Morgan E. Wirthlin

This study trains CNNs to predict chromatin accessibility across species but finds poor performance in predicting quantitative differences between species.

The paper develops a cross-species enhancer-AAV toolkit for targeting specific cell types in the basal ganglia across species, validated in mouse and macaque.

The paper analyzes multi-modal Patch-seq data from macaque striatum to reveal morphoelectric diversity and functional specialization of neuronal cell types, highlighting primate-specific features and differences from rodents.

The paper creates a cross-species spinal cord cell type atlas integrating data from mouse, macaque, and human, identifying conserved and species-specific features.

The paper creates a cross-species consensus atlas of primate basal ganglia cell types using multiomic data from human, macaque, and marmoset, identifying 61 conserved cell types and a novel hybrid medium spiny neuron type.

The paper presents an enhancer AAV toolbox for targeting and perturbing striatal cell types and circuits in mice, with validated specificity and cross-species applicability.

The paper introduces WhAMPRA, an in vivo MPRA platform in mice, to assess tissue-specific regulatory functions of genetic variants and enhancers.

The paper identifies proteins and regulatory elements associated with vocal learning by analyzing bat neural activity and epigenomic data.

A machine learning approach identifies enhancer-phenotype associations across mammals, revealing links to brain size and behavior evolution.

A 240-species placental mammal alignment resolves constraint to single bases, estimating at least 10.7% of the human genome is constrained, with 48.5% of constrained bases unannotated.

The paper identifies convergent gene expression patterns between songbird and human vocal motor circuits, highlighting shared molecular specializations in specific brain regions.

The paper introduces SNAIL, a virus-based method using machine learning to design cell type-specific enhancer probes for targeted nuclear isolation and characterization.

The paper introduces a machine learning method to predict tissue-specific regulatory conservation by analyzing open chromatin patterns across mammals, showing improved performance over nucleotide alignment-based approaches.

The paper identifies addiction-associated genetic variants in brain cell type- and region-specific cis-regulatory elements using GWAS and machine learning.

The paper compares open chromatin regions in motor cortex subdivisions across primates and rodents to identify gene regulatory programs involved in the evolution of fine motor skills.

This study identifies molecularly distinct medium spiny neuron types in the primate striatum, revealing new subtypes associated with specific anatomical compartments and functional roles.