Cargando…

Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

Integrating -omics data with biological networks such as protein–protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data...

Descripción completa

Detalles Bibliográficos
Autores principales: Pierrelée, Michaël, Reynders, Ana, Lopez, Fabrice, Moqrich, Aziz, Tichit, Laurent, Habermann, Bianca H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249521/
https://www.ncbi.nlm.nih.gov/pubmed/34211067
http://dx.doi.org/10.1038/s41598-021-93128-5
_version_ 1783716917787230208
author Pierrelée, Michaël
Reynders, Ana
Lopez, Fabrice
Moqrich, Aziz
Tichit, Laurent
Habermann, Bianca H.
author_facet Pierrelée, Michaël
Reynders, Ana
Lopez, Fabrice
Moqrich, Aziz
Tichit, Laurent
Habermann, Bianca H.
author_sort Pierrelée, Michaël
collection PubMed
description Integrating -omics data with biological networks such as protein–protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus.
format Online
Article
Text
id pubmed-8249521
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-82495212021-07-06 Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs) Pierrelée, Michaël Reynders, Ana Lopez, Fabrice Moqrich, Aziz Tichit, Laurent Habermann, Bianca H. Sci Rep Article Integrating -omics data with biological networks such as protein–protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus. Nature Publishing Group UK 2021-07-01 /pmc/articles/PMC8249521/ /pubmed/34211067 http://dx.doi.org/10.1038/s41598-021-93128-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Pierrelée, Michaël
Reynders, Ana
Lopez, Fabrice
Moqrich, Aziz
Tichit, Laurent
Habermann, Bianca H.
Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)
title Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)
title_full Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)
title_fullStr Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)
title_full_unstemmed Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)
title_short Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)
title_sort introducing the novel cytoscape app timenexus to analyze time-series data using temporal multilayer networks (tmlns)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249521/
https://www.ncbi.nlm.nih.gov/pubmed/34211067
http://dx.doi.org/10.1038/s41598-021-93128-5
work_keys_str_mv AT pierreleemichael introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns
AT reyndersana introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns
AT lopezfabrice introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns
AT moqrichaziz introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns
AT tichitlaurent introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns
AT habermannbiancah introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns