William S. DeWitt

Across 119 monoclonal germinal centers, B cell affinity rises reliably despite divergent phylogenies. Selection is noisy but persistent; mutability limits exploration. A DMS-based landscape shows saturating fitness and survivorship biases.

The paper uses simulation-based deep learning to infer the affinity-fitness response function in germinal centers from experimental data.

The paper shows that MULTI-evolve's multimutant predictions correlate closely with additive models, suggesting its performance is not due to epistasis learning.

This paper introduces POLEGON, a method for estimating branch lengths in Ancestral Recombination Graphs using uninformative priors, showing improved coalescence time estimates and population size inferences.

The paper estimates mutation rates across SARS-CoV-2 genome sites and identifies factors influencing these rates, finding significant variability and patterns related to gene boundaries, sequence context, and RNA structure.

The paper uses allelic inclusion data to train models that identify antibody sequence constraints, finding stronger correlations with antibody properties than existing methods.

The paper uses deep mutational scanning to analyze CYP2C19 variants, revealing a tradeoff between substrate specificity and protein abundance.

The paper develops a method to identify mutations with shifted effects across DMS experiments on SARS-CoV-2 spike homologs, finding some mutations show >1,000-fold differences in impact across variants.

mutyper assigns mutation types to SNP data using ancestral genome estimates and computes mutation spectra for individuals and populations.

The paper assesses SNVs within human segmental duplications, finding increased mutation rates and interlocus gene conversion, along with distinct mutational spectra.

This paper introduces a statistical model and method for estimating cell lineage trees using maximum-likelihood phylogenetics, showing improved accuracy over existing methods in simulations and zebrafish development.

The paper identifies a shared T-cell receptor motif specific for a mycobacterial lipid antigen, associated with tuberculosis and independent of host genetic background.