Analysis of publicly available datasets to produce novel findings with clinical relevance

Abstract

Advances in high-throughput sequencing technologies have facilitated the generation of large-scale genomic and pharmacogenomic databases. Such databases represent an important source of multi-platform data and a critical resource for biomedical research. Moreover, the computational tools available to analyse such ‘big data’ have evolved substantially in recent years. Here, we have utilised various open-access data resources for our cancer and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/coronavirus disease 2019 (COVID-19)-related research. Specifically, in our cancer studies, we have correlated expression of genes differentially expressed in response to the phosphorylation status of phosphoprotein enriched in astrocytes 15 (PEA-15) in cisplatin-treated SKOV-3 ovarian cancer cell lines with survival of cisplatin-treated patients. We have also investigated the role of deoxynucleoside triphosphate triphosphohydrolase SAMHD1 in influencing drug sensitivity and cancer patient survival using data from both cell line and clinical studies. In our SARS-CoV-2/COVID-19 studies, we have used structural data to predict the impact of differentially conserved amino acid positions (DCPs) between SARS-CoV and SARS-CoV-2 on the function of SARS-CoV-2 proteins. We have also used transcriptomic and proteomic datasets of SARS-CoV-2-infected cells and patients to identify links between pathways of COVID-19 clinicopathogenesis and deregulation of genes involved in those pathways. Our computational approach demonstrates how publicly accessible data can not only be used to complement in vitro investigations, but also to generate novel findings with clinical significance.