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dc.contributor.authorPadi, Megha
dc.contributor.authorQuackenbush, John
dc.date.accessioned2019-06-27T16:37:20Z
dc.date.available2019-06-27T16:37:20Z
dc.date.issued2018-04-19
dc.identifier.citationPadi, M., & Quackenbush, J. (2018). Detecting phenotype-driven transitions in regulatory network structure. NPJ systems biology and applications, 4(1), 16.en_US
dc.identifier.issn2056-7189
dc.identifier.pmid29707235
dc.identifier.doi10.1038/s41540-018-0052-5
dc.identifier.urihttp://hdl.handle.net/10150/633047
dc.description.abstractComplex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.en_US
dc.description.sponsorshipNIH [K25 HG006031, R01 HL111759, R35 CA197449]en_US
dc.language.isoenen_US
dc.publisherNATURE PUBLISHING GROUPen_US
dc.relation.urlhttps://www.nature.com/articles/s41540-018-0052-5en_US
dc.rights© The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.titleDetecting phenotype-driven transitions in regulatory network structureen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Mol & Cellular Biolen_US
dc.identifier.journalNPJ SYSTEMS BIOLOGY AND APPLICATIONSen_US
dc.description.noteOpen access journalen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleNPJ systems biology and applications
refterms.dateFOA2019-06-27T16:37:21Z


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