Macrogenetic atlas of prokaryotes
Yang, C.; Huang, H.; Wang, N.; Didelot, X.; Yang, R.; Cui, Y.; Falush, D.
Show abstract
Macrogenetics investigates patterns and predictors of intraspecific genetic variation across diverse taxa, offering a framework for understanding species diversity and addressing evolutionary hypotheses. Here, we present a macrogenetic atlas of prokaryotes (MAP), integrating genomic data (30 parameters in 12 categories) from 15,235 prokaryotic species and population genetic data (22 parameters in 7 categories) from 786 species with phylogenetic, phenotypic, and ecological information. MAP enables quantitative species characterization, straightforward cross-species comparisons, and easy generation, testing, and refinement of evolutionary hypotheses. For example, our analyses show that long- and short-range genetic linkage capture distinct evolutionary dynamics and independently shape genetic diversity, contradicting neutral theory. Instead, we propose that as within-species diversity increases, epistatic selection becomes an increasingly strong force structuring diversity, exemplified by convergent ecospecies structures in Streptococcus mitis and S. oralis. Overall, MAP represents a widely applicable resource (www.genomap.cn) and offers novel insights into the drivers of macroevolution and the life cycle of prokaryotic species.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Compensatory Relationship between Low Complexity Regions and Gene Paralogy in the Evolution of Prokaryotes 96%
- Microbial population dynamics and evolutionary outcomes under extreme energy-limitation 96%
- Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators and role in key physiological responses 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.