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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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COSGAP: COntainerized Statistical Genetics Analysis Pipelines.

Bayram Cevdet Akdeniz^1,2, Oleksandr Frei^1,2, Espen Hagen²

¹Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo 0373, Norway.

Bioinformatics Advances

|May 29, 2024

Summary

This summary is machine-generated.

COSGAP simplifies statistical genetics by containerizing analysis pipelines, overcoming common software and data challenges for researchers. This enables easier genome-wide association studies and polygenic scoring globally.

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Area of Science:

Statistical Genetics
Bioinformatics
Computational Biology

Background:

Large-scale genetic data analysis faces challenges due to data sensitivity and non-shareable nature.
Software installation, dependencies, and data wrangling hinder federated analysis across diverse operating systems and HPC facilities.

Purpose of the Study:

To develop a standardized, automated solution for statistical genetic data analysis.
To streamline complex analyses such as genome-wide association studies (GWAS) and polygenic scoring.

Main Methods:

Developed COSGAP (COntainerized Statistical Genetics Analysis Pipelines) using Singularity containers.
Integrated established statistical genetics software tools, code, and instructions.
Created Python helper scripts for auto-generating analysis scripts for HPC or personal computers.

Main Results:

COSGAP provides a unified environment for various statistical genetic analyses.
Users can perform analyses without extensive software installation or data format conversion.
The pipeline is actively used internationally, demonstrating its effectiveness and ease of use.

Conclusions:

COSGAP offers a robust solution to common challenges in statistical genetics research.
Containerization significantly simplifies and standardizes complex genetic data analysis.
The platform facilitates broader access to advanced statistical genetics tools for researchers worldwide.