A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read data
Abstract
Alternative splicing plays a crucial role in transcriptomic complexity, yet remains difficult to resolve at the singlecell level due to the limitations of short-read technologies. Coupling single-cell with long-read sequencing offers fulllength transcript coverage, enabling more accurate isoform detection. Multiple specialized computational tools tailored for single-cell and spatial long-read transcriptomics have been developed, with diverse strategies. To compare the effectiveness of these approaches, we generated paired shortread and Nanopore long-read single-cell datasets, tailored for benchmarking bioinformatics tools. We evaluated ten state-ofthe-art methods, spanning four analytical dimensions: barcodes and UMI detection, demultiplexing and UMI clustering, gene-level expression profiling, and isoform detection and quantification.
Using real and simulated datasets across different protocols, sequencing depths and chemistries, we assessed the accuracy, robustness, and scalability of each tool. Our results revealed method-specific trade-offs, and highlight the importance of sequencing quality and UMI correction strategies. This benchmark provides a practical resource for optimizing isoform analysis and accurate gene expression profiling in single-cell and spatial transcriptomics using longread sequencing. Our benchmarking workflow is designed to be reusable, thereby enabling method developers to compare their own approaches against the set of reference methods evaluated in this work.
single-cell | long-read | spatial transcriptomics | isoforms | benchmark
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BibTeX
@article{hamraoui2026systematic,
title = {{A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read data}},
author = {Ali Hamraoui and Audrey Onfroy and Catherine Sénamaud-Beaufort and Fanny Coulpier and Sophie Lemoine and Laurent Jourdren and Morgane Thomas-Chollier},
journal = {NAR Genomics and Bioinformatics},
year = {2026},
month = jul,
doi = {10.1093/nargab/lqag070},
url = {https://hal.science/hal-05190995v2},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read data
AU - Ali Hamraoui
AU - Audrey Onfroy
AU - Catherine Sénamaud-Beaufort
AU - Fanny Coulpier
AU - Sophie Lemoine
AU - Laurent Jourdren
AU - Morgane Thomas-Chollier
PY - 2026
DA - 2026/07/06
JO - NAR Genomics and Bioinformatics
DO - 10.1093/nargab/lqag070
UR - https://hal.science/hal-05190995v2
AB - Alternative splicing plays a crucial role in transcriptomic complexity, yet remains difficult to resolve at the singlecell level due to the limitations of short-read technologies. Coupling single-cell with long-read sequencing offers fulllength transcript coverage, enabling more accurate isoform detection. Multiple specialized computational tools tailored for single-cell and spatial long-read transcriptomics have been developed, with diverse strategies. To compare the effectiveness of these approaches, we generated paired shortread and Nanopore long-read single-cell datasets, tailored for benchmarking bioinformatics tools. We evaluated ten state-ofthe-art methods, spanning four analytical dimensions: barcodes and UMI detection, demultiplexing and UMI clustering, gene-level expression profiling, and isoform detection and quantification. Using real and simulated datasets across different protocols, sequencing depths and chemistries, we assessed the accuracy, robustness, and scalability of each tool. Our results revealed method-specific trade-offs, and highlight the importance of sequencing quality and UMI correction strategies. This benchmark provides a practical resource for optimizing isoform analysis and accurate gene expression profiling in single-cell and spatial transcriptomics using longread sequencing. Our benchmarking workflow is designed to be reusable, thereby enabling method developers to compare their own approaches against the set of reference methods evaluated in this work. single-cell | long-read | spatial transcriptomics | isoforms | benchmark
SN - 2631-9268
ER -