Detecting COVID-19 related human orphan gene expression in single cells with comparative genomics and data mining

The COVID-19 pandemic caused by the novel SARS-CoV-2 virus has infected nearly 200 million people and\n killed over 4 million world-wide. Orphan (or “de novo”) genes code for proteins that do not have recognizable homologs in any other species; these genes are a major source of evolutionary novelty and can play a major role in conferring novel beneficial traits to the species. Because they have no homologs, many orphan genes have not yet been identified, and are therefore not represented in most processed gene-expression analyses, even in the very important processed expression data on SARS-CoV-2 infected/non-infected tissues at the Covid-19 Data Portal. The proposed comprehensive analysis of these raw single cell RNA-seq data from human datasets with SARS-CoV-2 infected/non-infected tissues can reveal orphan genes that are expressed in association with Covid-19, leading to the identification of marker genes of the infection and potentially prognostic genes that can be used to infer the severity of the infection or even as a treatment target.