Ran a de novo assembly on our HiSeq data from Hollie’s juvenile EPI 115 sample “super low OA”. This was done for Christian to use in some long, non-coding RNA (lncRNA) analysis.

Trimming of the HiSeq data was performed via Trinity, using the --trimmomatic option.

SBATCH script (GitHub):

20190409_trinity_pgen_EPI115_RNAseq.sh

#!/bin/bash
## Job Name
#SBATCH --job-name=trin_epi115
## Allocation Definition
#SBATCH --account=coenv
#SBATCH --partition=coenv
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=30-00:00:00
## 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/20190409_trinity_pgen_EPI115_RNAseq

# Exit script if a command fails
set -e

# 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

# User-defined variables
reads_dir=/gscratch/scrubbed/samwhite/data/P_generosa/RNAseq/epi_115
threads=28
assembly_stats=assembly_stats.txt

# Paths to programs
trinity_dir="/gscratch/srlab/programs/Trinity-v2.8.3"
samtools="/gscratch/srlab/programs/samtools-1.9/samtools"


## Inititalize arrays
R1_array=()
R2_array=()

# Variables for R1/R2 lists
R1_list=""
R2_list=""

# Create array of fastq R1 files
R1_array=(${reads_dir}/*_R1_*.gz)

# Create array of fastq R2 files
R2_array=(${reads_dir}/*_R2_*.gz)

# Create list of fastq files used in analysis
## Uses parameter substitution to strip leading path from filename
for fastq in ${reads_dir}/*.gz
do
  echo ${fastq##*/} >> fastq.list.txt
done

# Create comma-separated lists of FastQ reads
R1_list=$(echo ${R1_array[@]} | tr " " ",")
R2_list=$(echo ${R2_array[@]} | tr " " ",")


# Run Trinity
${trinity_dir}/Trinity \
--trimmomatic \
--seqType fq \
--max_memory 120G \
--CPU ${threads} \
--left \
${R1_list} \
--right \
${R2_list}

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

# Create gene map files
${trinity_dir}/util/support_scripts/get_Trinity_gene_to_trans_map.pl \
trinity_out_dir/Trinity.fasta \
> trinity_out_dir/Trinity.fasta.gene_trans_map

# Create FastA index
${samtools} faidx \
trinity_out_dir/Trinity.fasta

RESULTS

This took ~12hrs to complete.

I’ll pass this along to Steven/Christian, since this was done for Christian to use in some long, non-coding RNA (lncRNA) analysis.

I’ll also probably just take this through the annotation pipeline, since it’s not difficult, nor time consuming.

Output folder:

20190409_trinity_pgen_EPI115_RNAseq/

Trinity FastA:

20190409_trinity_pgen_EPI115_RNAseq/trinity_out_dir/Trinity.fasta

Trinity FastA index file:

20190409_trinity_pgen_EPI115_RNAseq/trinity_out_dir/Trinity.fasta.fai

Assembly stats (text):

20190409_trinity_pgen_EPI115_RNAseq/assembly_stats.txt

################################
## Counts of transcripts, etc.
################################
Total trinity 'genes':  199765
Total trinity transcripts:  320691
Percent GC: 36.50

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

    Contig N10: 5803
    Contig N20: 3937
    Contig N30: 2902
    Contig N40: 2153
    Contig N50: 1581

    Median contig length: 411
    Average contig: 842.06
    Total assembled bases: 270039845


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

    Contig N10: 4344
    Contig N20: 2835
    Contig N30: 1998
    Contig N40: 1433
    Contig N50: 1002

    Median contig length: 339
    Average contig: 641.10
    Total assembled bases: 128069660

List of input FastQs (text):

20190409_trinity_pgen_EPI115_RNAseq/fastq.list.txt

Geoduck-juvenile-OA-exposure-RNA-EPI-115-1_S4_L001_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-1_S4_L001_R2_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-2_S12_L002_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-2_S12_L002_R2_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-3_S20_L003_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-3_S20_L003_R2_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-4_S28_L004_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-4_S28_L004_R2_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-5_S36_L005_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-5_S36_L005_R2_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-6_S44_L006_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-6_S44_L006_R2_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-7_S52_L007_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-7_S52_L007_R2_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-8_S60_L008_R1_001.fastq.gz
Geoduck-juvenile-OA-exposure-RNA-EPI-115-8_S60_L008_R2_001.fastq.gz