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Gene Families

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

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Related Experiment Video

Updated: Jun 25, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

ReactomeGSA: new features to simplify public data reuse.

Alexander Grentner¹, Eliot Ragueneau², Chuqiao Gong²

¹Department of Dermatology, Medical University of Vienna, Vienna 1090, Austria.

Bioinformatics (Oxford, England)

|May 28, 2024

Summary

This summary is machine-generated.

ReactomeGSA now offers simplified integration of public multi-omics datasets through a new Python loader and enhanced search functionality. This update improves accessibility and comparative pathway analysis across species.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

ReactomeGSA is a leading multi-omics pathway analysis platform within the Reactome knowledgebase.
It supports quantitative pathway analysis for diverse 'omics data types and comparative analysis across datasets and species.

Purpose of the Study:

To present a major update of ReactomeGSA, simplifying public data integration and reuse.
To enhance the platform's capabilities for comparative pathway analysis using publicly available datasets.

Main Methods:

Developed the `grein_loader` Python application to fetch experiments from the GREIN resource.
Integrated support for EMBL-EBI's Expression Atlas and GEO RNA-seq Experiments Interactive Navigator.
Implemented a novel search function for public datasets across supported resources.
Completely redeveloped the ReactomeGSA web frontend and R/Bioconductor package.

Main Results:

Enabled direct fetching and integration of public datasets from GREIN, Expression Atlas, and GEO.
Introduced a unified search interface for discovering public datasets.
Simplified the user experience through a redeveloped web frontend and R package.
Facilitated easier comparative pathway analysis across multiple datasets and species.

Conclusions:

The updated ReactomeGSA platform significantly enhances the accessibility and usability of public multi-omics data for pathway analysis.
The new features streamline comparative pathway analysis, promoting broader scientific discovery.
The platform is readily available through a web interface, R/Bioconductor package, and Python application.