Towards Democratizing CNS Tumor Classification: 10-fold reduction in methylation sequencing cost with the Flongle Flow Cell
- 1Tel Aviv University
Abstract
Oxford Nanopore Technologies-based methylation profiling enables rapid, accurate CNS tumor classification but is currently mostly performed with MinION flow cells (~$1,000 USD). We benchmarked the low-cost Flongle flow cell (~$100, 10-fold reduction) for methylation-based tumor classification. Across 12 pediatric CNS tumor samples with highly variable sequencing yields (17-330 Mbp), both Sturgeon and nanoDx classifiers achieved perfect diagnostic accuracy when operating above significance thresholds, despite extreme data sparsity. Bootstrap analysis determined empirically defined minimum data thresholds. These results demonstrate that Flongle flow cells enable cost-effective deployment of rapid, accurate nanopore-based CNS diagnostics while maintaining clinical reliability.
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BibTeX
@misc{grunwald2026towards,
title = {{Towards Democratizing CNS Tumor Classification: 10-fold reduction in methylation sequencing cost with the Flongle Flow Cell}},
author = {A. Grunwald and G. Feinberg-Gorenshtein and H. Toledano and Y. Birger and S. Izraeli and Y. Ebenstein},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.03.26361431},
url = {https://www.medrxiv.org/content/10.64898/2026.09.03.26361431v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Towards Democratizing CNS Tumor Classification: 10-fold reduction in methylation sequencing cost with the Flongle Flow Cell
AU - A. Grunwald
AU - G. Feinberg-Gorenshtein
AU - H. Toledano
AU - Y. Birger
AU - S. Izraeli
AU - Y. Ebenstein
PY - 2026
DA - 2026/09/03
PB - medRxiv
DO - 10.64898/2026.09.03.26361431
UR - https://www.medrxiv.org/content/10.64898/2026.09.03.26361431v1
AB - Oxford Nanopore Technologies-based methylation profiling enables rapid, accurate CNS tumor classification but is currently mostly performed with MinION flow cells (~$1,000 USD). We benchmarked the low-cost Flongle flow cell (~$100, 10-fold reduction) for methylation-based tumor classification. Across 12 pediatric CNS tumor samples with highly variable sequencing yields (17-330 Mbp), both Sturgeon and nanoDx classifiers achieved perfect diagnostic accuracy when operating above significance thresholds, despite extreme data sparsity. Bootstrap analysis determined empirically defined minimum data thresholds. These results demonstrate that Flongle flow cells enable cost-effective deployment of rapid, accurate nanopore-based CNS diagnostics while maintaining clinical reliability.
ER -