dc.contributor.author
Morselli Gysi, Deisy
dc.contributor.author
de Miranda Fragoso, Tiago
dc.contributor.author
Zebardast, Fatemeh
dc.contributor.author
Bertoli, Wesley
dc.contributor.author
Busskamp, Volker
dc.contributor.author
Almaas, Eivind
dc.contributor.author
Nowick, Katja
dc.date.accessioned
2021-01-18T12:21:29Z
dc.date.available
2021-01-18T12:21:29Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/29295
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-29042
dc.description.abstract
Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and-to best of our knowledge-no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts:Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects linksandnodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as anRpackage in CRAN (https://CRAN. R-project.org/package=CoDiNA).
en
dc.format.extent
28 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
gene-expression
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
e0240523
dcterms.bibliographicCitation.doi
10.1371/journal.pone.0240523
dcterms.bibliographicCitation.journaltitle
PLoS ONE
dcterms.bibliographicCitation.number
10
dcterms.bibliographicCitation.volume
15
dcterms.bibliographicCitation.url
https://doi.org/10.1371/journal.pone.0240523
refubium.affiliation
Biologie, Chemie, Pharmazie
refubium.affiliation.other
Institut für Biologie
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1932-6203
refubium.resourceType.provider
WoS-Alert