Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe

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.

Automated summary

Horizontal gene transfer (HGT) is important for the evolution and diversification of microbial species. Existing methods for analyzing gene presence/absence patterns do not consider errors in annotation and clustering. The new method Panstripe, based on generalized linear regression, can effectively identify differences in HGT events by accounting for population structure and errors in gene prediction.