Abstract 5035: Barcode-free demultiplexing of HiFi reads using natural genetic variation

Abstract Cleverly exploiting the natural genetic variation among cells from unrelated individuals enables high throughput, highly parallel droplet single-cell RNA sequencing. We sought to lift this principle and bring analogous improvements to the setting of single-molecule long read sequencing. Toward this end, we present an error-robust method for matching individual long genomic reads to their genetic background based on a Naive Bayes Classifier. As a case study, we apply this to explore allele-specific splicing variation in the colorectal adenocarcinoma cell line DLD1 using Iso-Seq long-read RNA sequencing. Surprisingly, we uncover many allele differentially-expressed isoforms even across genes with no allele-specific expression bias in aggregate. Further, we investigate RNA-binding proteins and functional pathways potentially involved in mediating this allele-specific isoform expression pattern. In future work, our method will be applied to pooled single-molecule epigenomic screens of cancer cell line responses to treatment. Citation Format: Deniz Guney Olgun, Simai Wang, Saeid Parast, Ali Shilatifard, Hani Goodarzi, Vijay Ramani. Barcode-free demultiplexing of HiFi reads using natural genetic variation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5035.