pypgx 0.7.0

A Python package for pharmacogenomics research

Introduction

The main purpose of the PyPGx package is to provide a unified platform for pharmacogenomics (PGx) research.

The package is written in Python, and supports both command line interface (CLI) and application programming interface (API) whose documentations are available at the Read the Docs.

Your contributions (e.g. feature ideas, pull requests) are most welcome.

Author: Seung-been “Steven” Lee

Email: [e-mail]

License: MIT License

Installation

Following packages are required to run PyPGx:

Package	Anaconda	PyPI
fuc	✅	✅
scikit-learn	✅	✅
openjdk	✅	❌

There are various ways you can install PyPGx. The recommended way is via conda (Anaconda):

$ conda install -c bioconda pypgx

Above will automatically download and install all the dependencies as well. Alternatively, you can use pip (PyPI) to install PyPGx and all of its dependencies except openjdk (i.e. Java JDK must be installed separately):

$ pip install pypgx

Finally, you can clone the GitHub repository and then install PyPGx locally:

$ git clone https://github.com/sbslee/pypgx
$ cd pypgx
$ pip install .

The nice thing about this approach is that you will have access to development versions that are not available in Anaconda or PyPI. For example, you can access a development branch with the git checkout command. When you do this, please make sure your environment already has all the dependencies installed.

Archive file, semantic type, and metadata

In order to efficiently store and transfer data, PyPGx uses the ZIP archive file format (.zip) which supports lossless data compression. Each archive file created by PyPGx has a metadata file (metadata.txt) and a data file (e.g. data.tsv, data.vcf). A metadata file contains important information about the data file within the same archive, which is expressed as pairs of =-separated keys and values (e.g. Assembly=GRCh37):

Metadata	Description	Examples
Assembly	Reference genome assembly.	GRCh37, GRCh38
Control	Control gene.	VDR, chr1:10000-20000
Gene	Target gene.	CYP2D6, GSTT1
Platform	Genotyping platform.	WGS, Targeted, Chip
Program	Name of the phasing program.	Beagle
Samples	Samples used for inter-sample normalization.	NA07000,NA10854,NA11993
SemanticType	Semantic type of the archive.	CovFrame[CopyNumber], Model[CNV]

Notably, all archive files have defined semantic types, which allows us to ensure that the data that is passed to a PyPGx command (CLI) or method (API) is meaningful for the operation that will be performed. Below is a list of currently defined semantic types:

• CovFrame[CopyNumber]

  • CovFrame for storing target gene’s per-base copy number which is computed from read depth with control statistics.

  • Requires following metadata: Gene, Assembly, SemanticType, Platform, Control, Samples.

• CovFrame[DepthOfCoverage]

  • CovFrame for storing read depth for all target genes with SV.

  • Requires following metadata: Assembly, SemanticType, Platform.

• CovFrame[ReadDepth]

  • CovFrame for storing read depth for single target gene.

  • Requires following metadata: Gene, Assembly, SemanticType, Platform.

• Model[CNV]

  • Model for calling CNV in target gene.

  • Requires following metadata: Gene, Assembly, SemanticType, Control.

• SampleTable[Alleles]

  • TSV file for storing target gene’s candidate star alleles for each sample.

  • Requires following metadata: Platform, Gene, Assembly, SemanticType, Program.

• SampleTable[CNVCalls]

  • TSV file for storing target gene’s CNV call for each sample.

  • Requires following metadata: Gene, Assembly, SemanticType, Control.

• SampleTable[Genotypes]

  • TSV file for storing target gene’s genotype call for each sample.

  • Requires following metadata: Gene, Assembly, SemanticType.

• SampleTable[Phenotypes]

  • TSV file for storing target gene’s phenotype call for each sample.

  • Requires following metadata: Gene, SemanticType.

• SampleTable[Results]

  • TSV file for storing various results for each sample.

  • Requires following metadata: Gene, Assembly, SemanticType.

• SampleTable[Statistcs]

  • TSV file for storing control gene’s various statistics on read depth for each sample. Used for converting target gene’s read depth to copy number.

  • Requires following metadata: Control, Assembly, SemanticType, Platform.

• VcfFrame[Consolidated]

  • VcfFrame for storing target gene’s consolidated variant data.

  • Requires following metadata: Platform, Gene, Assembly, SemanticType, Program.

• VcfFrame[Imported]

  • VcfFrame for storing target gene’s raw variant data.

  • Requires following metadata: Platform, Gene, Assembly, SemanticType.

• VcfFrame[Phased]

  • VcfFrame for storing target gene’s phased variant data.

  • Requires following metadata: Platform, Gene, Assembly, SemanticType, Program.

Getting help

For detailed documentations on the CLI and API, please refer to the Read the Docs.

