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https://github.com/sbslee/pypgx

A Python package for pharmacogenomics (PGx) research
https://github.com/sbslee/pypgx

api cli cyp2d6 genotype pgx pharmacogenetics pharmacogenomics phenotype star-alleles structural-variation

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A Python package for pharmacogenomics (PGx) research

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README 

        
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README

******



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Introduction

============



The main purpose of the PyPGx package is to provide a unified platform for

pharmacogenomics (PGx) research. PyPGx is and always will be completely free

and open source.



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 `_.



Quick links:



- `README `__

- `Genes `__

- `Glossary `__

- `Tutorials `__

- `CLI `__

- `API `__

- `SDK `__

- `FAQ `__

- `Changelog `__



PyPGx can predict PGx genotypes (e.g. ``*4/*5``) and phenotypes (e.g.

``Poor Metabolizer``) using various genomic data, including data from

next-generation sequencing (NGS), single nucleotide polymorphism (SNP) array,

and long-read sequencing. Importantly, for NGS data the package can detect

`structural variation (SV) `__ using a machine learning-based

approach. Finally, note that PyPGx is compatible with both of the Genome

Reference Consortium Human (GRCh) builds, GRCh37 (hg19) and GRCh38 (hg38).



There are currently 87 pharmacogenes in PyPGx:



.. list-table::



   * - ABCB1

     - ABCG2

     - ACYP2

     - ADRA2A

     - ADRB2

   * - ANKK1

     - APOE

     - ATM

     - BCHE

     - BDNF

   * - CACNA1S

     - CFTR

     - COMT

     - CYP1A1

     - CYP1A2

   * - CYP1B1

     - CYP2A6/CYP2A7

     - CYP2A13

     - CYP2B6/CYP2B7

     - CYP2C8

   * - CYP2C9

     - CYP2C19

     - CYP2D6/CYP2D7

     - CYP2E1

     - CYP2F1

   * - CYP2J2

     - CYP2R1

     - CYP2S1

     - CYP2W1

     - CYP3A4

   * - CYP3A5

     - CYP3A7

     - CYP3A43

     - CYP4A11

     - CYP4A22

   * - CYP4B1

     - CYP4F2

     - CYP17A1

     - CYP19A1

     - CYP26A1

   * - DBH

     - DPYD

     - DRD2

     - F2

     - F5

   * - G6PD

     - GRIK1

     - GRIK4

     - GRIN2B

     - GSTM1

   * - GSTP1

     - GSTT1

     - HTR1A

     - HTR2A

     - IFNL3

   * - IFNL3

     - ITGB3

     - ITPA

     - MTHFR

     - NAT1

   * - NAT2

     - NUDT15

     - OPRK1

     - OPRM1

     - POR

   * - PTGIS

     - RARG

     - RYR1

     - SLC6A4

     - SLC15A2

   * - SLC22A2

     - SLC28A3

     - SLC47A2

     - SLCO1B1

     - SLCO1B3

   * - SLCO2B1

     - SULT1A1

     - TBXAS1

     - TPMT

     - UGT1A1

   * - UGT1A4

     - UGT1A6

     - UGT2B7

     - UGT2B15

     - UGT2B17

   * - VKORC1

     - XPC

     -

     -

     -



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



| Author: Seung-been "Steven" Lee

| Email: [email protected]

| License: MIT License



Citation

========



If you use PyPGx in a published analysis, please report the program version

and cite the following article:



- Lee et al., 2022. `ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation `__. PLOS ONE.



In this article, PyPGx was used to call star alleles for genomic DNA

reference materials from the Centers for Disease Control and Prevention–based

`Genetic Testing Reference Materials Coordination Program (GeT-RM)

`__, where it

showed almost 100% concordance with genotype results from previous works.



The development of PyPGx was heavily inspired by `Stargazer `__, another star-allele calling

tool developed by Steven when he was in his PhD program at the University of

Washington. Therefore, please also cite the following articles:



- Lee et al., 2019. `Calling star alleles with Stargazer in 28 pharmacogenes with whole genome sequences `__. Clinical Pharmacology & Therapeutics.

