https://socictopen.socict.org/files/original/539377e70989443fd364316eb4650e8f.pdf 5aab465167636d91faf08451e9e9a4d6 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Multi-omic network signatures of disease Creator An entity primarily responsible for making the resource David L Gibbs, Lisa E Gralinski, Ralph S. Baric, Shannon K McWeeney Description An account of the resource To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis.We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. Date A point or period of time associated with an event in the lifecycle of the resource 2014 Subject The topic of the resource proteomics, Transcription, Genetic, transfection, Virology, omics, biomarkers Identifier An unambiguous reference to the resource within a given context DOI: 10.3389/fgene.2013.00309 Source A related resource from which the described resource is derived Frontiers in Genetics Publisher An entity responsible for making the resource available Frontiers Media S.A. Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Genetics Language A language of the resource EN