CellMethy_v1
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
Options:
-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”.
examples:
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.
examples:
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
1 maintainer
Metadata
1 maintainer
Release files for CellMethy 1.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| CellMethy-1.2.0.tar.gz | 3.2 MB | Without provenance | Details for CellMethy-1.2.0.tar.gz |
Release files / CellMethy-1.2.0.tar.gz
| Download URL | CellMethy-1.2.0.tar.gz |
|---|---|
| Size | 3.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d61790eb9ce6245f8d94ec6eb99b47d0d551a83ddf31897ff4d7da0e0db2bc89
|
|
BLAKE2b-256 checksum How to use checksums |
6de3d1f715bad17815100e1a9da0210f31cadb0697f7abdb76bb95711badcff0
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for CellMethy 1.2.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
