PyGNA: a unified framework for geneset network analysis

• Published in: BMC bioinformatics Vol. 21; no. 1; pp. 476 - 22
• Main Authors: Fanfani, Viola, Cassano, Fabio, Stracquadanio, Giovanni
• Format: Journal Article
• Language: English
• Published: London BioMed Central 22.10.2020; Springer Nature B.V; BMC
• Subjects: Algorithms; Bioinformatics; Biological Networks; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer programs; Computer Simulation; Data analysis; Data integration; Experiments; Gene Regulatory Networks; Gene sequencing; Genes; Geneset Network Analysis; Interaction models; Life Sciences; Microarrays; Network analysis; Network analysis workflow; Networks analysis; Open source software; Phenotypes; Pipelines; Programming Languages; Proteins; Ribonucleic acid; RNA; Sequence Analysis, RNA; Software; Source code; Stochastic Processes; Wiring; Geneset Network Analysis; Biological Networks; Network analysis workflow
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-03801-1

Background Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes. Results Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http://github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker. Conclusions We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.