Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators

Complex organisms originate from and are maintained by the information encoded in the genome. A major challenge of systems biology is to develop algorithms that describe the dynamic regulation of genome functions from large omics datasets. Here, we describe TETRAMER, which reconstructs gene-regulato...

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Autores principales: Cholley, Pierre-Etienne, Moehlin, Julien, Rohmer, Alexia, Zilliox, Vincent, Nicaise, Samuel, Gronemeyer, Hinrich, Mendoza-Parra, Marco Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6070484/
https://www.ncbi.nlm.nih.gov/pubmed/30083390
http://dx.doi.org/10.1038/s41540-018-0066-z
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author Cholley, Pierre-Etienne
Moehlin, Julien
Rohmer, Alexia
Zilliox, Vincent
Nicaise, Samuel
Gronemeyer, Hinrich
Mendoza-Parra, Marco Antonio
author_facet Cholley, Pierre-Etienne
Moehlin, Julien
Rohmer, Alexia
Zilliox, Vincent
Nicaise, Samuel
Gronemeyer, Hinrich
Mendoza-Parra, Marco Antonio
author_sort Cholley, Pierre-Etienne
collection PubMed
description Complex organisms originate from and are maintained by the information encoded in the genome. A major challenge of systems biology is to develop algorithms that describe the dynamic regulation of genome functions from large omics datasets. Here, we describe TETRAMER, which reconstructs gene-regulatory networks from temporal transcriptome data during cell fate transitions to predict “master” regulators by simulating cascades of temporal transcription-regulatory events.
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spelling pubmed-60704842018-08-06 Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators Cholley, Pierre-Etienne Moehlin, Julien Rohmer, Alexia Zilliox, Vincent Nicaise, Samuel Gronemeyer, Hinrich Mendoza-Parra, Marco Antonio NPJ Syst Biol Appl Brief Communication Complex organisms originate from and are maintained by the information encoded in the genome. A major challenge of systems biology is to develop algorithms that describe the dynamic regulation of genome functions from large omics datasets. Here, we describe TETRAMER, which reconstructs gene-regulatory networks from temporal transcriptome data during cell fate transitions to predict “master” regulators by simulating cascades of temporal transcription-regulatory events. Nature Publishing Group UK 2018-08-02 /pmc/articles/PMC6070484/ /pubmed/30083390 http://dx.doi.org/10.1038/s41540-018-0066-z Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Brief Communication
Cholley, Pierre-Etienne
Moehlin, Julien
Rohmer, Alexia
Zilliox, Vincent
Nicaise, Samuel
Gronemeyer, Hinrich
Mendoza-Parra, Marco Antonio
Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
title Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
title_full Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
title_fullStr Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
title_full_unstemmed Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
title_short Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
title_sort modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6070484/
https://www.ncbi.nlm.nih.gov/pubmed/30083390
http://dx.doi.org/10.1038/s41540-018-0066-z
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