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ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis
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 seemi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000Research
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184317/ https://www.ncbi.nlm.nih.gov/pubmed/25309732 http://dx.doi.org/10.12688/f1000research.4431.2 |
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author | Wu, Guanming Dawson, Eric Duong, Adrian Haw, Robin Stein, Lincoln |
author_facet | Wu, Guanming Dawson, Eric Duong, Adrian Haw, Robin Stein, Lincoln |
author_sort | Wu, Guanming |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4184317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | F1000Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-41843172014-10-09 ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis Wu, Guanming Dawson, Eric Duong, Adrian Haw, Robin Stein, Lincoln F1000Res Software Tool 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. F1000Research 2014-09-12 /pmc/articles/PMC4184317/ /pubmed/25309732 http://dx.doi.org/10.12688/f1000research.4431.2 Text en Copyright: © 2014 Wu G et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/publicdomain/zero/1.0/ Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). |
spellingShingle | Software Tool Wu, Guanming Dawson, Eric Duong, Adrian Haw, Robin Stein, Lincoln ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis |
title | ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis |
title_full | ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis |
title_fullStr | ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis |
title_full_unstemmed | ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis |
title_short | ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis |
title_sort | reactomefiviz: a cytoscape app for pathway and network-based data analysis |
topic | Software Tool |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184317/ https://www.ncbi.nlm.nih.gov/pubmed/25309732 http://dx.doi.org/10.12688/f1000research.4431.2 |
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