msPIPE

• Methylation analysis pipeline for WGBS data

Requirements

• R >= 4.1.3, python2 and python3
• Trim Galore
• Samtools
• cutadapt
• bowtie, bowtie2
• Bismark
• BS-Seeker2
• Other required R packages can be installed via msPIPE/bin/script/Package_install.R.
  

Or you can use msPIPE on docker without having to prepare the environment. \< Recommended>
:point_right: HOW TO USE msPIPE on docker

Download

git clone https://github.com/jkimlab/msPIPE.git

Running

Running command

/PATH/TO/msPIPE/msPIPE.py -p params.conf -o OUTDIR 

Preparing an input parameter file

The parameter file must contain information necessary for pipeline execution.

• params_format.conf

  INPUT PARAMETER FILE FORMAT

  [DMR]
  ANALYSIS1 = sample1, sample2 (Two sample names for DMR analysis)
  ANALYSIS2 = sample1, sample3 (Two sample names for DMR analysis)

  [REFERENCE]
  UCSC_NAME = UCSC reference version name
  FASTA = [path to reference fasta file (not required)]
  GTF= [path to reference gtf file (not required)]

  [LIB1]
  SAMPLE_NAME = sample name
  LIB_NAME = library name
  LIB_TYPE = P or S (Paired-End or Single-Read)
  FILE_1 = [path to sequencing read file]
  FILE_2 = [path to sequencing read file]

Additional options

msPIPE options

 usage: msPIPE.py [-h] --param params.conf --out PATH [--core int] [--qvalue float] [--skip_trimming] [--program bismark or bs2]
                            [--bsmooth] [--skip_mapping] [--skip_calling] [--calling_data PATH] [--skip_GMA] [--skip_DMR]

optional arguments:
  -h, --help            show this help message and exit
  --param params.conf, -p params.conf
                        config format parameter file
  --out PATH, -o PATH   output directory
  --core int, -c int    core (default:5)
  --qvalue float, -q float
                        q-value cutoff (default:0.5)
  --skip_trimming       skip the trimgalore trimming
  --program bismark or bs2
                        program option for mapping & calling
  --bsmooth             use bsmooth for DMR analysis
  --skip_mapping        skip the bismark mapping
  --skip_calling        skip the methylation calling
  --calling_data PATH, -m PATH
                        methylCALL directory
  --skip_GMA            skip the Gene-Methyl analysis
  --skip_DMR            skip the DMR analysis

Software options

You can choose the software to be used for analysis by selecting the module.

1. WGBS reads mapping & calling (--program bismark or bs2).
   Use this option to select the tools to be used for mapping and calling of wgbs reads.
   --program bismark : use Bismark with bowtie2 (default)
   --program bs2 : use BS-Seeker2 with bowtie

2. Differential methylation analysis (--bsmooth)
   With this option, BSmooth is used to analyze instead of methylKit and inhouse script.

Skip options

You can leave out some pipeline steps with the --skip_\<STEP> option.
The main steps of the entire pipeline and the steps that can be omitted are as follows.

1. Check all input.
2. Prepare bisulfite-converted reference genome (Bismark or BS-Seeker2)
   • It will be skipped if the same assembly name of the bisulfite genome has already been created under msPIPE/reference/ directory.
3. WGBS reads trimming (TrimGalore)
   • Can drop with --skip_trimming option.
   • Trimmed reads to be used in mapping can be delivered through the TRIMMEDFILE* parameters. ([LIB1] on below format)
   • Without TRIMMEDFILE* parameters, the pipeline searches the files on the output directory.
4. WGBS reads mapping (Bismark or BS-Seeker2)
   • Can drop with --skip_mapping option.
   • Mapping file to be used in the next step can be delivered through the BAM_FILE parameter. ([LIB2] on below format)
   • Without BAM_FILE parameter, the pipeline searches the file on the output directory.
5. Methylation calling (Bismark or BS-Seeker2)
   • Can drop with --skip_calling option.
   • Pipeline use calling output on the output directory.
   • Other msPIPE calling output can be given with the --calling_data option.
6. Gene-Methylation analysis (Methylation profiling and Hypomethylated region analysis)
   • Can drop with --skip_GMA option.
7. Differential methylation analysis
   • Can drop with --skip_DMR option.

• params_format.conf

  INPUT WITH SKIP OPTIONS

  ...

