
--CPU 56 \
--left \
${data_dir}/Geoduck-heart-RNA-1_S2_L001_R1_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-2_S10_L002_R1_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-3_S18_L003_R1_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-4_S26_L004_R1_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-5_S34_L005_R1_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-6_S42_L006_R1_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-7_S50_L007_R1_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-8_S58_L008_R1_001.fastq.gz \
--right \
${data_dir}/Geoduck-heart-RNA-1_S2_L001_R2_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-2_S10_L002_R2_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-3_S18_L003_R2_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-4_S26_L004_R2_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-5_S34_L005_R2_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-6_S42_L006_R2_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-7_S50_L007_R2_001.fastq.gz,\
${data_dir}/Geoduck-heart-RNA-8_S58_L008_R2_001.fastq.gz

# Assembly stats
${trinity_dir}/util/TrinityStats.pl trinity_out_dir/Trinity.fasta \
> ${assembly_stats}

RESULTS

Output folder:

20190215_trinity_geoduck_heart_RNAseq/

Trinity assembly:

20190215_trinity_geoduck_heart_RNAseq/trinity_out_dir/Trinity.fasta(FastA)
- FastA index (samtools faidx):
  - 20190215_trinity_geoduck_heart_RNAseq/trinity_out_dir/Trinity.fasta.fai

Assembly stats:

20190215_trinity_geoduck_heart_RNAseq/assembly_stats.txt




################################
## Counts of transcripts, etc.
################################
Total trinity 'genes':  178536
Total trinity transcripts:  300280
Percent GC: 35.78

########################################
Stats based on ALL transcript contigs:
########################################

    Contig N10: 6032
    Contig N20: 4207
    Contig N30: 3130
    Contig N40: 2364
    Contig N50: 1738

    Median contig length: 448
    Average contig: 913.32
    Total assembled bases: 274251211


#####################################################
## Stats based on ONLY LONGEST ISOFORM per 'GENE':
#####################################################

    Contig N10: 4878
    Contig N20: 3211
    Contig N30: 2293
    Contig N40: 1631
    Contig N50: 1146

    Median contig length: 382
    Average contig: 715.95
    Total assembled bases: 127822700

Will likely run the resulting assembly through Trinnotate and Transdecoder to try to get a more refined assembly.

Will also run BUSCO on the refined assembly to evaluate its completeness.

Will also explore combining all of the geoduck tissue-specific transcriptome assemblies using DRAP (mentioned/suggested by Katherine in that GitHub issue).