gwasstudio 2.14.0

No project description provided

GWASStudio: A Tool for Genomic Data Management

Overview

GWASStudio is a powerful CLI tool designed for efficient storage, retrieval, and querying of genomic summary statistics. It offers a high-performance infrastructure for handling and analyzing large-scale GWAS and QTL datasets, enabling seamless cross-dataset exploration.

Core Purpose

GWASStudio provides a unified interface across the CDH infrastructure, handling the ingestion, storage, querying and export of genomic data using high-performance technologies.

Key Functionalities

GWASStudio consists of several key functionalities:

1. Data Ingestion

• Data Ingestion: Imports summary statistics data and its metadata associated.
• Support for Multiple Storage Options: Works with both local filesystems and cloud storage (S3).

2. Data Querying

• Flexible Search: Enables searching metadata using template files.

3. Data Export

• Selective Export: Extracts subsets of data and its metadata associated based on genomic regions, SNPs, or the entire set of data.

Technical Architecture

GWASStudio leverages several advanced technologies:

1. TileDB Embedded: A high-performance array storage engine that enables efficient storage and retrieval of genomic data.
2. MongoDB: A flexible, scalable NoSQL database used for storing and querying metadata associated with genomic datasets.
3. Dask: Provides distributed computing capabilities for processing large datasets.
4. Python Ecosystem: Built on Python with libraries like Click/Cloup for CLI interfaces, Pandas for data manipulation, and various genomics-specific tools.

Installation

For detailed installation instructions, please refer to the documentation at https://ht-diva.github.io/gwasstudio/

Usage

For detailed instructions on how to use this tool, please refer to the documentation and check the cli_test script for a practical guide by examples.

Citation

Example files are derived from:

The variant call format provides efficient and robust storage of GWAS summary statistics. Matthew Lyon, Shea J Andrews, Ben Elsworth, Tom R Gaunt, Gibran Hemani, Edoardo Marcora. bioRxiv 2020.05.29.115824; doi: https://doi.org/10.1101/2020.05.29.115824