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

Area of Science:

• Computational biology
• Systems biology
• Genomics

Background:

• Standard transcriptomic analyses are insufficient for fully elucidating disease pathophysiology and outcomes.
• A need exists for advanced methods to integrate heterogeneous biological data for deeper molecular insights.

Purpose of the Study:

• To present a novel computational protocol for heterogeneous data integration and mining.
• To identify functional feature complexes and modules representing higher-order biological machines.
• To apply this protocol for the functional characterization of COVID-19.

Main Methods:

• Integration of transcriptional signatures from multiple model systems.
• Incorporation of protein-protein interaction networks.
• Analysis of single-cell RNA-sequencing markers.
• Inclusion of phenotype-genotype associations.

Main Results:

• Identification of functional feature complexes (modules) from integrated data.
• These modules represent coordinated biological activities underlying disease processes.
• Demonstration of the protocol's utility in COVID-19 functional characterization.

Conclusions:

• The developed protocol enables a more comprehensive understanding of disease mechanisms beyond standard transcriptomics.
• Functional feature modules offer insights into coordinated molecular activities driving pathophysiology.
• This approach is adaptable for studying various complex diseases.