- Lee et al., 2018. `Stargazer: a software tool for calling star alleles from next-generation sequencing data using CYP2D6 as a model `__. Genetics in Medicine.



Below is an incomplete list of publications which have used PyPGx:



- Wroblewski et al., 2022. `Pharmacogenetic variation in Neanderthals and Denisovans and implications for human health and response to medications `__. bioRxiv.

- Botton et al., 2020. `Phased Haplotype Resolution of the SLC6A4 Promoter Using Long-Read Single Molecule Real-Time (SMRT) Sequencing `__. Genes.



Support PyPGx

=============



If you find my work useful, please consider becoming a `sponsor `__.



Installation

============



Following packages are required to run PyPGx:



.. list-table::

   :header-rows: 1



   * - Package

     - Anaconda

     - PyPI

   * - ``fuc``

     - ✅

     - ✅

   * - ``scikit-learn``

     - ✅

     - ✅

   * - ``openjdk``

     - ✅

     - ❌



There are various ways you can install PyPGx. The recommended way is via

conda (`Anaconda `__):



.. code-block:: text



   $ 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):



.. code-block:: text



   $ pip install pypgx



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



.. code-block:: text



   $ 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.



.. note::

    `Beagle `__

    is one of the default software tools used by PyPGx for haplotype phasing

    SNVs and indels. The program is freely available and published under the

    `GNU General Public License `__. Users do not need to download Beagle separately

    because a copy of the software (``beagle.22Jul22.46e.jar``) is already

    included in PyPGx.



.. warning::

    You're not done yet! Keep scrolling down to obtain the resource bundle

    for PyPGx, which is essential for running the package.



Resource bundle

===============



Starting with the 0.12.0 version, reference haplotype panel files and

structural variant classifier files in PyPGx are moved to the

``pypgx-bundle`` `repository `__

(only those files are moved; other files such as ``allele-table.csv`` and

``variant-table.csv`` are intact). Therefore, the user must clone the

``pypgx-bundle`` repository with matching PyPGx version to their home

directory in order for PyPGx to correctly access the moved files (i.e. replace 

``x.x.x`` with the version number of PyPGx you're using, such as ``0.18.0``):



.. code-block:: text



   $ cd ~

   $ git clone --branch x.x.x --depth 1 https://github.com/sbslee/pypgx-bundle



This is undoubtedly annoying, but absolutely necessary for portability

reasons because PyPGx has been growing exponentially in file size due to the

increasing number of genes supported and their variation complexity, to the

point where it now exceeds upload size limit for PyPI (100 Mb). After removal

of those files, the size of PyPGx has reduced from >100 Mb to <1 Mb.



Starting with version 0.22.0, you can now specify a custom location for the 

``pypgx-bundle`` directory instead of using the home directory. This can be 

achieved by setting the bundle location using the ``PYPGX_BUNDLE`` environment 

variable:



.. code-block:: text



   $ export PYPGX_BUNDLE=/path/to/pypgx-bundle



Structural variation detection

==============================



Many pharmacogenes are known to have `structural variation (SV)

`__ such as gene deletions, duplications, and hybrids. You can visit the

`Genes `__ page to see the

list of genes with SV.



Some of the SV events can be quite challenging to detect accurately with NGS

data due to misalignment of sequence reads caused by sequence homology with

other gene family members (e.g. CYP2D6 and CYP2D7). PyPGx attempts to address

this issue by training a `support vector machine (SVM) `__-based multiclass

classifier using the `one-vs-rest strategy `__ for each

gene for each GRCh build. Each classifier is trained using copy number

profiles of real NGS samples as well as simulated ones, including those from

`1KGP `__ and `GeT-RM `__.



