Ashley R. Brown

A molecular and spinal circuit basis for the functional segregation of itch and pain

The paper identifies functionally distinct subtypes within Grpr+ neurons in the dorsal horn that mediate itch and mechanical allodynia, demonstrating the utility of genomic enhancer-based strategies for modality-specific targeting.

Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomics

The paper introduces CTACIT to fine-map neuropsychiatric regulatory variants by integrating conservation scores with cell type-specific open chromatin data across species.

Challenges in predicting chromatin accessibility differences between species

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

Context-dependent regulatory variants in Alzheimer's disease

The paper identifies context-dependent regulatory variants in Alzheimer's disease by analyzing their effects across different cellular contexts using MPRAs and machine learning.

Combining Machine Learning and Multiplexed, In Situ Profiling to Engineer Cell Type and Behavioral Specificity

The paper develops a platform combining machine learning and multiplexed in situ screening to identify enhancers with cell-type and behavioral specificity for neural circuit engineering.

Cell Type Specific Enhancers for Dorsolateral Prefrontal Cortex.

The paper identifies cell type-specific enhancers for the DLPFC in NHPs using single nucleus RNA-Seq and ATAC-Seq, validates them with AAV injections, and demonstrates functional expression in specific neuron types.

An in vivo massively parallel platform for deciphering tissue-specific regulatory function

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

Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements

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

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

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

Evolutionary constraint and innovation across hundreds of placental mammals

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.

Machine learning sequence prioritization for cell type-specific enhancer design

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

Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin

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.

Addiction-associated genetic variants implicate brain cell type- and region-specific cis-regulatory elements in addiction neurobiology

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

The Regulatory Evolution of the Primate Fine-Motor System

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.