Detecting cancer-related RNA dysregulation with long-read sequencing 

Full-length, single-cell RNA data provides critical insights into the understanding of cancer transcriptomic features, such as isoforms, fusions, and expressed mutations. However, until recently, cell typing based on gene expression has been challenging with long reads due to insufficient sequencing depth.

In a recent preprint, Dondi et al. (2022) used transcript concatenation and highly accurate long-read sequencing (HiFi sequencing) to increase sequencing depth. This increase enabled cell type identification without needing short reads, and the detection of many new isoforms. They also detected fusions, cell-specific and cell type-specific isoform usage, and revealed differential isoform expression in tumor and mesothelial cells.

The authors conclude that “future studies with similar or increased long-read throughput will not have to rely on parallel short-read sequencing, thereby saving cost and labor.”

In this on demand webinar Arthur Dondi discusses:

  • Detection of cell type-specific known and novel isoforms
  • Capture and quantification of full-length isoforms, mutations and fusions in the same scRNA-seq dataset
  • Detection of a gene fusion that was misclassified in matched short-read data

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Arthur Dondi
PhD student in computational biology,
ETH Zürich

Mark Chaisson, PhD
Assistant Professor
Computational Biology and Bioinformatics
University of Southern California
Guest speaker

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Ted Kalbfleisch, Ph.D., Associate Professor, University of Kentucky

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Mitchell Feldmann, Ph.D., Postdoctoral Fellow, University of California, Davis.