Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19. Ghandikota et al

Published: 31 March 2021| Version 1 | DOI: 10.17632/3cwxv9swkc.1
Sudhir Ghandikota,


Supplemental dataset associated with our work, "Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19". In this study, we proposed an integrated network analysis framework that integrates transcriptional gene signatures from multiple model systems with protein-protein interactions to find gene clusters. By performing a meta-analysis of multiple feature types enriched in these gene modules, we extract communities of similar and interconnected features. These higher-order feature clusters, working as a multifeatured machine, enable us to assess their contributions towards a disease or phenotype. We show the utility of our proposed workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identified several pathways and biological processes that could help towards understanding and hypothesizing molecular signatures involved in COVID-19.


Steps to reproduce

The code and input data files required for reproducing our results and figures is accessible at


Cincinnati Children's Hospital and Medical Center Hospital Medicine, University of Cincinnati


Data Mining, Data Integration, Meta-Analysis, Network Analysis, Coronavirus, Pattern Detection, Severe Acute Respiratory Syndrome Coronavirus 2, COVID-19