Metadata-Version: 2.4
Name: snakemake-storage-plugin-orcestra
Version: 0.2.0
Project-URL: repository, https://github.com/bhklab/snakemake-storage-plugin-orcestra
Project-URL: documentation, https://snakemake.github.io/snakemake-plugin-catalog/plugins/storage/orcestra.html
Author-email: Jermiah Joseph <jermiahjoseph98@gmail.com>
License-Expression: MIT
Keywords: google cloud storage,plugin,snakemake,storage
Requires-Python: >=3.11
Requires-Dist: orcestra-downloader
Requires-Dist: snakemake-interface-common<2.0.0,>=1.17.4
Requires-Dist: snakemake-interface-storage-plugins<4.0.0,>=3.3.0
Description-Content-Type: text/markdown

# Snakemake Storage Plugin for Orcestra

## Introduction

This plugin enables Snakemake workflows to seamlessly access and utilize
datasets from the Orcestra.ca platform, enhancing research reproducibility
 and data accessibility.

## About Orcestra.ca

Orcestra.ca serves as a comprehensive repository of scientific datasets,
 designed to accelerate research across multiple domains. The platform
  centralizes access to high-quality, curated datasets that are essential
   for cutting-edge research in biomedicine and computational biology.

## Available Dataset Types

Orcestra.ca provides researchers with access to diverse dataset categories:

* **Pharmacogenomics**: Data connecting genetic variations to drug responses,
 supporting personalized medicine research
* **Clinical Genomics**: Integrated multimodal datasets combining clinical
 information with genomic data
* **Immuno-oncology (ICB)**: Specialized collections focused on immune
 checkpoint blockade therapies and cancer treatment
* **Radiomics**: Datasets linking quantitative imaging features with clinical
 outcomes and genomic data
* **Toxicogenomics**: Resources for studying genetic responses to environmental
 toxins and their health implications

## Benefits

By integrating Orcestra.ca with Snakemake workflows, researchers can conduct
 more robust, reproducible, and comprehensive studies. This plugin bridges
  the gap between powerful computational pipelines and rich biological datasets,
   streamlining scientific discovery and data-driven research.
