Metagenome Assembly - P.generosa Water Sample Trimmed HiSeqX Data Using Megahit on Mox

Panopea generosa
geoduck
megahit
mox
metagenome
assembly
2019
Miscellaneous
Author

Sam White

Published

January 2, 2019

Attempting an assembly of our geoduck metagenomics HiSeqX data using Megahit (v.1.1.4) on a Mox node.

Assembly was run using default settings.

Assembly coverage was assessed using the following BBmap (v.38.34) scripts:

SBATCH script is linked here and pasted in full below:

#!/bin/bash
## Job Name
#SBATCH --job-name=megahit
## Allocation Definition
#SBATCH --account=srlab
#SBATCH --partition=srlab
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=5-00:00:00
## Memory per node
#SBATCH --mem=500G
##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/20190102_metagenomics_geo_megahit

# Load Python Mox module for Python module availability

module load intel-python3_2017

# Load Open MPI module for parallel, multi-node processing

module load icc_19-ompi_3.1.2

# SegFault fix?
export THREADS_DAEMON_MODEL=1

# 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


# variables
fastq_dir=/gscratch/srlab/sam/data/metagenomics/P_generosa
megahit=/gscratch/srlab/programs/megahit_v1.1.4_LINUX_CPUONLY_x86_64-bin/megahit
bbmap_dir=/gscratch/srlab/programs/bbmap_38.34

## Inititalize arrays
fastq_array_R1=()
fastq_array_R2=()

# Create array of fastq R1 files
for fastq in ${fastq_dir}/*R1*.gz; do
  fastq_array_R1+=(${fastq})
done

# Create array of fastq R2 files
for fastq in ${fastq_dir}/*R2*.gz; do
  fastq_array_R2+=(${fastq})
done

# Create comma-separated list of input files
R1_fastq_list=$(IFS=,; echo "${fastq_array_R1[*]}")
R2_fastq_list=$(IFS=,; echo "${fastq_array_R2[*]}")


# Run Megahit using paired-end reads
${megahit} \
-1 ${R1_fastq_list} \
-2 ${R2_fastq_list} \
--num-cpu-threads 28

# Determine coverage
## Align reads with BBmap BBwrap
${bbmap_dir}/bbwrap.sh \
ref=megahit_out/final.contigs.fa \
in1=${R1_fastq_list} \
in2=${R2_fastq_list} \
out=aln.sam.gz

## Output contig coverage
${bbmap_dir}/pileup.sh \
in=aln.sam.gz \
out=coverage.txt

RESULTS

This took a little less than a day to run:

Output folder:

Assembly (FastA):

Alignment (Gzipped SAM):

Coverage file (txt):

This assembly yielded 2,276,153 contigs (grep -c ">" final.contigs.fa).

Here’re some alignment stats from the BBmmap pileup.sh command (can be found at end of the slurm file (text)):

Reads:                                  163236894
Mapped reads:                           143703254
Mapped bases:                           16437148607
Ref scaffolds:                          2276153
Ref bases:                              2243059881

Percent mapped:                         88.034
Percent proper pairs:                   76.768
Average coverage:                       7.328
Standard deviation:                     27.892
Percent scaffolds with any coverage:    70.54
Percent of reference bases covered:     56.07

Also, ran Quast to assess assembly:

python \
/home/sam/software/quast-4.5/quast.py \
--threads=20 \
--min-contig=100 \
--labels=meta_megahit_assembly \
/mnt/gannet/Atumefaciens/20190102_metagenomics_geo_megahit/megahit_out/final.contigs.fa

Quast output folder:

Quast report (HTML):

Quast report (text):

Quast report.txt:

All statistics are based on contigs of size >= 100 bp, unless otherwise noted (e.g., "# contigs (>= 0 bp)" and "Total length (>= 0 bp)" include all contigs).

Assembly                    meta_megahit_assembly
# contigs (>= 0 bp)         2276153              
# contigs (>= 1000 bp)      496414               
# contigs (>= 5000 bp)      41075                
# contigs (>= 10000 bp)     12255                
# contigs (>= 25000 bp)     2797                 
# contigs (>= 50000 bp)     864                  
Total length (>= 0 bp)      2243059881           
Total length (>= 1000 bp)   1342986697           
Total length (>= 5000 bp)   487449312            
Total length (>= 10000 bp)  293688660            
Total length (>= 25000 bp)  154694058            
Total length (>= 50000 bp)  89125138             
# contigs                   2276153              
Largest contig              2035937              
Total length                2243059881           
GC (%)                      39.32                
N50                         1413                 
N75                         624                  
L50                         308641               
L75                         932685               
# N's per 100 kbp           0.00

Now, should probably BLASTn, identify ORFs, translate and BLASTp those ORFs.