This is a quick and dirty (i.e. no adaptor/quality trimming) assessment of all of our Crassostrea irginica bisulfite sequencing efforts to date in order to get a rough idea of the methylation mapping, per this GitHub issue. Ran Bismark on Mox on a series of subset of the reads:

100k
500k
1M
2M
5M
10M

This was run as both paired end and single end, as the paired end analysis didn’t actually produce a mapping efficiency (but does have alignment percentages), but the single end ended up yielding mapping efficiencies. Single end analysis consisted of just the R1 reads.

SBATCH scripts are linked and pasted below:

Paired end script:


#!/bin/bash
## Job Name
#SBATCH --job-name=bismark
## Allocation Definition
#SBATCH --account=coenv
#SBATCH --partition=coenv
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=10-00:00:00
## 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/20190222_cvirginica_pe_bismark

# 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


# Directories and programs
wd=$(pwd)
bismark_dir="/gscratch/srlab/programs/Bismark-0.19.0"
bowtie2_dir="/gscratch/srlab/programs/bowtie2-2.3.4.1-linux-x86_64/"
samtools="/gscratch/srlab/programs/samtools-1.9/samtools"
reads_dir="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/"

## genomes

genome="/gscratch/srlab/sam/data/C_virginica/genomes/"

## An array of subsets of reads to use in bismark:
## 100k, 500k, 1M, 2M, 5M, 10M
subset_array=(100000 500000 1000000 2000000 5000000 10000000)

## FastQ Files
R1="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/cvir_bsseq_all_pe_R1.fastq.gz"
R2="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/cvir_bsseq_all_pe_R2.fastq.gz"

## FastQ files lists
R1_list="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/cvir_bsseq_pe_all_R1.list"
R2_list="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/cvir_bsseq_pe_all_R2.list"

# Check for existence of previous concatenation
# If they exist, delete them

for file in ${R1} ${R1_list} ${R2} ${R2_list}
do
  if [ -e ${file} ]; then
    rm ${file}
  fi
done

# Concatenate R1 reads and generate lists of FastQs
for fastq in ${reads_dir}*R1*.gz
do
  echo ${fastq} >> ${R1_list}
  cat ${fastq} >> ${R1}
done

# Concatenate R2 reads and generate lists of FastQs
for fastq in ${reads_dir}*R2*.gz
do
  echo ${fastq} >> ${R2_list}
  cat ${fastq} >> ${R2}
done

# Run bismark using bisulftie-converted genome

for subset in ${subset_array[@]}
do
    mkdir subset_${subset}
    cd subset_${subset}
    ${bismark_dir}/bismark \
    --path_to_bowtie ${bowtie2_dir} \
    --genome ${genome} \
    --score_min L,0,-0.6 \
    -u ${subset} \
    -p 28 \
    -1 ${R1} \
    -2 ${R2} \
    2> subset_${subset}_summary.txt

    # Deduplicate bam files
    ${bismark_dir}/deduplicate_bismark \
    --bam \
    --single \
    *.bam

    # Methylation extraction

    ${bismark_dir}/bismark_methylation_extractor \
    --bedgraph \
    --counts \
    --scaffolds \
    --remove_spaces \
    --multicore 28 \
    --buffer_size 75% \
    *deduplicated.bam

    # Bismark processing report

    ${bismark_dir}/bismark2report

    #Bismark summary report

    ${bismark_dir}/bismark2summary

    # Sort files for methylkit and IGV

    find *deduplicated.bam \
    | xargs basename -s .bam \
    | xargs -I{} ${samtools} \
    sort \
    --threads 28 \
    {}.bam \
    -o {}.sorted.bam

    # Index sorted files for IGV
    # The "-@ 56" below specifies number of CPU threads to use.

    find *.sorted.bam \
    | xargs basename -s .sorted.bam \
    | xargs -I{} ${samtools} \
    index -@ 28 \
    {}.sorted.bam
    cd ${wd}
done

Single end script:


#!/bin/bash
## Job Name
#SBATCH --job-name=bismark
## Allocation Definition
#SBATCH --account=coenv
#SBATCH --partition=coenv
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=10-00:00:00
## 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/20190222_cvirginica_se_bismark

# 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


# Directories and programs
wd=$(pwd)
bismark_dir="/gscratch/srlab/programs/Bismark-0.19.0"
bowtie2_dir="/gscratch/srlab/programs/bowtie2-2.3.4.1-linux-x86_64/"
samtools="/gscratch/srlab/programs/samtools-1.9/samtools"
reads_dir="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/"