For getting help on the CLI:

$ pypgx -h

usage: pypgx [-h] [-v] COMMAND ...

positional arguments:
  COMMAND
    call-genotypes      Call genotypes for the target gene.
    call-phenotypes     Call phenotypes for the target gene.
    combine-results     Combine various results for the target gene.
    compare-genotypes   Calculate concordance rate between two genotype results.
    compute-control-statistics
                        Compute summary statistics for the control gene from BAM files.
    compute-copy-number
                        Compute copy number from read depth for the target gene.
    compute-target-depth
                        Compute read depth for the target gene from BAM files.
    create-consolidated-vcf
                        Create a consolidated VCF file.
    create-regions-bed  Create a BED file which contains all regions used by PyPGx.
    estimate-phase-beagle
                        Estimate haplotype phase of observed variants with the Beagle program.
    filter-samples      Filter Archive file for specified samples.
    import-read-depth   Import read depth data for target gene.
    import-variants     Import variant data for the target gene.
    plot-bam-copy-number
                        Plot copy number profile from CovFrame[CopyNumber].
    plot-bam-read-depth
                        Plot read depth profile with BAM data.
    plot-cn-af          Plot both copy number profile and allele fraction profile in one figure.
    plot-vcf-allele-fraction
                        Plot allele fraction profile with VCF data.
    plot-vcf-read-depth
                        Plot read depth profile with VCF data.
    predict-alleles     Predict candidate star alleles based on observed variants.
    predict-cnv         Predict CNV for the target gene based on copy number data.
    prepare-depth-of-coverage
                        Prepare a depth of coverage file for all target genes with SV.
    print-metadata      Print the metadata of specified archive.
    run-chip-pipeline   Run PyPGx's genotyping pipeline for chip data.
    run-ngs-pipeline    Run PyPGx's genotyping pipeline for NGS data.
    test-cnv-caller     Test a CNV caller for the target gene.
    train-cnv-caller    Train a CNV caller for the target gene.

optional arguments:
  -h, --help            Show this help message and exit.
  -v, --version         Show the version number and exit.

For getting help on a specific command (e.g. call-genotypes):

$ pypgx call-genotypes -h

Below is the list of submodules available in the API:

• core : The core submodule is the main suite of tools for PGx research.

• genotype : The genotype submodule is a suite of tools for accurately predicting genotype calls.

• pipeline : The pipeline submodule is used to provide convenient methods that combine multiple PyPGx actions and automatically handle semantic types.

• plot : The plot submodule is used to plot various kinds of profiles such as read depth, copy number, and allele fraction.

• utils : The utils submodule contains main actions of PyPGx.

For getting help on a specific submodule (e.g. utils):

>>> from pypgx.api import utils
>>> help(utils)

CLI examples

We can print the metadata of an archive file:

$ pypgx print-metadata CYP2D6-copy-number.zip

Above will print:

Gene=CYP2D6
Assembly=GRCh37
SemanticType=CovFrame[CopyNumber]
Platform=WGS
Control=VDR
Samples=None

We can run the NGS pipeline for the CYP2D6 gene:

$ pypgx run-ngs-pipeline \
CYP2D6 \
CYP2D6-pipeline \
--variants variants.vcf \
--depth-of-coverage depth-of-coverage.zip \
--control-statistics control-statistics-VDR.zip

Above will create a number of archive files:

Saved VcfFrame[Imported] to: CYP2D6-pipeline/imported-variants.zip
Saved VcfFrame[Phased] to: CYP2D6-pipeline/phased-variants.zip
Saved VcfFrame[Consolidated] to: CYP2D6-pipeline/consolidated-variants.zip
Saved SampleTable[Alleles] to: CYP2D6-pipeline/alleles.zip
Saved CovFrame[ReadDepth] to: CYP2D6-pipeline/read-depth.zip
Saved CovFrame[CopyNumber] to: CYP2D6-pipeline/copy-number.zip
Saved SampleTable[CNVCalls] to: CYP2D6-pipeline/cnv-calls.zip
Saved SampleTable[Genotypes] to: CYP2D6-pipeline/genotypes.zip
Saved SampleTable[Results] to: CYP2D6-pipeline/results.zip

API examples

We can obtain allele function for the CYP2D6 gene:

>>> import pypgx
>>> pypgx.get_function('CYP2D6', '*1')
'Normal Function'
>>> pypgx.get_function('CYP2D6', '*4')
'No Function'
>>> pypgx.get_function('CYP2D6', '*22')
'Uncertain Function'
>>> pypgx.get_function('CYP2D6', '*140')
'Unknown Function'

We can predict phenotype for the CYP2D6 gene based on two haplotype calls:

>>> import pypgx
>>> pypgx.predict_phenotype('CYP2D6', '*4', '*5')   # Both alleles have no function
'Poor Metabolizer'
>>> pypgx.predict_phenotype('CYP2D6', '*5', '*4')   # The order of alleles does not matter
'Poor Metabolizer'
>>> pypgx.predict_phenotype('CYP2D6', '*1', '*22')  # *22 has uncertain function
'Indeterminate'
>>> pypgx.predict_phenotype('CYP2D6', '*1', '*1x2') # Gene duplication
'Ultrarapid Metabolizer'