ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis
Author(s) -
Guanming Wu,
Eric T. Dawson,
Adrian Duong,
Robin Haw,
Lincoln Stein
Publication year - 2014
Publication title -
f1000research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.099
H-Index - 60
ISSN - 2046-1402
DOI - 10.12688/f1000research.4431.2
Subject(s) - computer science , suite , context (archaeology) , biological network , biological pathway , gene regulatory network , computational biology , data mining , systems biology , data science , bioinformatics , gene , biology , gene expression , history , paleontology , biochemistry , archaeology
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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