You can plot copy number profile and allele fraction profile with PyPGx to

visually inspect SV calls. Below are CYP2D6 examples:



.. list-table::

   :header-rows: 1

   :widths: 10 30 60



   * - SV Name

     - Gene Model

     - Profile

   * - Normal

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/gene-model-CYP2D6-1.png

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/GRCh37-CYP2D6-8.png

   * - WholeDel1

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/gene-model-CYP2D6-2.png

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/GRCh37-CYP2D6-1.png

   * - WholeDel1Hom

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/gene-model-CYP2D6-3.png

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/GRCh37-CYP2D6-6.png

   * - WholeDup1

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/gene-model-CYP2D6-4.png

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/GRCh37-CYP2D6-2.png

   * - Tandem3

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/gene-model-CYP2D6-11.png

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/GRCh37-CYP2D6-9.png

   * - Tandem2C

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/gene-model-CYP2D6-10.png

     - .. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/dpsv/GRCh37-CYP2D6-7.png



PyPGx was recently applied to the entire high-coverage WGS dataset from 1KGP

(N=2,504). Click `here `__ to see individual SV calls, and corresponding copy number

profiles and allele fraction profiles.



GRCh37 vs. GRCh38

=================



When working with PGx data, it's not uncommon to encounter a situation

where you are handling GRCh37 data in one project but GRCh38 in another. You

may be tempted to use tools like ``LiftOver`` to convert GRCh37 to GRCh38, or

vice versa, but deep down you know it's going to be a mess (and please don't

do this). The good news is, PyPGx supports both of the builds!



In many PyPGx actions, you can simply indicate which genome build to use. For

example, for GRCh38 data you can use ``--assembly GRCh38`` in CLI and

``assembly='GRCh38'`` in API. **Note that GRCh37 will always be the

default.** Below is an example of using the API:



.. code:: python3



    >>> import pypgx

    >>> pypgx.list_variants('CYP2D6', alleles=['*4'], assembly='GRCh37')

    ['22-42524947-C-T']

    >>> pypgx.list_variants('CYP2D6', alleles=['*4'], assembly='GRCh38')

    ['22-42128945-C-T']



However, there is one important caveat to consider if your sequencing data is

GRCh38. That is, sequence reads must be aligned only to the main contigs

(i.e. ``chr1``, ``chr2``, ..., ``chrX``, ``chrY``), and not to the

alternative (ALT) contigs such as ``chr1_KI270762v1_alt``. This is because

the presence of ALT contigs reduces the sensitivity of variant calling

and many other analyses including SV detection. Therefore, if you have

sequencing data in GRCh38, make sure it's aligned to the main contigs only.



The only exception to above rule is the GSTT1 gene, which is located on

``chr22`` for GRCh37 but on ``chr22_KI270879v1_alt`` for GRCh38. This gene is

known to have an extremely high rate of gene deletion polymorphism in the

population and thus requires SV analysis. Therefore, if you are interested in

genotyping this gene with GRCh38 data, then you must include that contig

when performing read alignment. To this end, you can easily filter your

reference FASTA file before read alignment so that it only contains the main

contigs plus the ALT contig. If you don't know how to do this, here's one way

using the ``fuc`` program (which should have already been installed along

with PyPGx):



.. code-block:: text



    $ cat contigs.list

    chr1

    chr2

    ...

    chrX

    chrY

    chr22_KI270879v1_alt

    $ fuc fa-filter in.fa --contigs contigs.list > out.fa



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``):



.. list-table::

    :widths: 20 40 40

    :header-rows: 1



    * - 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``, ``LongRead``

    * - ``Program``

      - Name of the phasing program.

      - ``Beagle``, ``SHAPEIT``

    * - ``Samples``

      - Samples used for inter-sample normalization.

      - ``NA07000,NA10854,NA11993``

    * - ``SemanticType``

      - Semantic type of the archive.

      - ``CovFrame[CopyNumber]``, ``Model[CNV]``



Semantic types

--------------



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[Statistics]``

    * 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``.



