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Estimating gene regulatory networks with pandaR
PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interactio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870629/ https://www.ncbi.nlm.nih.gov/pubmed/28334344 http://dx.doi.org/10.1093/bioinformatics/btx139 |
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author | Schlauch, Daniel Paulson, Joseph N Young, Albert Glass, Kimberly Quackenbush, John |
author_facet | Schlauch, Daniel Paulson, Joseph N Young, Albert Glass, Kimberly Quackenbush, John |
author_sort | Schlauch, Daniel |
collection | PubMed |
description | PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks. Availability and Implementation: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR. |
format | Online Article Text |
id | pubmed-5870629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-58706292018-04-05 Estimating gene regulatory networks with pandaR Schlauch, Daniel Paulson, Joseph N Young, Albert Glass, Kimberly Quackenbush, John Bioinformatics Applications Notes PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks. Availability and Implementation: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR. Oxford University Press 2017-07-15 2017-03-11 /pmc/articles/PMC5870629/ /pubmed/28334344 http://dx.doi.org/10.1093/bioinformatics/btx139 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Schlauch, Daniel Paulson, Joseph N Young, Albert Glass, Kimberly Quackenbush, John Estimating gene regulatory networks with pandaR |
title | Estimating gene regulatory networks with pandaR |
title_full | Estimating gene regulatory networks with pandaR |
title_fullStr | Estimating gene regulatory networks with pandaR |
title_full_unstemmed | Estimating gene regulatory networks with pandaR |
title_short | Estimating gene regulatory networks with pandaR |
title_sort | estimating gene regulatory networks with pandar |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870629/ https://www.ncbi.nlm.nih.gov/pubmed/28334344 http://dx.doi.org/10.1093/bioinformatics/btx139 |
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