Accurate long-read transcript discovery and quantification at single-cell, pseudo-bulk and bulk resolution with Isosceles
Michał Kabza, Alexander J. Ritter, Ashley Byrne, Kostianna Sereti, Daniel Le, William Stephenson, Timothy Sterne-Weiler
Abstract
Accurate detection and quantification of mRNA isoforms from nanopore long-read sequencing remains challenged by technical noise, particularly in single cells. To address this, we introduce Isosceles, a computational toolkit that outperforms other methods in isoform detection sensitivity and quantification accuracy across single-cell, pseudo-bulk and bulk resolution levels, as demonstrated using synthetic and biologically-derived datasets. Here we show Isosceles improves the fidelity of single-cell transcriptome quantification at the isoform-level, and enables flexible downstream analysis. As a case study, we apply Isosceles, uncovering coordinated splicing within and between neuronal differentiation lineages. Isosceles is suitable to be applied in diverse biological systems, facilitating studies of cellular heterogeneity across biomedical research applications. Analysis of nanopore long-read sequencing is challenged by technical noise, particularly in single cells. Here, authors introduce Isosceles, a toolkit for accurate isoform detection, quantification, and flexible downstream analysis of long-read data at single-cell, pseudo-bulk and bulk resolutions.