MethylC-analyzer is a analyzer developing for analysing DNA methylation on WGBS and RRBS, it could utilize not only individual sample also do comparison between two groups.
MethylC-analyzer will produce 7 analysis and each analysis contains CG, CHG and CHH 3 context:
Linux or Mac OS Environment
CPU:No special restrictions, but CPU has 16 cores is more efficient
MEM:12GB or higer (for plant sample) / 256GB or higher (for human sample)
Python 3.9 and R>3.6
Python module
numpy
pandas
math
scipy
matplolib
argparse
glob
pyBigWig
PyQt5
seaborn
R package
ComplexHeatmap
gplots
ggplot2
viridis
:pushpin: Recommend using docker image to avoid enveioment conflict
docker pull peiyulin/methylc
Recommend to create a conda environment somewhere on your disk, and then activate it.
$ conda create -n methylC_analyzer_env python=3.9
$ conda activate methylC_analyzer_env
Download the source code and install the requirements.
$ git clone https://github.com/RitataLU/MethylC-analyzer.git
Install Package - Run MethylC-analyzer/requirements/base.txt
ex: sh MethylC-analyzer/requirements/base.txt
CGmap format
chr1 G 13538 CG CG 0.6 6 10 chr1 G 13539 CHG CC 0.0 0 9 chr1 G 13541 CHH CA 0.0 0 9 chr1 G 13545 CHH CA 0.0 0 8
The methylation calling files from other aligners/callers, MethylC-analyzer provides a python script (methcalls2CGmap.py) to convert them to CGmap.gz, including CX report files generated by Bismark, the methylation calls generated by methratio.py in BSMAP (v2.73), and the TSV files exported from the methylation calling status with METHimpute.
usage: methcalls2CGmap.py [-h] [-n FILENAME]
[-f {bismark,bsmap,methimpute}]
optional arguments:
-h, --help show this help message and exit
Input format:
-n FILENAME, --filename FILENAME
the file name that the users want to convert to CGMap
format
-f {bismark,bsmap,methimpute}, --format {bismark,bsmap,methimpute}
the type of file to CGmap
Example for converting methylation calls to CGmap.gz:
# bismark to CGmap.gz
python methcalls2CGmap.py -n CX_report.txt.gz -f bismark
2.Gene annotation (GTF)
gene annotation in GTF file: User can downloaded from ensemble FTP
Please follow the tutorial of example use case
MethylC-analyzer docker tutorial :mega:Recommend
MethylC-analyzer command line tutorial
Make a sample description file and name it as "samples_list.txt" in the location where methylc.py script. The file should be tab-delimited without a header.
Sample Description File (tab-delimited, no header in the first line) Sample list ( sample_name CGmap_location group )
wt1 wt1.CGmap.gz WT
wt2 wt2.CGmap.gz WT
wt3 wt3.CGmap.gz WT
met1_1 met1_1.CGmap.gz met1
met1_2 met1_2.CGmap.gz met1
met1_3 met1_3.CGmap.gz met1
Usage:
$ python MethylC.py samples_list.txt TAR10.genes.gtf
usage: MethylC_new.py [-h] [-a GROUP1] [-b GROUP2] [-d DEPTH] [-r REGION]
[-q QUALIFIED] [-context CONTEXT] [-hc HEATMAP_CUTOFF]
[-dmrc DMR_CUTOFF] [-test TESTMETHOD] [-pvalue PVALUE]
[-bs BIN_SIZE] [-p PROMOTER_SIZE]
samples_list input_gtf_file
positional arguments:
samples_list samples CGmap description
input_gtf_file path of gene annotation
## Arguments
-h, --help show this help message and exit
-a GROUP1 Name of group1
-b GROUP2 Name of group2
-d DEPTH Minimum depth of sites. Default=4
-r REGION Size of region. Default=500
-q QUALIFIED Minimum sites within a region. Default=4
-context CONTEXT Context used. Default=CG
-hc HEATMAP_CUTOFF Methylation cutoff of PCA & Heatmap. Default = 0.2
-dmrc DMR_CUTOFF Methylation cutoff of DMR. Default = 0.1
-test TESTMETHOD DMR testing method. 0:TTest, 1:KS, 2:MWU. Default=0
-pvalue PVALUE p-value cutoff for identifying DMR. Default = 0.05
-bs BIN_SIZE Bin size of chrView and Metaplot. Default = 1000000
-p PROMOTER_SIZE promoter_size
## activate interface (Users select analysis that want to process)
Heatmap & PCA Analysis? (y/n): y
Identify DMR? (y/n): y
Identify DMG? (y/n): y
Use Fold Enrichment Analysis? (y/n): y
Chromosome View Analysis? (y/n): y
Metaplot Analysis? (y/n): y
enter experimental group name analysis: met1
enter control group name analysis: WT
Output Figures
The average methylation in 3 context (CG, CHG, CHH)
PCA & Heatmap show variable region among samples
PCA:
Heatmap:
The distribution fo DNA methylation difference on each chromosome
Summary of dentifying Differentially Methylated Regions (DMRs) & Differentially Methylated Genes (DMGs)