tetano
Editor, Senior Moderator
Patterns (N Y)
. 2021 Apr 5;100247.
doi: 10.1016/j.patter.2021.100247. Online ahead of print.
Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19
Sudhir Ghandikota[SUP] 1 2 [/SUP], Mihika Sharma[SUP] 1 [/SUP], Anil G Jegga[SUP] 1 2 3 [/SUP]
Affiliations
Abstract
Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable in understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.
Keywords: ? COVID-19? SARS-CoV-2? coronavirus? data mining? data integration? pattern search? meta-analysis? module detection? network analysis.
. 2021 Apr 5;100247.
doi: 10.1016/j.patter.2021.100247. Online ahead of print.
Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19
Sudhir Ghandikota[SUP] 1 2 [/SUP], Mihika Sharma[SUP] 1 [/SUP], Anil G Jegga[SUP] 1 2 3 [/SUP]
Affiliations
- PMID: 33842903
- PMCID: PMC8020120
- DOI: 10.1016/j.patter.2021.100247
Abstract
Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable in understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.
Keywords: ? COVID-19? SARS-CoV-2? coronavirus? data mining? data integration? pattern search? meta-analysis? module detection? network analysis.