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Updated: Jul 12, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

RichPathR: a gene set enrichment analysis and visualization tool.

Binod Regmi¹, Stephen R Brooks¹, Andrew J Uhlman¹

¹Biodata Mining and Discovery Section, NIAMS, NIH, Bethesda, MD, USA 1.

Biorxiv : the Preprint Server for Biology

|October 27, 2023

Summary

This summary is machine-generated.

Gene set enrichment analysis (GSEA) helps discover disease and drug targets by analyzing gene lists. The richPathR package simplifies large-scale GSEA using the popular EnrichR database for biological insights.

Keywords:

Jacard’s similarity index adjacency matrix heat map network plot pathway analysis

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Last Updated: Jul 12, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

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Published on: October 27, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput biological analyses generate extensive gene lists requiring interpretation within biological contexts.
Gene set enrichment analysis (GSEA) is crucial for identifying biological functions and phenotypes associated with gene expression changes.
Existing knowledge bases and tools like EnrichR aid in GSEA, but large-scale data mining remains challenging.

Approach:

The richPathR R package provides a collection of functions for exploratory analysis and visualization of gene set enrichment.
It facilitates the use of the popular EnrichR web application for large-scale GSEA.
richPathR aims to streamline the process of extracting meaningful biological insights from complex gene lists.

Key Points:

richPathR enhances the utility of EnrichR for researchers by offering programmatic access and visualization capabilities.
The package supports the investigation of gene lists derived from various high-throughput omics studies.
It enables efficient exploration of biological functions and phenotypes linked to gene sets.

Conclusions:

richPathR offers a valuable R-based solution for researchers conducting large-scale gene set enrichment analysis.
The package simplifies the interpretation of omics data by leveraging the EnrichR knowledge base.
It supports advancements in disease and drug discovery through improved biological data exploration.