Currently there is a lack of efficient computational pipelines/tools for conducting alignment of viral reads to a viral genome for specific viral genome sequence reads from single cell RNA sequencing (scRNAseq) experiments of virus-infected cells. Contemporary options utilize a time and labor-intensive process that includes integration of the viral genome and viral gene location information into the host reference genome data set, regeneration of genome index files, and then conducting read alignments. To address the need for new tools to directly map and quantify viral sequence reads from within an infected cell scRNAseq data sets, we have built a python package, called scViralQuant. scViralQuant extracts sequences that were not aligned to the primary host genome, maps them to a viral genome of interest, counts viral reads, and then reintegrates viral sequence counts into matrix files that are used by standard single cell pipelines for downstream analyses with only one command. scViralQuant provides a scRNAseq viral genome-wide sequence read abundance analysis.
