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otargen: GraphQL-based R package for tidy data accessing and processing from Open Targets Genetics.

Amir Feizi¹, Kamalika Ray¹

¹Bioinformatics Department, OMass Therapeutics, Oxford Business Park, ARC, Oxford OX4 2GX, United Kingdom.

Bioinformatics (Oxford, England)

|July 19, 2023

Summary

The otargen R package simplifies accessing Open Target Genetics data, enabling easier variant prioritization and drug target identification. This tool streamlines genetic data retrieval for researchers, accelerating target discovery pipelines.

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Area of Science:

Genetics
Bioinformatics
Pharmacogenomics

Background:

Open Target Genetics provides variant-centric statistical evidence for prioritizing causal variants and identifying drug targets.
GraphQL technology facilitates efficient data querying but presents challenges in data retrieval and processing for R and Python users.
Complex GraphQL queries and nested JSON outputs require significant effort to integrate genetic data into target discovery workflows.

Purpose of the Study:

To develop an open-source R package that simplifies data retrieval and analysis from the Open Target Genetics portal.
To provide R users with a streamlined approach to access genetic information, avoiding complex GraphQL scripting.
To facilitate the integration of genetic data into data-driven drug target discovery pipelines.

Main Methods:

Development of the open-source R package 'otargen'.
Implementation of functions to cover all query types available on the Open Target Genetics portal.
Design of functions to return data in a tidy table format for easy analysis.
Inclusion of plotting functions for data visualization.

Main Results:

The 'otargen' R package offers a simplified, single-line code solution for accessing Open Target Genetics data.
Users can retrieve genetic data in a tidy format, bypassing complex GraphQL queries and JSON processing.
The package includes convenient plotting functions for visualizing complex genetic datasets.
Streamlined data access accelerates the process of identifying potential drug targets.

Conclusions:

'otargen' significantly reduces the complexity and time required for R users to leverage Open Target Genetics data.
The package enhances the integration of genetic evidence into drug discovery pipelines.
This open-source tool democratizes access to valuable genetic insights for a wider research community.