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Utility of long-read sequencing for All of Us.
Mahmoud, M; Huang, Y; Garimella, K; Audano, P A; Wan, W; Prasad, N; Handsaker, R E; Hall, S; Pionzio, A; Schatz, M C; Talkowski, M E; Eichler, E E; Levy, S E; Sedlazeck, F J.
Affiliation
  • Mahmoud M; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
  • Huang Y; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
  • Garimella K; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
  • Audano PA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
  • Wan W; The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
  • Prasad N; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
  • Handsaker RE; Discovery Life Sciences, Huntsville, AL, 35806, USA.
  • Hall S; Department of Genetics, Harvard Medical School, Boston, MA, USA.
  • Pionzio A; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
  • Schatz MC; Discovery Life Sciences, Huntsville, AL, 35806, USA.
  • Talkowski ME; Discovery Life Sciences, Huntsville, AL, 35806, USA.
  • Eichler EE; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Levy SE; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
  • Sedlazeck FJ; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Nat Commun ; 15(1): 837, 2024 Jan 29.
Article in En | MEDLINE | ID: mdl-38281971
ABSTRACT
The All of Us (AoU) initiative aims to sequence the genomes of over one million Americans from diverse ethnic backgrounds to improve personalized medical care. In a recent technical pilot, we compare the performance of traditional short-read sequencing with long-read sequencing in a small cohort of samples from the HapMap project and two AoU control samples representing eight datasets. Our analysis reveals substantial differences in the ability of these technologies to accurately sequence complex medically relevant genes, particularly in terms of gene coverage and pathogenic variant identification. We also consider the advantages and challenges of using low coverage sequencing to increase sample numbers in large cohort analysis. Our results show that HiFi reads produce the most accurate results for both small and large variants. Further, we present a cloud-based pipeline to optimize SNV, indel and SV calling at scale for long-reads analysis. These results lead to widespread improvements across AoU.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: High-Throughput Nucleotide Sequencing / Population Health Type of study: Observational_studies Limits: Humans Language: En Journal: Nat Commun Journal subject: BIOLOGIA / CIENCIA Year: 2024 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: High-Throughput Nucleotide Sequencing / Population Health Type of study: Observational_studies Limits: Humans Language: En Journal: Nat Commun Journal subject: BIOLOGIA / CIENCIA Year: 2024 Document type: Article Affiliation country: Estados Unidos