Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes

Typical genotyping workflows map reads to a reference genome before identifying genetic variants. Generating such alignments introduces reference biases and comes with substantial computational burden. Furthermore, short-read lengths limit the ability to characterize repetitive genomic regions, which are particularly challenging for fast k-mer-based genotypers. In the present study, we propose a new algorithm, PanGenie, that leverages a haplotype-resolved pangenome reference together with k-mer counts from short-read sequencing data to genotype a wide spectrum of genetic variation-a process we refer to as genome inference. Compared with mapping-based approaches, PanGenie is more than 4 times faster at 30-fold…

• BF
  Bundesministerium für Bildung und Forschung

  Awards: 031A537C, 031A533A, 031A535A, 031A537A, 031A538A, 031A537D, 031L0181A, 031L0184, 031A534A, 031A532B, 031A533B, 031A537B
• HD
  Heinrich-Heine-Universität Düsseldorf
• GN
  German Network for Bioinformatics Infrastructure

  Awards: 031A533A, 031A534A, 031A538A, 031A537D, 031A537A, 031L0181A, 031A535A, 031A532B, 031A537B, 031A533B, 031A537C
• NI
  National Institutes of Health

  Awards: 5U24HG007497, 1U01HG010973, HG010169, R01HG002385
• NH
  National Human Genome Research Institute

  Awards: HG010169, 1U01HG010973