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PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space
BACKGROUND: Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists’ understanding of phenotypic behavior associated with specific genes. RESULTS: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333168/ https://www.ncbi.nlm.nih.gov/pubmed/28251874 http://dx.doi.org/10.1186/s12859-016-1447-1 |
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author | Ma, Chihua Luciani, Timothy Terebus, Anna Liang, Jie Marai, G. Elisabeta |
author_facet | Ma, Chihua Luciani, Timothy Terebus, Anna Liang, Jie Marai, G. Elisabeta |
author_sort | Ma, Chihua |
collection | PubMed |
description | BACKGROUND: Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists’ understanding of phenotypic behavior associated with specific genes. RESULTS: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. CONCLUSIONS: We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1447-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5333168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53331682017-03-06 PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space Ma, Chihua Luciani, Timothy Terebus, Anna Liang, Jie Marai, G. Elisabeta BMC Bioinformatics Research BACKGROUND: Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists’ understanding of phenotypic behavior associated with specific genes. RESULTS: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. CONCLUSIONS: We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1447-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-15 /pmc/articles/PMC5333168/ /pubmed/28251874 http://dx.doi.org/10.1186/s12859-016-1447-1 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Ma, Chihua Luciani, Timothy Terebus, Anna Liang, Jie Marai, G. Elisabeta PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space |
title | PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space |
title_full | PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space |
title_fullStr | PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space |
title_full_unstemmed | PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space |
title_short | PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space |
title_sort | prodigen: visualizing the probability landscape of stochastic gene regulatory networks in state and time space |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333168/ https://www.ncbi.nlm.nih.gov/pubmed/28251874 http://dx.doi.org/10.1186/s12859-016-1447-1 |
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