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happi: a Hierarchical Approach to Pangenomics Inference

Pauline Trinh, David S. Clausen, View ORCID ProfileAmy D. Willis
doi: https://doi.org/10.1101/2022.04.26.489591
Pauline Trinh
1Department of Environmental & Occupational Health Sciences, University of Washington
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David S. Clausen
2Department of Biostatistics, University of Washington
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Amy D. Willis
2Department of Biostatistics, University of Washington
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Abstract

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality, and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs and via simulation.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/statdivlab/happi

  • https://github.com/statdivlab/happi_supplementary

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 4.0 International license.
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Posted April 28, 2022.
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happi: a Hierarchical Approach to Pangenomics Inference
Pauline Trinh, David S. Clausen, Amy D. Willis
bioRxiv 2022.04.26.489591; doi: https://doi.org/10.1101/2022.04.26.489591
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happi: a Hierarchical Approach to Pangenomics Inference
Pauline Trinh, David S. Clausen, Amy D. Willis
bioRxiv 2022.04.26.489591; doi: https://doi.org/10.1101/2022.04.26.489591

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