Working with archive files

--------------------------



To demonstrate how easy it is to work with PyPGx archive files, below we will

show some examples. First, download an archive to play with, which has

``SampleTable[Results]`` as semantic type:



.. code-block:: text



    $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch37-CYP2D6-results.zip



Let's print its metadata:



.. code-block:: text



    $ pypgx print-metadata grch37-CYP2D6-results.zip

    Gene=CYP2D6

    Assembly=GRCh37

    SemanticType=SampleTable[Results]



Now print its main data (but display first sample only):



.. code-block:: text



    $ pypgx print-data grch37-CYP2D6-results.zip | head -n 2

    	Genotype	Phenotype	Haplotype1	Haplotype2	AlternativePhase	VariantData	CNV

    HG00276_PyPGx	*4/*5	Poor Metabolizer	*4;*10;*74;*2;	*10;*74;*2;	;	*4:22-42524947-C-T:0.913;*10:22-42526694-G-A,22-42523943-A-G:1.0,1.0;*74:22-42525821-G-T:1.0;*2:default;	DeletionHet



We can unzip it to extract files inside (note that ``tmpcty4c_cr`` is the

original folder name):



.. code-block:: text



    $ unzip grch37-CYP2D6-results.zip

    Archive:  grch37-CYP2D6-results.zip

      inflating: tmpcty4c_cr/metadata.txt

      inflating: tmpcty4c_cr/data.tsv



We can now directly interact with the files:



.. code-block:: text



    $ cat tmpcty4c_cr/metadata.txt

    Gene=CYP2D6

    Assembly=GRCh37

    SemanticType=SampleTable[Results]

    $ head -n 2 tmpcty4c_cr/data.tsv

    	Genotype	Phenotype	Haplotype1	Haplotype2	AlternativePhase	VariantData	CNV

    HG00276_PyPGx	*4/*5	Poor Metabolizer	*4;*10;*74;*2;	*10;*74;*2;	;	*4:22-42524947-C-T:0.913;*10:22-42526694-G-A,22-42523943-A-G:1.0,1.0;*74:22-42525821-G-T:1.0;*2:default;	DeletionHet



We can easily create a new archive:



.. code-block:: text



    $ zip -r grch37-CYP2D6-results-new.zip tmpcty4c_cr

      adding: tmpcty4c_cr/ (stored 0%)

      adding: tmpcty4c_cr/metadata.txt (stored 0%)

      adding: tmpcty4c_cr/data.tsv (deflated 84%)

    $ pypgx print-metadata grch37-CYP2D6-results-new.zip

    Gene=CYP2D6

    Assembly=GRCh37

    SemanticType=SampleTable[Results]



Phenotype prediction

====================



Many genes in PyPGx have a genotype-phenotype table available from the

Clinical Pharmacogenetics Implementation Consortium (CPIC) or

the Pharmacogenomics Knowledge Base (PharmGKB). PyPGx uses these tables to

perform phenotype prediction with one of the two methods:



- Method 1. Simple diplotype-phenotype mapping: This method directly uses the

  diplotype-phenotype mapping as defined by CPIC or PharmGKB. Using the

  CYP2B6 gene as an example, the diplotypes \*6/\*6, \*1/\*29, \*1/\*2,

  \*1/\*4, and \*4/\*4 correspond to Poor Metabolizer, Intermediate

  Metabolizer, Normal Metabolizer, Rapid Metabolizer, and Ultrarapid

  Metabolizer.

- Method 2. Summation of haplotype activity scores: This method uses a

  standard unit of enzyme activity known as an activity score. Using the

  CYP2D6 gene as an example, the fully functional reference \*1 allele is

  assigned a value of 1, decreased-function alleles such as \*9 and \*17

  receive a value of 0.5, and nonfunctional alleles including \*4 and \*5

  have a value of 0. The sum of values assigned to both alleles constitutes

  the activity score of a diplotype. Consequently, subjects with \*1/\*1,

  \*1/\*4, and \*4/\*5 diplotypes have an activity score of 2 (Normal

  Metabolizer), 1 (Intermediate Metabolizer), and 0 (Poor Metabolizer),

  respectively.



Please visit the `Genes `__ page to see the list of genes with a genotype-phenotype

table and each of their prediction method.



