ArticleMolecular biology and evolution2020
Predicting the Landscape of Recombination Using Deep Learning.
Article in Molecular biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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100 citing papers in PubMed.
- Diversity and divergence of two sympatric, sibling octopus species.Molecular biology and evolution · 2026Article
- High‑resolution recombination landscape in Iranian native chicken ecotypes: Elevated hotspot density and candidate genes for heat and drought adaptation.Poultry science · 2026Article
- Adaptation across an extreme elevational gradient in Andean leaf-eared mice, the world's highest-dwelling mammal.Science (New York, N.Y.) · 2026Article
- Neural posterior estimation for population genetics.Genetics · 2026Article
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.PLoS computational biology · 2026Article
- Mutation rate estimate and population genomic analysis reveals decline of koalas prior to human arrival.Molecular biology and evolution · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- The persistence and loss of hard selective sweeps amid admixture in ancient Eurasians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Influence of geography, seasonality and experimental selection on Chironomus riparius recombination rates.BMC genomics · 2026Article
- Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Comparative Genomics Reveals the Ancestral Recombination Landscape of Placental Mammals.bioRxiv : the preprint server for biology · 2026Article
- Soft Selective Sweeps Predominate in the Yellow Fever Mosquito Aedes aegypti.Molecular biology and evolution · 2026Article
- Recombination landscape shaped by inversion polymorphisms: a high-density linkage map and chromosome-level assembly of inversion-rich spruce bark beetle genome.G3 (Bethesda, Md.) · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.bioRxiv : the preprint server for biology · 2026Article
- Article
- Multispecies pangenomes reveal a pervasive influence of population size on structural variation.Science (New York, N.Y.) · 2025Article
- The molecular evolutionary basis of species formation revisited.Trends in genetics : TIG · 2025Review
- Mixed Outcomes in Recombination Rates After Domestication: Revisiting Theory and Data.Molecular ecology · 2025Review
- Unraveling Plant Recombination Patterns: Insights From Genome k-mers.Plant direct · 2025Article
40 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recombination landscape estimation using recurrent neural networks (ReLERNN), a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, although largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.
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