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    Detecting phenotype-driven transitions in regulatory network structure

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    Author
    Padi, Megha
    Quackenbush, John
    Affiliation
    Univ Arizona, Dept Mol & Cellular Biol
    Issue Date
    2018-04-19
    
    Metadata
    Show full item record
    Publisher
    NATURE PUBLISHING GROUP
    Citation
    Padi, M., & Quackenbush, J. (2018). Detecting phenotype-driven transitions in regulatory network structure. NPJ systems biology and applications, 4(1), 16.
    Journal
    NPJ SYSTEMS BIOLOGY AND APPLICATIONS
    Rights
    © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License.
    Collection Information
    This 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.
    Abstract
    Complex 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.
    Note
    Open access journal
    ISSN
    2056-7189
    PubMed ID
    29707235
    DOI
    10.1038/s41540-018-0052-5
    Version
    Final published version
    Sponsors
    NIH [K25 HG006031, R01 HL111759, R35 CA197449]
    Additional Links
    https://www.nature.com/articles/s41540-018-0052-5
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41540-018-0052-5
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