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Version 2. F1000Res. 2014 Jul 1 [revised 2014 Sep 12];3:146. doi: 10.12688/f1000research.4431.2. eCollection 2014.

ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis.

Author information

1
Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada ; DMICE, Oregon Health & Science University, Portland, Oregon 97239, USA.
2
Section of Integrative Biology, Institute for Cellular and Molecular Biology, and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA.
3
Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada.
4
Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada ; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.

Abstract

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called "ReactomeFIViz", which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

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