John P. Barton

Sequence-structured fitness landscapes predict influenza evolution

This study develops a generative framework to infer influenza virus fitness landscapes from sequence data, revealing antigenic determinants and improving evolutionary predictions.

Evolutionary regimes determine the accuracy of epistasis inference from temporal genetic data

This study evaluates epistasis inference methods using temporal genetic data across different evolutionary regimes, finding that accuracy depends on selection, recombination, and genetic drift.

Dual Role of Glycosylation in Resistance to CD4-binding Site Broadly Neutralizing Antibodies

The paper shows that glycan N276 at Env affects resistance to VRC01-class bNAbs, with its impact varying based on Env, bNAb, and producer cell glycosylation.

Linkage-aware inference of fitness from short-read time-series genomic data

This paper introduces a method to infer fitness effects from short-read time-series genomic data while accounting for genetic linkage, validated on HIV and SARS-CoV-2 datasets.

Chronic infections can generate SARS-CoV-2-like bursts of viral evolution without epistasis

This study shows chronic infections can generate SARS-CoV-2-like mutation bursts without epistasis, increasing global evolutionary rates.

Parallel HIV-1 fitness landscapes shape viral dynamics in humans and macaques that develop broadly neutralizing antibodies.

The paper quantifies HIV-1 evolution in humans and macaques, showing similar fitness effects of mutations and a pattern of rapid fitness gains preceding bnAb development.

Clonal heterogeneity and antigenic stimulation shape persistence of the latent reservoir of HIV

This study develops a quantitative model to show that antigenic stimulation and clonal heterogeneity shape the persistence of the HIV-1 latent reservoir.

Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities

This paper develops a method to predict viral sensitivity to antibodies using genetic sequences and antibody similarities, showing it outperforms existing approaches and can predict neutralization values for unseen viruses.

Back-projection improves inference from sparsely sampled genomic surveillance data

This study introduces a back-projection model to improve inference from sparse genomic surveillance data by estimating infection times and accounting for sampling biases in SARS-CoV-2 variants.

Efficient epistasis inference via higher-order covariance matrix factorization

This paper introduces a method to efficiently infer epistasis from temporal genetic data, finding strong negative epistasis between beneficial mutations in HIV-1 evolution.

Inferring fitness seascapes from evolutionary histories

The paper infers time-varying selection pressures from genetic data using a statistical physics approach, applied to study HIV-1 immune escape dynamics.

A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses

The paper develops a binary trait model to separate the fitness effects of HIV-1 immune escape from replication, finding that T cell escape contributes significantly to early fitness gains.

popDMS infers mutation effects from deep mutational scanning data

popDMS infers mutation effects from DMS data by applying population genetics theory, showing consistency across replicates and outperforming existing methods.

A quantitative model for the regulation of innate immune activation

The paper introduces a quantitative model of innate immune activation regulation using Bayesian inference, revealing tradeoffs between precision and adaptation.