Leveraging RNA Velocity To Assess Impact Of SNVs On Splicing Dynamics In Cancer Cells
Open Access DepositedSingle-cell RNA sequencing techniques have the ability of sequencing reads at the cellular level with greater sensitivity and accuracy. This has allowed us to be able to distinguish between intronic and exonic reads, giving us insight into the abundance of spliced and unspliced RNA in cells. However, the abundance of the intronic and exonic sequences are captured in a static time frame. To overcome this limitation, RNA velocity has been developed as a computational technique to leverage the ratio of spliced and unspliced mRNA to predict future states of cells in terms of gene transcription over a time period. This concept is based on the principle that cells with higher relative abundance of unspliced RNA will undergo splicing over time and undergo degradation when they consist of lower abundance of unspliced RNA. This technique determines the rate of change of unspliced and spliced RNA over time to predict future cell states. In this study, a computational workflow leveraging a framework scVelo is developed to determine splicing dynamics in various cancer types. We introduce Single Nucleotide Variants (SNVs) observed in scRNA-seq datasets within the workflow to understand the correlation of presence of SNVs with splicing dynamics of different cells by visualizing cells consisting of these variants. Identifying SNVs that correlate with unexplained splicing dynamics will help us gain a deeper understanding of gene expression being impacted in various cancer cell types.
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