Back

Assessing the readiness of Oxford Nanopore sequencing for clinical genomics applications

Arres, J.; Elavalli, S.; Behl, S.; Matias Sanchez, D.; Al Ali, A.; Ahmed Yehia Abdelaziz Saad, A.; Attia, A.; Minas, C.; Pariyachery, S.; Ahmed, S.; Aldhuhoori, F.; Thulasidharan, N.; Katagi, G.; Soliman, O.; Purohit, S.; Kusuma, V.; Cardoso, T.; Paulin, L. F.; Sanio, P.; Mafofo, J.; Wu, H.; Zvereff, V.; El-Khani, A.; Al Marzooqi, F.; Magalhaes, T. R.; Sedlazeck, F.; Quilez, J.

2024-11-04 genetic and genomic medicine
10.1101/2024.10.30.24316253 medRxiv
Show abstract

Long-read sequencing (LRS) technologies, namely Oxford Nanopore Technologies (ONT) and Pacific Biosciences, have emerged as promising solutions to overcome the limitations of short-read sequencing (SRS). Nevertheless, the still higher sequencing error rates compared to SRS, need for customized pipelines, rapidly updating software and incipient scalability are proving the adoption of the ONT for standard clinical practice to be challenging. Here we assess the performance of ONT (R9 and R10 chemistries) in comparison to Illumina and MGI across 17 well-characterized reference samples with 11 clinical variants representing 9 different genetic diseases. To enable this, we have implemented a production-ready pipeline including SNV, INDEL, STR, SV and CNV detection together with reporting key summary metrics to ensure high quality of data at production sequencing level. Our results show high accuracy of ONT across SNV (F-score >0.975) and SV, but still several weaknesses across INDEL (F-score<0.80). However, we highlight that ONT accurately detected all four pathogenic INDELs as well as the performance improvement in exons and with the newer R10 chemistry. We further demonstrated the importance of long-reads to detect clinical-impacting variants such as a FMR1 pathogenic expansion, often misclassified by SRS as premutation range. Some issues remain as long-read analysis reported the wrong CNV genotype in one of the medical case samples. Remarkably, our multi-platform analysis and Sanger validation discovered a 1-bp error in the Coriell annotation for a cystic fibrosis causing INDEL in GM07829. Overall, this work highlights the readiness of ONT for clinical applications and large-scale operations.

Published in Genome Research (predicted rank #2) · training set

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.