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 webinar Arthur Dondi will discuss:

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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SPEAKER:

Arthur Dondi
PhD student in computational biology,
ETH Zürich

Wilson Cheng
Sr. Bioinformatics Scientist, Field Application, Asia Pacific
PacBio

Host & Moderator

Zhu Tingting, PhD
Bioinformatics Scientist, Field Application, Asia Pacific
PacBio