To perform phenotype prediction with the API, you can use the

``pypgx.predict_phenotype`` method:



.. code:: python3



    >>> 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'



To perform phenotype prediction with the CLI, you can use the

``call-phenotypes`` command. It takes a ``SampleTable[Genotypes]`` file as

input and outputs a ``SampleTable[Phenotypes]`` file:



.. code-block:: text



   $ pypgx call-phenotypes genotypes.zip phenotypes.zip



Pipelines

=========



PyPGx currently provides three pipelines for performing PGx genotype analysis

of single gene for one or multiple samples: NGS pipeline, chip pipeline, and

long-read pipeline. In additional to genotyping, each pipeline will perform

phenotype prediction based on genotype results. All pipelines are compatible

with both GRCh37 and GRCh38 (e.g. for GRCh38 use ``--assembly GRCh38`` in CLI

and ``assembly='GRCh38'`` in API).



NGS pipeline

------------



.. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/flowchart-ngs-pipeline.png



Implemented as ``pypgx run-ngs-pipeline`` in CLI and

``pypgx.pipeline.run_ngs_pipeline`` in API, this pipeline is designed for

processing short-read data (e.g. Illumina). Users must specify whether the

input data is from whole genome sequencing (WGS) or targeted sequencing

(custom targeted panel sequencing or whole exome sequencing).



This pipeline supports SV detection based on copy number analysis for genes

that are known to have SV. Therefore, if the target gene is associated with

SV (e.g. CYP2D6) it's strongly recommended to provide a

``CovFrame[DepthOfCoverage]`` file and a ``SampleTable[Statistics]`` file in

addtion to a VCF file containing SNVs/indels. If the target gene is not

associated with SV (e.g. CYP3A5) providing a VCF file alone is enough. You can

visit the `Genes `__ page

to see the full list of genes with SV. For details on SV detection algorithm,

please see the `Structural variation detection `__ section.



When creating a VCF file (containing SNVs/indels) from BAM files, users have

a choice to either use the ``pypgx create-input-vcf`` command (strongly

recommended) or a variant caller of their choice (e.g. GATK4

HaplotypeCaller). See the `Variant caller choice `__ section for detailed

discussion on when to use either option.



Check out the `GeT-RM WGS tutorial `__ to see this pipeline in action.



Chip pipeline

-------------



.. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/flowchart-chip-pipeline.png



Implemented as ``pypgx run-chip-pipeline`` in CLI and

``pypgx.pipeline.run_chip_pipeline`` in API, this pipeline is designed for

DNA chip data (e.g. Global Screening Array from Illumina). It's recommended

to perform variant imputation on the input VCF prior to feeding it to the

pipeline using a large reference haplotype panel (e.g. `TOPMed Imputation

Server `__).

Alternatively, it's possible to perform variant imputation with the 1000

Genomes Project (1KGP) data as reference within PyPGx using ``--impute`` in

CLI and ``impute=True`` in API.



The pipeline currently does not support SV detection. Please post a GitHub

issue if you want to contribute your development skills and/or data for

devising an SV detection algorithm.



Check out the `Coriell Affy tutorial `__ to see this pipeline in action.



Long-read pipeline

------------------



.. image:: https://raw.githubusercontent.com/sbslee/pypgx-data/main/flowchart-long-read-pipeline.png



Implemented as ``pypgx run-long-read-pipeline`` in CLI and

``pypgx.pipeline.run_long_read_pipeline`` in API, this pipeline is designed

for long-read data (e.g. Pacific Biosciences and Oxford Nanopore

Technologies). The input VCF must be phased using a read-backed haplotype

phasing tool such as `WhatsHap `__.



The pipeline currently does not support SV detection. Please post a GitHub

issue if you want to contribute your development skills and/or data for

devising an SV detection algorithm.



Results interpretation

======================



PyPGx outputs per-sample genotype results in a table, which is stored in an

archive file with the semantic type ``SampleTable[Results]``. Below, we will

use the CYP2D6 gene with GRCh37 as an example to illustrate how to interpret

genotype results from PyPGx.



