Skip to content

Latest commit

 

History

History
340 lines (268 loc) · 13 KB

README.md

File metadata and controls

340 lines (268 loc) · 13 KB
Raw

msPIPE

  • Methylation analysis pipeline for WGBS data

Requirements


Or you can use msPIPE on docker without having to prepare the environment. < Recommended>
👉 HOW TO USE msPIPE on docker 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 TRIMMED_FILE_* parameters. ([LIB1] on below format)
    • Without TRIMMED_FILE_* 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