Fast visualization of genomic data
Project description
Genomeshader is a Rust-backed Python library for rapid visualization of read-level data spanning variants across huge numbers of samples. It is intended for use within Jupyter notebooks.
Documentation for the API can be found on the documentation page.
Prerequisites
Genomeshader is designed to access local files or data in Google Cloud Storage (GCS). Within certain cloud-computing environments (i.e. Terra, All of Us Researcher Workbench), access to GCS is already configured. For accessing files in GCS on your local machine, you will also need to install the Google Cloud CLI. Then, configure your Application Default Credentials (ADC).
Installation
pip is recommended for installation.
pip install genomeshader
Building from source
To build from source (particularly for those interested in contributing to the code), follow the procedure below.
# Clone repository.
git clone https://github.com/broadinstitute/genomeshader.git
cd genomeshader
# Create a Python virtual environment and install Maturin, the tool that
# will compile the Rust and Python code into a complete library.
# For more information on Maturin, visit https://github.com/PyO3/maturin .
python -mvenv venv
. venv/bin/activate
pip install maturin
# Build the library (with release optimizations) and install it in
# the currently active virtual environment.
maturin develop --release
Supported platforms
Genomeshader is compiled for Linux and MacOSX. Windows is not currently supported.
Getting help
If you encounter bugs or have questions/comments/concerns, please file an issue on our Github page.
Developers’ guide
For information on contributing to Genomeshader development, visit our developer documentation.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for genomeshader-0.1.52-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 817b7fc3a826f68c374f885398bb608045cd3aef9b9e3ff77e25063d2bcc6b31 |
|
MD5 | 91b4358a4803a5d3c3053fdcf1a1bc88 |
|
BLAKE2b-256 | 4f010e41d8ce8680ce703ead9bf2fe4fed19d5d883fb077cd8fb8bcf335b2b6e |
Hashes for genomeshader-0.1.52-cp37-abi3-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bba834ea56f127dc6e937d9f2c1e1f935b369de095f420a306e6938d4803d428 |
|
MD5 | c8f36741b5fa6ac303989c542ad52227 |
|
BLAKE2b-256 | 28c1db1d527a5b4df361bb9b4e01aa938e834075a69b7d52442a27ff97ea6dbc |
Hashes for genomeshader-0.1.52-cp37-abi3-macosx_10_12_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3720deaa0927cef2a6c1bcaeff6fca89e67a1a5db673df318075317412855efe |
|
MD5 | efeee55e54d8cf60b4befa63c5b8f0f1 |
|
BLAKE2b-256 | 0349f081e17450e010ee998cd6841d1b9e17641e97a0ecc2463f1d82bd029bd2 |