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Updated: Dec 4, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

PyGNA: a unified framework for geneset network analysis.

Viola Fanfani¹, Fabio Cassano¹, Giovanni Stracquadanio²

¹School of Biological Science, The University of Edinburgh, Edinburgh, EH9 3BF, UK.

BMC Bioinformatics

|October 23, 2020

Summary

This summary is machine-generated.

We developed Python Geneset Network Analysis (PyGNA), an open-source tool for analyzing gene and protein interaction networks. PyGNA aids in identifying disease-associated networks by integrating functional genomics data for robust biological network analysis.

Keywords:

Biological Networks Geneset Network Analysis Network analysis workflow

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Gene and protein interactions reveal cellular molecular mechanisms.
Integrating functional genomics data aids in identifying disease-associated networks.

Purpose of the Study:

Introduce an integrated statistical framework for network analysis of gene sets.
Develop open-source software (PyGNA) for network-aware geneset analysis.
Facilitate integration into existing analysis pipelines.

Main Methods:

Implemented an integrated statistical framework as open-source software (PyGNA).
Utilized multi-core systems for generating null distributions on large datasets.
Benchmarked PyGNA's tests and demonstrated its use in RNA sequencing data analysis.

Main Results:

PyGNA provides a tool for network-aware geneset analysis.
The software is designed for easy integration and high-quality output generation.
Demonstrated PyGNA's utility in a use case inspired by RNA sequencing data.

Conclusions:

PyGNA offers a viable approach for large-scale geneset network analysis.
The tool can be readily used or extended as a Python package.
Facilitates the study of biological networks with increasing omic data availability.