Collecting SARS-CoV-2 Encoded miRNAs via Text Mining

Abstract

Established text mining approaches can be used to identify miRNAs mentioned in published papers and preprints. Here, we apply such a targeted approach to the LitCovid literature collection in order to find viral miRNAs published in connection to SARS-CoV-2. As LitCovid aims at being a comprehensive collection of literature on new findings on SARS-CoV-2 and the COVID-19 pandemic, it is perfectly suited for our goal of finding all reported SARS-CoV-2 miRNAs. The identified miRNAs provide an up-to-date and quite comprehensive collection of potential viral miRNAs, which is a useful resource for further research to fight the current pandemic. We identified 564 putative SARS-CoV-2 miRNAs together with the respective evidences, i.e. the original publications, and collect them for critical review. The text mining method and the corresponding synonym list are optimized for finding viral miRNAs and the results are manually curated. Since not all miRNAs were experimentally verified, the collection might contain false positives, but it is highly sensitive. Moreover, the text mining approach and resulting collection of miRNA candidates can be useful resources for further SARS-CoV-2 research and for experimental validation.