Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe
- Gerry Tonkin-Hill1,2,
- Rebecca A. Gladstone1,
- Anna K. Pöntinen1,
- Sergio Arredondo-Alonso1,2,
- Stephen D. Bentley2 and
- Jukka Corander1,2,3
- 1Department of Biostatistics, University of Oslo, 0372 Blindern, Norway;
- 2Parasites and Microbes, Wellcome Sanger Institute, Cambridge CB10 1RQ, United Kingdom;
- 3Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
Abstract
Horizontal gene transfer (HGT) plays a critical role in the evolution and diversification of many microbial species. The resulting dynamics of gene gain and loss can have important implications for the development of antibiotic resistance and the design of vaccine and drug interventions. Methods for the analysis of gene presence/absence patterns typically do not account for errors introduced in the automated annotation and clustering of gene sequences. In particular, methods adapted from ecological studies, including the pangenome gene accumulation curve, can be misleading as they may reflect the underlying diversity in the temporal sampling of genomes rather than a difference in the dynamics of HGT. Here, we introduce Panstripe, a method based on generalized linear regression that is robust to population structure, sampling bias, and errors in the predicted presence/absence of genes. We show using simulations that Panstripe can effectively identify differences in the rate and number of genes involved in HGT events, and illustrate its capability by analyzing several diverse bacterial genome data sets representing major human pathogens.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.277340.122.
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Freely available online through the Genome Research Open Access option.
- Received September 18, 2022.
- Accepted December 14, 2022.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