## genomes

genome="/gscratch/srlab/sam/data/C_virginica/genomes/"

## An array of subsets of reads to use in bismark:
## 100k, 500k, 1M, 2M, 5M, 10M
subset_array=(100000 500000 1000000 2000000 5000000 10000000)

## FastQ Files
se_reads="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/cvir_bsseq_all_R1.fastq.gz"

## FastQ files lists
R1_list="/gscratch/scrubbed/samwhite/data/C_virginica/BSseq/cvir_bsseq_all_R1.list"

# Check for existence of previous concatenation
# If they exist, delete them

for file in ${se_reads} ${R1_list}
do
  if [ -e ${file} ]; then
    rm ${file}
  fi
done

# Concatenate R1 reads and generate lists of FastQs
for fastq in ${reads_dir}*R1*.gz
do
  echo ${fastq} >> ${R1_list}
  cat ${fastq} >> ${se_reads}
done

# Run bismark using bisulftie-converted genome

for subset in ${subset_array[@]}
do
    mkdir subset_${subset}
    cd subset_${subset}
    ${bismark_dir}/bismark \
    --path_to_bowtie ${bowtie2_dir} \
    --genome ${genome} \
    --score_min L,0,-0.6 \
    -u ${subset} \
    -p 28 \
    ${se_reads} \
    2> subset_${subset}_summary.txt

    # Deduplicate bam files
    ${bismark_dir}/deduplicate_bismark \
    --bam \
    --single \
    *.bam

    # Methylation extraction

    ${bismark_dir}/bismark_methylation_extractor \
    --bedgraph \
    --counts \
    --scaffolds \
    --remove_spaces \
    --multicore 28 \
    --buffer_size 75% \
    *deduplicated.bam

    # Bismark processing report

    ${bismark_dir}/bismark2report

    #Bismark summary report

    ${bismark_dir}/bismark2summary

    # Sort files for methylkit and IGV

    find *deduplicated.bam \
    | xargs basename -s .bam \
    | xargs -I{} ${samtools} \
    sort \
    --threads 28 \
    {}.bam \
    -o {}.sorted.bam

    # Index sorted files for IGV
    # The "-@ 56" below specifies number of CPU threads to use.

    find *.sorted.bam \
    | xargs basename -s .sorted.bam \
    | xargs -I{} ${samtools} \
    index -@ 28 \
    {}.sorted.bam
    cd ${wd}
done

RESULTS

Output folder:

20190222_cvirginica_se_bismark/

Bedgraphs for each subset:

There is a bedgraph for each single-end subset. I will not link them all. Here’s an example for the 10,000,000 subset:
- 20190222_cvirginica_se_bismark/subset_10000000/cvir_bsseq_all_R1_bismark_bt2.deduplicated.bedGraph.gz

Code used to pull mapping efficiencies:

find ./*_cvirginica_* -name "subset*.txt" -print0 | xargs -0 grep "Mapping efficiency"
./20190222_cvirginica_se_bismark/subset_500000/subset_500000_summary.txt:Mapping efficiency:    18.6%
./20190222_cvirginica_se_bismark/subset_10000000/subset_10000000_summary.txt:Mapping efficiency:    16.1%
./20190222_cvirginica_se_bismark/subset_1000000/subset_1000000_summary.txt:Mapping efficiency:  19.2%
./20190222_cvirginica_se_bismark/subset_5000000/subset_5000000_summary.txt:Mapping efficiency:  18.6%
./20190222_cvirginica_se_bismark/subset_100000/subset_100000_summary.txt:Mapping efficiency:    18.5%
./20190222_cvirginica_se_bismark/subset_2000000/subset_2000000_summary.txt:Mapping efficiency:  19.5%

Reads Subset	Mapping Efficiency (%)
100000	18.5
500000	18.6
1000000	19.2
2000000	19.5
5000000	18.6
10000000	16.1

Read Type (PE or SE)	Reads Subset	C methylated in CpG context (%)
pe	100000	73.3
pe	500000	73.6
pe	1000000	73.0
pe	2000000	72.5
pe	5000000	68.9
pe	10000000	60.6
se	100000	75.7
se	500000	75.7
se	1000000	75.5
se	2000000	75.5
se	5000000	72.0
se	10000000	75.4