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164 changes: 164 additions & 0 deletions .gitignore
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# Project-specific data directories
inputs/
output/

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml

# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/

# pytype static type analyzer
.pytype/

# Cython debug symbols
cython_debug/

# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
55 changes: 54 additions & 1 deletion README.md
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# ReducingVariantClassificationInequities
# Analysis Notebooks for Reducing Variant Classification Inequalities

This repository contains the [Jupyter notebook](https://jupyter.org/) used to generate figures and key results for
"Defining and Reducing Variant Classification Inequities in Genomic Medicine" by Dawood ... Gallego Romero
(final citation TBA).

## How to use this notebook

### Getting set up

To begin,
[clone the repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository)
so that you have a local copy of the notebook and the required directory structure.

Create a [new virtual Python environment](https://docs.python.org/3/library/venv.html) for running the notebooks.
The notebooks have been tested using Python 3.7 and Python 3.10, but other versions are likely to work as well.
Activate the environment and install the required packages defined in `requirements.txt`.

The following commands will set up and launch the notebook on a Unix-like system:

```
git clone git@github.com:MoezDawood/ReducingVariantClassificationInequities.git
cd ReducingVariantClassificationInequities
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
jupyter notebook ReducingVariantClassificationInequitiesCode_20240311.ipynb
```

### Downloading required data files

Running the notebook requires several large collections of variants from [gnomAD](https://gnomad.broadinstitute.org/)
and other sources.
Pre-formatted files can be downloaded from
[this link](https://drive.google.com/drive/folders/1cdXoB0AMPCO0dWH_6Eff1smtCd5i5YPB?usp=drive_link)
and extracted into the `inputs/` folder in the same directory as the notebook file.

### Reproducing the results

It will take around 5-6 hours to produce all figures and tables found in the paper
(including main text figures and supplement and a few additional figures) for gnomAD v2.1.1 and gnomAD v3.1.2 (non v2).
The figures and tables will be found in the output folder.

The notebook is designed to be run from start to finish,
but there is a table of contents and users should be able to reproduce individual figures once the setup is completed.

Note that due to the large size of the data frames containing all the required variant information,
running this notebook requires a computer with a large amount of memory and we recommend using a shared server or cluster node for this purpose.

### Data and code availability for All of Us

Per the policy of the All of Us program, the code, inputs, and outputs for the All of Us analyses is in a declared workspace in the All of Us workbench.
The code in the All of Us workspace is the same as below with modifications made for the specific data requirements of the All of Us platform.
The workspace will be gladly and immediately shared with approved users upon request.
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