Identifying focal full methylation of cell subpopulation and inferring fraction
DNA methylation patterns within a cell population from individual somatic tissues are highly heterogeneous and polymorphic. We developed CellMethy method to quantify fraction of focal full methylation of cell subpopulation (FMC fraction) and identify fully methylated regions of cell subpopulation (CellMethy) based on single base resolution DNA methylation data. This script is used for infering fraction of focal full methylation cell subpopulation.
Easy to start:
Inputfile: File sepearated by “\t” after bismark processed including read id, strand, chromosome, position of CpGc and methylation state (Z or z).
The result would be placed into input folder: outputfile.
If your fastq mapping is done with bismark and extracted methylation state, use following command:
python CellMethy.py -f inputfile -o outputfile
-f: The file name of input file after bismark processed, include five columns: read ID, strand, chromosome, position of CpG and methylation states Z (methylated) or z (unmethylated) separated by ‘\t’.
-c: Lowest coverage cutoff in each bin, default is 10.
-b: number of CpGs in each bin, default is 5.
-o: The file name of output file showing full methylation of cell subpopulation, include five columns: chromosome, start, end, FMC fraction, and CpGs number in the region separated by “\t”.
cd ~/CellMethy-*.*.* python ./CellMethy/bin/CellMethy.py -f ./data/data_file -b5 -c 10 -o myoutput
How to get Input file
If you have fastq data, you can mapping with bismark tools.
bismark ./referenceGenome --bowtie2 test.fastq -o test.sam bismark_methylation_extractor -s --comprehensive test.sam
The output file named “CpG_context_test.txt” is inputfile of CellMethy. The format of CellMethy inputfile include read id, strand, chromosome, position of CpGc and methylation state (Z or z) separated by ‘\t’.
Read1 + chr21 9827508 Z Read1 - chr21 9827503 z Read1 - chr21 9827484 z Read2 + chr21 9827434 Z Read2 + chr21 9827454 Z Read2 - chr21 9827483 z
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