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

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  • Additional Information
    • Source:
      Publisher: Cell Press Country of Publication: United States NLM ID: 101769501 Publication Model: eCollection Cited Medium: Internet ISSN: 2666-1667 (Electronic) Linking ISSN: 26661667 NLM ISO Abbreviation: STAR Protoc
    • Publication Information:
      Original Publication: [Cambridge, MA] : Cell Press, [2020]-
    • Subject Terms:
      Data Analysis* ; Software* ; Transcriptome* ; Workflow*; COVID-19/*diagnosis ; Computational Biology/*methods ; SARS-CoV-2/*isolation & purification; COVID-19/genetics ; COVID-19/virology ; Humans ; SARS-CoV-2/genetics
    • Abstract:
      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).
      Competing Interests: The authors declare no competing interests.
      (© 2021 The Author(s).)
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    • Grant Information:
      UG3 TR002612 United States TR NCATS NIH HHS
    • Contributed Indexing:
      Keywords: Bioinformatics; Gene Expression; Genomics; Health Sciences; Immunology; RNAseq; Single Cell; Systems biology
    • Publication Date:
      Date Created: 20211108 Date Completed: 20220218 Latest Revision: 20221231
    • Publication Date:
      20221231
    • Accession Number:
      PMC8551262
    • Accession Number:
      10.1016/j.xpro.2021.100873
    • Accession Number:
      34746856