- Methylation analysis pipeline for WGBS data
- Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)
- Samtools (http://www.htslib.org/)
- Bismark (https://github.com/FelixKrueger/Bismark)
- cutadapt (https://cutadapt.readthedocs.io/en/stable/)
Or you can use msPIPE on docker without having to prepare the environment.
👉 HOW TO USE msPIPE on docker
git clone https://github.com/jkimlab/msPIPE.git
The parameter file contains the information necessary for pipeline execution.
### INPUT PARAMETER FILE FORMAT ###
## [DMR]
## ANALYSIS1 = Two sample names for DMR analysis
## ANALYSIS2 = 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
-
Running command
/PATH/TO/msPIPE/msPIPE.py -p params.conf -o OUTDIR &> logs
-
msPIPE options
./msPIPE/msPIPE.py -h usage: msPIPE.py [-h] --param params.conf --out PATH [--core int] [--qvalue float] [--skip_trimming] [--skip_mapping] [--skip_calling] [--skip_HMR] [--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 --skip_mapping skip the bismark mapping --skip_calling skip the methylation calling --skip_HMR skip the HMR analysis --skip_DMR skip the DMR analysis
-
Running example using mouse rod WGBS data from Corso-DĂaz, Ximena et al.
-
GEO accession : GSE134873
-
Used samples
-
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
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
#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.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
- The parameter file must be written based on the internal path of the docker container and placed within the output dir.
- All paths must be expressed as absolute paths.
- Replace the '/PATH/TO/*' with a directory path on your local server.