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

Abstract 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 -o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Michaël Pierrelée, Ana Reynders, Fabrice Lopez, Aziz Moqrich, Laurent Tichit, Bianca H. Habermann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a290c56fc05d43d9bc816fdaed2e0bed
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a290c56fc05d43d9bc816fdaed2e0bed
record_format	dspace
spelling	oai:doaj.org-article:a290c56fc05d43d9bc816fdaed2e0bed2021-12-02T14:33:57ZIntroducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)10.1038/s41598-021-93128-52045-2322https://doaj.org/article/a290c56fc05d43d9bc816fdaed2e0bed2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93128-5https://doaj.org/toc/2045-2322Abstract 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 .Michaël PierreléeAna ReyndersFabrice LopezAziz MoqrichLaurent TichitBianca H. HabermannNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michaël Pierrelée Ana Reynders Fabrice Lopez Aziz Moqrich Laurent Tichit Bianca H. Habermann Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)
description	Abstract 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	article
author	Michaël Pierrelée Ana Reynders Fabrice Lopez Aziz Moqrich Laurent Tichit Bianca H. Habermann
author_facet	Michaël Pierrelée Ana Reynders Fabrice Lopez Aziz Moqrich Laurent Tichit Bianca H. Habermann
author_sort	Michaël Pierrelée
title	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_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_sort	introducing the novel cytoscape app timenexus to analyze time-series data using temporal multilayer networks (tmlns)
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a290c56fc05d43d9bc816fdaed2e0bed
work_keys_str_mv	AT michaelpierrelee introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns AT anareynders introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns AT fabricelopez introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns AT azizmoqrich introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns AT laurenttichit introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns AT biancahhabermann introducingthenovelcytoscapeapptimenexustoanalyzetimeseriesdatausingtemporalmultilayernetworkstmlns
_version_	1718391148287885312

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

Ejemplares similares