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

Autor: Aziz Moqrich, Fabrice Lopez, Bianca Habermann, Michaël Pierrelée, Laurent Tichit, Ana Reynders
Přispěvatelé: Institut de Biologie du Développement de Marseille (IBDM), Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Theories and Approaches of Genomic Complexity (TAGC), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), ANR-17-CE16-0020,Myochronic,Etude des mécanismes moléculaires qui sous-tendent la chronicisation de la douleur. Ce qu'une Myosine non conventionnelle nous apprend(2017), Spinelli, Lionel, Etude des mécanismes moléculaires qui sous-tendent la chronicisation de la douleur. Ce qu'une Myosine non conventionnelle nous apprend - - Myochronic2017 - ANR-17-CE16-0020 - AAPG2017 - VALID
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scientific Reports
Scientific Reports, Nature Publishing Group, 2021, 11 (1), ⟨10.1038/s41598-021-93128-5⟩
Scientific Reports, 2021, 11 (1), ⟨10.1038/s41598-021-93128-5⟩
Scientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
ISSN: 2045-2322
DOI: 10.1038/s41598-021-93128-5⟩
Popis: 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.
Databáze: OpenAIRE
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