Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights.

ABSTRACT: The emergence of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of coronavirus disease-2019 (COVID-19) during the pandemic. This study coupled existing transcriptomic data from severe acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1) lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g

SUBMITTER: Flynn J 

PROVIDER: S-EPMC10723038 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature