Accurate, scalable and integrative haplotype estimation
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Updated: Jan 3, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
Published on: December 7, 2021
Olivier Delaneau1,2, Jean-François Zagury3, Matthew R Robinson4,5
1Department of Computational Biology, University of Lausanne, Génopode, 1015, Lausanne, Switzerland. olivier.delaneau@unil.ch.
Abstract:
The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.
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