Hidden but Expanding Sampling Bias Distorts Prokaryotic Genome Collections
- 1Justus-Liebig-University Gießen
Abstract
Public genome collections underpin our understanding of bacterial diversity and the distribution of traits such as antibiotic resistance. Their growing size promises an increasingly complete view of microbial genetic variation. However, these collections combine studies with different sampling priorities, often overrepresenting subgroups of closely related strains of particular interest. The resulting bias reduces effective information, and its consequences for diversity estimates and the value of further sequencing remain insufficiently quantified. Here we show that sampling bias in prokaryotic genome collections is widespread, escalating in recent years, and distorts gene frequency estimates and the representation of genomic diversity. We propose to detect and reduce sampling bias based on phylogenetic relationships compared to expectations under random sampling, which, in contrast to dereplication and data reduction methods, accounts for sample size. Applied to 402 prokaryotic species in the NCBI genome database, our method PhyloThin indicates that the effective number of samples is 41% lower than the actual number. Oversampling varies substantially among species, showing that genome counts alone provide an incomplete measure of how well a species has already been characterized. Accounting for oversampling reveals greater genomic diversity and more genes awaiting discovery than current data suggest and improves gene frequency estimates with implications for pathogen surveillance and the discovery of novel genetic functions.
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BibTeX
@misc{gotsch2026hidden,
title = {{Hidden but Expanding Sampling Bias Distorts Prokaryotic Genome Collections}},
author = {H. Götsch and F. Baumdicker},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.755372},
url = {https://www.biorxiv.org/content/10.64898/2026.09.29.755372v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Hidden but Expanding Sampling Bias Distorts Prokaryotic Genome Collections
AU - H. Götsch
AU - F. Baumdicker
PY - 2026
DA - 2026/09/29
PB - bioRxiv
DO - 10.64898/2026.09.29.755372
UR - https://www.biorxiv.org/content/10.64898/2026.09.29.755372v1
AB - Public genome collections underpin our understanding of bacterial diversity and the distribution of traits such as antibiotic resistance. Their growing size promises an increasingly complete view of microbial genetic variation. However, these collections combine studies with different sampling priorities, often overrepresenting subgroups of closely related strains of particular interest. The resulting bias reduces effective information, and its consequences for diversity estimates and the value of further sequencing remain insufficiently quantified. Here we show that sampling bias in prokaryotic genome collections is widespread, escalating in recent years, and distorts gene frequency estimates and the representation of genomic diversity. We propose to detect and reduce sampling bias based on phylogenetic relationships compared to expectations under random sampling, which, in contrast to dereplication and data reduction methods, accounts for sample size. Applied to 402 prokaryotic species in the NCBI genome database, our method PhyloThin indicates that the effective number of samples is 41% lower than the actual number. Oversampling varies substantially among species, showing that genome counts alone provide an incomplete measure of how well a species has already been characterized. Accounting for oversampling reveals greater genomic diversity and more genes awaiting discovery than current data suggest and improves gene frequency estimates with implications for pathogen surveillance and the discovery of novel genetic functions.
ER -