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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.

Zhuoqing Fang¹, Xinyuan Liu², Gary Peltz¹

¹Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.

Bioinformatics (Oxford, England)

|November 25, 2022

Summary

We developed GSEApy, a Python package for gene set enrichment analysis (GSEA). GSEApy efficiently analyzes large single-cell datasets and offers faster performance with its Rust implementation.

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Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Gene Set Enrichment Analysis (GSEA) is vital for interpreting gene expression data.
Existing GSEA tools struggle with large datasets, especially single-cell data.

Purpose of the Study:

Introduce GSEApy, a Python package designed for efficient GSEA.
Address the limitations of current GSEA tools for large-scale datasets.

Main Methods:

Developed GSEApy, a Python package for GSEA.
Implemented a Rust core for enhanced performance.
Integrated interfaces for Enrichr and BioMart.

Main Results:

GSEApy efficiently analyzes large single-cell datasets.
The Rust implementation is 3x faster and uses 4x less memory than the previous Numpy version.
GSEApy supports command-line and Python environments, with added Enrichr and BioMart integration.

Conclusions:

GSEApy provides an efficient and scalable solution for GSEA.
Facilitates over-representation analysis and other enrichment analyses.
Offers a robust tool for modern genomic data analysis.