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BadgerSeq- a decentralized model for ultra-rapid, long-read whole-genome sequencing, Stephen Meyn

Abstract Rapid genome sequencing (rGS) is transforming the care of critically ill newborns due to its comprehensive nature, actionable insights, and cost-effectiveness. Yet, as currently implemented, rGS is not widely available, misses clinically important variants, and is too slow. BadgerSeq could address these issues by replacing centralized short-read, next-generation sequencing with local nanopore long-read sequencing and by using AI to identify infants likely to benefit from rGS and to accelerate tertiary variant analysis. To potentially generate and interpret a patient’s genome in three days from clinical presentation, we developed a workflow for on-demand nanopore trio genome sequencing that uses two PromethION Flow Cells in parallel for each genome, with basecalling and alignment performed ‘on the fly’. Variants are assessed using Fabric Genomics software, with final identification of causal variants carried out in consultation with patient’s clinicians. In this talk, we will discuss our initial development of the BadgerSeq sequencing workflow. Biography Dr. Meyn directs the Center for Human Genomics and Precision Medicine at the University of Wisconsin. Trained in pediatrics and medical genetics, Dr. Meyn has held faculty positions at Yale and the University of Toronto, where he co-led the Hospital for Sick Children’s Genome Clinic Project, which pioneered the use of diagnostic and predictive genome sequencing in children. His current research focuses on applying novel technologies to discovering disease genes and determining the role of structural genomic variants in human disease.