Five years ago, Chuck Gawad, MD, PhD, and Jay West, PhD, founded BioSkryb Genomics with a goal to focus on single-cell analysis through multiomics. Because, West told GEN, next-generation sequencing is great for detecting germline differences, but it is not great at detecting somatic variation between cells.
Now BioSkryb announced that it has joined the NIST Genome Editing Consortium to help address the standards needed to increase confidence of using genome editing technologies in research and commercial products.
“Whether genome editing will be used in healthcare, agriculture or basic research, robust quantitative measurements and standards are necessary to enable high confidence characterization of DNA alterations,” said Samantha Maragh, PhD, leader of the Genome Editing Program at NIST. “We are pleased to have innovative partners on board to apply their technology and help improve our overall understanding and confidence in detecting and quantifying on-and off-target genome editing.”
BioSkryb’s ResolveDNA® and ResolveOME™technologies will be used to facilitate characterization of genome editing on a cell-by-cell basis, to help define genome editing and reporting standards, generate benchmark data, and improve the understanding of on-and off-target effects.
ResolveDNA reproduces genomes of single cells allowing for analysis of single-cell genomic heterogeneity. ResolveOME generates single-cell multiomics datasets by combining whole genome or exome analysis with whole transcriptome analysis within each individual cell.
Industry experts are still working to understand how often genome editing alters the genome of each cell outside of the targeted edit and how these off-target changes affect the state and health of these cells. NIST has brought together experts across the genome editing field including stakeholders in industry, academia, and government to address these open questions around necessary measurements and standards. The goal of the consortium is to establish greater confidence in the characterization of genome editing outputs through the evaluation of genome editing assay pipelines, generation of benchmark data, and more.