  [LIB1]
  SAMPLE_NAME = sample name
  LIB_NAME = library name
  LIB_TYPE = P or S (Paired-End or Single-Read)
  TRIMMED_FILE_1 = [path to preprocessed read file (with --skip_trimming option)]
  TRIMMED_FILE_2 = [path to preprocessed read file (with --skip_trimming option)]

  [LIB2]
  SAMPLE_NAME = sample name
  LIB_NAME = library name
  LIB_TYPE = P or S (Paired-End or Single-Read)
  BAM_FILE = [path to bismark mapping file (with --skip_mapping option)]

Running example

• Running example using mouse rod WGBS data from Corso-Díaz, Ximena et al.

  GEO accession	sample-24M	sample-3M
  GSE134873	rep1, rep2, rep3	rep1, rep2, rep3

• params_mouse.conf
  Replace the '/PATH/TO/DATA' with a data path on your local server.

  [DMR]
  ANALYSIS1 = 24M, 3M
  
  [REFERENCE]
  UCSC_NAME = mm10
  
  [LIB1]
  SAMPLE_NAME = 24M
  LIB_NAME = 24M_rep1
  LIB_TYPE = P
  FILE_1 = /PATH/TO/DATA/SRX6589858_1.fastq.gz
  FILE_2 = /PATH/TO/DATA/SRX6589858_2.fastq.gz
  
  [LIB2]
  SAMPLE_NAME = 24M
  LIB_NAME = 24M_rep2
  LIB_TYPE = P
  FILE_1 = /PATH/TO/DATA/SRX6589859_1.fastq.gz
  FILE_2 = /PATH/TO/DATA/SRX6589859_2.fastq.gz
  
  [LIB3]
  SAMPLE_NAME = 24M
  LIB_NAME = 24M_rep3
  LIB_TYPE = P
  FILE_1 = /PATH/TO/DATA/SRX6589860_1.fastq.gz
  FILE_2 = /PATH/TO/DATA/SRX6589860_2.fastq.gz
  
  [LIB4]
  SAMPLE_NAME = 3M
  LIB_NAME = 3M_rep1
  LIB_TYPE = P
  FILE_1 = /PATH/TO/DATA/SRX6589850_1.fastq.gz
  FILE_2 = /PATH/TO/DATA/SRX6589850_2.fastq.gz
  
  [LIB5]
  SAMPLE_NAME = 3M
  LIB_NAME = 3M_rep2
  LIB_TYPE = P
  FILE_1 = /PATH/TO/DATA/SRX6589851_1.fastq.gz
  FILE_2 = /PATH/TO/DATA/SRX6589851_2.fastq.gz
  
  [LIB6]
  SAMPLE_NAME = 3M
  LIB_NAME = 3M_rep3
  LIB_TYPE = P
  FILE_1 = /PATH/TO/DATA/SRX6589852_1.fastq.gz
  FILE_2 = /PATH/TO/DATA/SRX6589852_2.fastq.gz

• Running command

   ./msPIPE/msPIPE.py -p params_mouse.conf -o mouse_result -c 5 -q 0.5

Analysis Output

All output created by msPIPE will be written to the methylCALL and Analysis directories in the given output directory. The output of pre-processing (read files processed by trimming and quality control), alignment, and methylation calling for each input library (named with LIB_NAME in config file) will be in methylCALL directory. The output of methylation analysis will be in Analysis directory.

• An example output structure of Analysis directory

  [Analysis]
  |- avg_methlevel.pdf
  |- [annotations]
  |- [sample1]
     |- ALL_TEXTFILES_AND_PLOTS_FOR_SAMPLE1
     |- [AroundTSS]
     |- [MethylSeekR]
  |- [sample2]
     |- ...
  |- [DMR]
     |- [sample1.sample2]

