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Updated: Jul 2, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

Joshua M Mitchell¹, Yuanye Chi¹, Maheshwor Thapa¹

¹The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.

Biorxiv : the Preprint Server for Biology

|February 26, 2024

Summary

This summary is machine-generated.

This study introduces a Python-centric pipeline for metabolomics data analysis, standardizing processing, quality control, and annotation. It enhances computational reproducibility and fills a gap in the Python ecosystem for metabolomics research.

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Area of Science:

Computational biology
Metabolomics
Bioinformatics

Background:

Standardizing metabolomics data analysis is crucial for computational development.
A lack of well-defined data structures hinders reproducible metabolomics research.

Approach:

Developed a Python-centric pipeline with standardized data structures for metabolomics.
Integrated modules for data processing, quality control, and annotation.
Utilized large-scale LC-MS metabolomics, lipidomics, and LC-MS/MS data for demonstration.

Key Points:

The pipeline streamlines data processing, quality control, and annotation.
Demonstrates efficient and transparent standardization of metabolomics data.
Reanalyzed published datasets to showcase utility in biological data analysis.

Conclusions:

This work addresses a significant gap in the Python ecosystem for computational metabolomics.
The pipeline facilitates future computational developments and enhances data analysis reproducibility.