IP : Oxford Nanopore Technologies and SeqOne partner to support interpretation of nanopore sequencing in clinical use

A new partnership will provide end-to-end solutions from sample to report for whole genome nanopore sequencing reads at scale in future clinical applications, including rare disease and oncology

Oxford Nanopore Technologies plc (Oxford Nanopore), the company delivering a new generation of nanopore-based molecular sensing technology, and SeqOne, the leading AI-driven genomic decision support software, today announced a new partnership enabling end-to-end analytical workflows from sample to report, focusing on rare diseases in the short-term and oncology in the longer-term.

Available today on SeqOne's platform, the germVar application enables AI-enhanced whole genome variant interpretation from Oxford Nanopore EPI2ME™, currently for research use only (RUO).

SeqOne's germVar WGS offers precise and comprehensive variant analysis for single and family cases (CNV, SNPs, INDEL, SV, STR) within an advanced and intuitive interpretation hub (phasing display, in-silico panels, advanced viewers, gold standard scores and variant knowledge base).

Designed to streamline whole genome variant interpretation at scale, SeqOne DiagAi (RUO) saves time and reduces costs by ranking, shortlisting, and suggesting causative variants with best-in-class accuracy. It also enables one-click HPO extraction from clinical notes with DiagAI Notes2HPO large language model.

After a successful test period with early adopters, germVar is now readily available for free trial testing within molecular diagnostic laboratories.

With currently more than 9,000 different rare diseases recognised, and counting, the Developing Nations Working Group of the Undiagnosed Diseases Network International (DNWG-UDNI) estimates that up to 50% of patients with a rare disease remain undiagnosed even in advanced clinical settings where genome sequencing techniques are applied routinely. This collaboration will enable the simplified analysis of nanopore sequencing and will bring the benefits of richer, more complete data to enable the characterisation of rare disease.

In the future, the collaboration will deliver other variant interpretation applications addressing the needs of cancer predisposition and somatic analysis.

In parallel, leveraging its best-in-class bioinformatics capabilities, SeqOne also announced its global Research Partnership Program that will leverage Oxford Nanopore Adaptive Sampling's data-rich insights to develop custom bioinformatic pipelines, working closely with the medical community and life science companies to improve diagnostic yield and patient care pathways.

An example of such research partnerships includes a collaboration with Pr. Laurent Mesnard, Nephrologist at the APHP. Sorbonne Université, Paris and Co-Director of the National Centre for Thrombotic microangiopathies, to improve aHUS rare disease diagnostic yield and care pathway with a custom, patent-pending, bioinformatic pipeline enabling CFH tandem double hybrid detection.

Gordon Sanghera, CEO, Oxford Nanopore Technologies, commented:

"We are excited to collaborate with SeqOne to provide end-to-end solutions for our customers in rare disease and oncology. Combining nanopore sequencing data with SeqOne's AI-powered variant interpretation platform will support the time-sensitive workflows of our clinical customers, and we look forward to advancing their research and supporting future clinical use".

Martin Dubuc, CEO, SeqOne, commented:

"The partnership with Oxford Nanopore Technologies represents a significant step forward for our customers in integrating cutting-edge long-read sequencing into their healthcare diagnostics routine workflows and research efforts. This collaboration not only enhances our ability to offer comprehensive genomic analyses but also strengthens our commitment to transforming patient care through innovative, data-rich genomic insights."

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IP Group plc published this content on 11 March 2024 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 12 March 2024 14:45:03 UTC.