.. list-table::

   :header-rows: 1



   * -

     - Genotype

     - Phenotype

     - Haplotype1

     - Haplotype2

     - AlternativePhase

     - VariantData

     - CNV

   * - NA11839

     - \*1/\*2

     - Normal Metabolizer

     - \*1;

     - \*2;

     - ;

     - \*1:22-42522613-G-C,22-42523943-A-G:0.5,0.488;\*2:default

     - Normal

   * - NA12006

     - \*4/\*41

     - Intermediate Metabolizer

     - \*41;\*2;

     - \*4;\*10;\*2;

     - \*69;

     - \*69:22-42526694-G-A,22-42523805-C-T:0.5,0.551;\*4:22-42524947-C-T:0.444;\*10:22-42523943-A-G,22-42526694-G-A:0.55,0.5;\*41:22-42523805-C-T:0.551;\*2:default;

     - Normal

   * - HG00276

     - \*4/\*5

     - Poor Metabolizer

     - \*4;\*10;\*74;\*2;

     - \*10;\*74;\*2;

     - ;

     - \*4:22-42524947-C-T:0.913;\*10:22-42523943-A-G,22-42526694-G-A:1.0,1.0;\*74:22-42525821-G-T:1.0;\*2:default;

     - WholeDel1

   * - NA19207

     - \*2x2/\*10

     - Normal Metabolizer

     - \*10;\*2;

     - \*2;

     - ;

     - \*10:22-42523943-A-G,22-42526694-G-A:0.361,0.25;\*2:default;

     - WholeDup1



This list explains each of the columns in the example results.



- **Genotype**: Diplotype call. When there is no SV this simply combines the two top-ranked star alleles from **Haplotype1** and **Haplotype2** with the delimiter '/'. In the presence of SV the final diplotype is determined using one of the genotypers in the ``pypgx.api.genotype`` module (e.g. `CYP2D6Genotyper `__).

- **Phenotype**: Phenotype call.

- **Haplotype1**, **Haplotype2**: List of candidate star alleles for each haplotype. For example, if a given haplotype contains three variants ``22-42523943-A-G``, ``22-42524947-C-T``, and ``22-42526694-G-A``, then it will get assigned ``*4;*10;`` because the haplotype pattern can fit both \*4 (``22-42524947-C-T``) and \*10 (``22-42523943-A-G`` and ``22-42526694-G-A``). Note that \*4 comes first before \*10 because it has higher priority for reporting purposes (see the ``pypgx.sort_alleles`` `method `__ for detailed implementation).

- **AlternativePhase**: List of star alleles that could be missed due to potentially incorrect statistical phasing. For example, let's assume that statistical phasing has put ``22-42526694-G-A`` for **Haplotype1** and ``22-42523805-C-T`` for **Haplotype2**. Even though the two variants are in trans orientation, PyPGx will also consider alternative phase in case the two variants are actually in cis orientation, resulting in ``*69;`` as **AlternativePhase** because \*69 is defined by ``22-42526694-G-A`` and ``22-42523805-C-T``.

- **VariantData**: Information for SNVs/indels used to define observed star alleles, including allele fraction which is important for allelic decomposition after identifying CNV (e.g. the sample NA19207). In some situations, there will not be any variants for a given star allele because the allele itself is "default" allele for the selected reference assembly (e.g. GRCh37 has \*2 as default while GRCh38 has \*1).

- **CNV**: Structural variation call. See the `Structural variation detection `__ section for more details.



Getting help

============



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

`Read the Docs `_.



For getting help on the CLI:



.. code-block:: text



   $ pypgx -h



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

   

   positional arguments:

     COMMAND

       call-genotypes      Call genotypes for target gene.

       call-phenotypes     Call phenotypes for target gene.

       combine-results     Combine various results for target gene.

       compare-genotypes   Calculate concordance between two genotype results.

       compute-control-statistics

                           Compute summary statistics for control gene from BAM

                           files.

       compute-copy-number

                           Compute copy number from read depth for target gene.

       compute-target-depth

                           Compute read depth for target gene from BAM files.

       create-consolidated-vcf

                           Create a consolidated VCF file.

       create-input-vcf    Call SNVs/indels from BAM files for all target genes.

