Since the emergence of SARS-CoV-2 in November 2019, there has been an exponential production of literature due to worldwide efforts to understand the interactions between the virus and the human body. Using an "in-house" developed script we retrieved gene annotations and identified phenotype enrichments. Human Phenotype Ontology terms were retrieved from the literature using the Onassis R package. This produced both disease-gene and disease-phenotype data as well as data for gene-phenotype interactions. Overall, we retrieved 181 human phenotypes that were identified by both approaches. Further in-depth analysis of these relationships could provide further insights in the molecular mechanisms related with the observed phenotypes, answers and hypotheses for key concepts within COVID-19 research.
Keywords: COVID-19; Computational Biology; Data Mining.