Computational workflow for functional characterization of COVID-19 through secondary data analysis

ABSTRACT: Summary Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021). Graphical abstract Highlights • Steps for meta-analysis of multiple transcriptomic studies and protein interactions• Network analysis-based workflow to identify gene and functional modules• Data-driven higher-order functional features provide a basis for characterizing disease Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases.

SUBMITTER: Ghandikota S 

PROVIDER: S-EPMC8551262 | biostudies-literature |

REPOSITORIES: biostudies-literature