       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 SNV/indel data for 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 from copy number data for target gene.

       prepare-depth-of-coverage

                           Prepare a depth of coverage file for all target

                           genes with SV from BAM files.

       print-data          Print the main data of specified archive.

       print-metadata      Print the metadata of specified archive.

       run-chip-pipeline   Run genotyping pipeline for chip data.

       run-long-read-pipeline

                           Run genotyping pipeline for long-read sequencing data.

       run-ngs-pipeline    Run genotyping pipeline for NGS data.

       slice-bam           Slice BAM file for all genes used by PyPGx.

       test-cnv-caller     Test CNV caller for target gene.

       train-cnv-caller    Train CNV caller for target gene.

   

   options:

     -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):



.. code-block:: text



   $ 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 primarily used to make final diplotype calls by interpreting candidate star alleles and/or detected structural variants.

- **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``):



.. code:: python3



   >>> from pypgx.api import utils

   >>> help(utils)



For getting help on a specific method (e.g. ``pypgx.predict_phenotype``):



.. code:: python3



   >>> import pypgx

   >>> help(pypgx.predict_phenotype)



In Jupyter Notebook and Lab, you can see the documentation for a python

function by hitting ``SHIFT + TAB``. Hit it twice to expand the view.



CLI examples

============



We can print the metadata of an archive file:



.. code-block:: text



    $ pypgx print-metadata grch37-depth-of-coverage.zip



Above will print:



.. code-block:: text



    Assembly=GRCh37

    SemanticType=CovFrame[DepthOfCoverage]

    Platform=WGS



We can run the NGS pipeline for the CYP2D6 gene:



.. code-block:: text



    $ pypgx run-ngs-pipeline \

    CYP2D6 \

    grch37-CYP2D6-pipeline \

    --variants grch37-variants.vcf.gz \

    --depth-of-coverage grch37-depth-of-coverage.zip \

    --control-statistics grch37-control-statistics-VDR.zip



Above will create a number of archive files:



.. code-block:: text



    Saved VcfFrame[Imported] to: grch37-CYP2D6-pipeline/imported-variants.zip

    Saved VcfFrame[Phased] to: grch37-CYP2D6-pipeline/phased-variants.zip

    Saved VcfFrame[Consolidated] to: grch37-CYP2D6-pipeline/consolidated-variants.zip

    Saved SampleTable[Alleles] to: grch37-CYP2D6-pipeline/alleles.zip

    Saved CovFrame[ReadDepth] to: grch37-CYP2D6-pipeline/read-depth.zip

    Saved CovFrame[CopyNumber] to: grch37-CYP2D6-pipeline/copy-number.zip

    Saved SampleTable[CNVCalls] to: grch37-CYP2D6-pipeline/cnv-calls.zip

    Saved SampleTable[Genotypes] to: grch37-CYP2D6-pipeline/genotypes.zip

    Saved SampleTable[Phenotypes] to: grch37-CYP2D6-pipeline/phenotypes.zip

    Saved SampleTable[Results] to: grch37-CYP2D6-pipeline/results.zip



API examples

============



We can obtain allele function for the CYP2D6 gene:



.. code:: python3



    >>> 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 CYP2D6 based on two haplotype calls:



.. code:: python3



    >>> 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'



We can also obtain recommendation (e.g. CPIC) for certain drug-phenotype combination:



.. code:: python3



    >>> import pypgx

    >>> # Codeine, an opiate and prodrug of morphine, is metabolized by CYP2D6

    >>> pypgx.get_recommendation('codeine', 'CYP2D6', 'Normal Metabolizer')

    'Use codeine label recommended age- or weight-specific dosing.'

    >>> pypgx.get_recommendation('codeine', 'CYP2D6', 'Ultrarapid Metabolizer')

    'Avoid codeine use because of potential for serious toxicity. If opioid use is warranted, consider a non-tramadol opioid.'

    >>> pypgx.get_recommendation('codeine', 'CYP2D6', 'Poor Metabolizer')

    'Avoid codeine use because of possibility of diminished analgesia. If opioid use is warranted, consider a non-tramadol opioid.'

    >>> pypgx.get_recommendation('codeine', 'CYP2D6', 'Indeterminate')

    'None'