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Pangenome-based genome inference

View ORCID ProfileJana Ebler, View ORCID ProfileWayne E. Clarke, View ORCID ProfileTobias Rausch, View ORCID ProfilePeter A. Audano, View ORCID ProfileTorsten Houwaart, View ORCID ProfileJan Korbel, View ORCID ProfileEvan E. Eichler, Michael C. Zody, View ORCID ProfileAlexander T. Dilthey, View ORCID ProfileTobias Marschall
doi: https://doi.org/10.1101/2020.11.11.378133
Jana Ebler
1Institute for Medical Biometry and Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Wayne E. Clarke
2New York Genome Center, New York, New York, USA
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Tobias Rausch
3European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
4European Molecular Biology Laboratory (EMBL), GeneCore, Heidelberg, Germany
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  • For correspondence: tobias.marschall@hhu.de
Peter A. Audano
5Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA
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Torsten Houwaart
7Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Jan Korbel
3European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
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Evan E. Eichler
5Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA
6Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA
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Michael C. Zody
2New York Genome Center, New York, New York, USA
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Alexander T. Dilthey
7Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Tobias Marschall
1Institute for Medical Biometry and Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Abstract

Typical analysis workflows map reads to a reference genome in order to detect genetic variants. Generating such alignments introduces references biases, in particular against insertion alleles absent in the reference and comes with substantial computational burden. In contrast, recent k-mer-based genotyping methods are fast, but struggle in repetitive or duplicated regions of the genome. We propose a novel algorithm, called PanGenie, that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation. The given haplotypes enable our method to take advantage of linkage information to aid genotyping in regions poorly covered by unique k-mers and provides access to regions otherwise inaccessible by short reads. Compared to classic mapping-based approaches, our approach is more than 4× faster at 30× coverage and at the same time, reached significantly better genotype concordances for almost all variant types and coverages tested. Improvements are especially pronounced for large insertions (> 50bp), where we are able to genotype > 99.9% of all tested variants with over 90% accuracy at 30× short-read coverage, where the best competing tools either typed less than 60% of variants or reached accuracies below 70%. PanGenie now enables the inclusion of this commonly neglected variant type in downstream analyses.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 12, 2020.
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Pangenome-based genome inference
Jana Ebler, Wayne E. Clarke, Tobias Rausch, Peter A. Audano, Torsten Houwaart, Jan Korbel, Evan E. Eichler, Michael C. Zody, Alexander T. Dilthey, Tobias Marschall
bioRxiv 2020.11.11.378133; doi: https://doi.org/10.1101/2020.11.11.378133
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Pangenome-based genome inference
Jana Ebler, Wayne E. Clarke, Tobias Rausch, Peter A. Audano, Torsten Houwaart, Jan Korbel, Evan E. Eichler, Michael C. Zody, Alexander T. Dilthey, Tobias Marschall
bioRxiv 2020.11.11.378133; doi: https://doi.org/10.1101/2020.11.11.378133

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