Cornetto: adaptively integrated nanopore sequencing and genome assembly | LC 25

Biography

Ira Deveson is a mid-career researcher leading the Genomic Technologies Lab at the Garvan Institute of Medical Research (Sydney, Australia). His research goals are to develop, adopt, optimise, and validate new techniques that may shed new light on the genome, show how these can be used to address unsolved challenges in genomic medicine, and facilitate their eventual translation into clinical practice.

Abstract

Recent advances in long-read sequencing and assembly algorithms have made it possible to generate highly complete telomere-to-telomere (T2T) genome assemblies for humans, animals, plants, and other eukaryotes. However, producing a genome assembly at T2T quality remains expensive and technically challenging. There is a need for ongoing development to improve the affordability of data production, increase the range of usable sample types, and reliably resolve the most challenging genome regions.

My talk will introduce a new genome assembly strategy nicknamed ‘Cornetto’. Under this paradigm, the genome assembly process is coupled to the adaptive sequencing functionality of Oxford Nanopore, with sequencing target regions iteratively updated to focus data production onto the challenging, unsolved regions of a nascent assembly. By enriching sequencing depth where it is most needed during assembly, Cornetto delivers dramatic improvements in assembly quality, at lower cost, both for human individuals and non-human species, including examples of fish, birds, and reptiles tested so far.

Cornetto also enables production of T2T assemblies from challenging sample types like human saliva, for the first time. Finally, I will present examples of targeted assembly of medically relevant loci at the very extremes of the genome, to demonstrate how these capabilities can be used to improve the diagnosis of genomic disease.

My team provides all laboratory and computational methods for Cornetto assembly as a new resource that will streamline and improve the production of T2T assemblies using nanopore data.