• Output files and directories in Analysis

  • avg_methlevel.pdf : a bar plot of average methylation level for CpG, CHG, and CHH context
  • annotations : a directory with information of genes, exons, introns, promoters, and intergenic regions in BED format files
  • sample1 : a directory with all results of methylation analysis for sample1
    • Average_methyl_lv.txt : average methylation level for each gene and its promoter
    • Avg_Genomic_Context_CpG.txt : average methylation level for each genomic context (gene, exon, intron, promoter, and intergenic)
    • CXX_methylCalls.bed : all methylation calls for each CX context (CXX is one of CpG, CHG, and CHH)
    • AroundTSS/meth_lv_3M.txt : for each gene, average methylation levels in bins around TSS (+/- 1500 bp)
    • MethylSeekR : a directory with all results for running MethylSeekR
    • UMR-Promoter.cnt.bed : the number of UMRs in each promoter region
    • UMR-Promoter.pos.bed : the genomic coordinates of UMRs in each promoter region
    • Circos.CpG_UMRs_LMRs.pdf : a circos plot for methylation level in whole-genome scale
    • Genomic_Context_CpG.pdf : a bar plot for average methylation level of each genomic context (gene, exon, intron, promoter, and intergenic)
    • hist_sample1_CXX.pdf : the distribution of methylation in CX context (CXX is one of CpG, CHG, and CHH)

If DMC/DMR analysis is performed, DMR directory will be created in Analysis directory. When the DMC/DMR analysis is performed, GO enrichment test will be carried out for the gene set with DMCs or DMRs in their promoter.

• Examples of output files and directories in DMR for comparison pair sample1 and sample2

  • sample1.sample2 : a directory with all results of DMC/DMR analysis, in this case sample1 will be treated as control and sample2 will be treated as case
    • DMR_q0.5.bed : information of differentially methylated regions
    • methylkit : output of running methylKit
    • DMC_q0.5.bed : filtered DMCs with q-value 0.5
    • hypoDMC_detailed_count_methyl.txt : the number of hypomethylated DMCs in each promoter (methylation level case < control)
    • hyperDMC_detailed_count_methyl.txt : the number of hypermethylated DMCs in each promoter (methylation level case > control)
    • intersection.DMC2Promoter.txt : a list of intersection between genes and DMCs
    • DMC_genelist.txt : a list of genes with DMCs overlapped their promoter region
    • DMC_gene.GOresult.txt : a text output of GO enrichment test for genes with DMCs from methylKit using g:Profiler
    • DMC_gene.GOresult.pdf : Plots of GO enrichment test for genes with DMCs from methylKit using g:Profiler
    • DMR_gene.GOresult.txt : a text output of GO enrichment test for genes with DMRs from BSmooth using g:Profiler
    • DMR_gene.GOresult.pdf : Plots of GO enrichment test for genes with DMRs from BSmooth using g:Profiler

      ---

Using Docker

Building msPIPE docker image

git clone https://github.com/jkimlab/msPIPE.git
cd msPIPE
docker build -t jkimlab/mspipe:latest .

• Or you can pull docker image from the docker hub

  docker pull jkimlab/mspipe:latest

Preparing an input parameter file for Docker

• The parameter file must be written based on the internal path of the docker container and placed within the output dir.
• Mount the volumes with '-v' options to deliver input data and receive output results.

• params_docker.conf

  [DMR]
  ANALYSIS1 = 24M, 3M
  
  [REFERENCE]
  UCSC_NAME = mm10
  
  [LIB1]
  SAMPLE_NAME = 24M
  LIB_NAME = 24M_rep1
  LIB_TYPE = P
  FILE_1 = /msPIPE/data/SRX6589858_1.fastq.gz
  FILE_2 = /msPIPE/data/SRX6589858_2.fastq.gz
  
  [LIB2]
  SAMPLE_NAME = 24M
  LIB_NAME = 24M_rep2
  LIB_TYPE = P
  FILE_1 = /msPIPE/data/SRX6589859_1.fastq.gz
  FILE_2 = /msPIPE/data/SRX6589859_2.fastq.gz
  
  ...
  

Running pipeline on Docker

 #docker run -v [local path]:[docker path] [docker image name] [msPIPE command]

 docker run -v /PATH/TO/INPUT/DATA:/msPIPE/data:ro -v /PATH/TO/REUSABLE/REFERENCE:/msPIPE/reference -v /PATH/TO/OUTDIR:/work_dir/ jkimlab/mspipe:latest msPIPE.py -p params_docker.conf -o result

• Mount the volumes with '-v' options to deliver input data and receive output results.
  • input data dir → /msPIPE/data
  • reusable references dir → /msPIPE/reference
  • output dir → /work_dir
• All local paths to mount volumes are must be expressed as absolute paths.

• Replace the '/PATH/TO/*' with a directory path on your local server.

  
  

  CONTACT

bioinfolabkr@gmail.com