Chaitanya Srinivasan

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

This paper identifies cell-type-specific genetic variants associated with chronic pain using GWAS and scRNA-seq data from brain and dorsal root ganglia tissues.

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.

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.

The paper characterizes striatal projection neuron heterogeneity in mice using single-nuclei multi-omics and spatial mapping, revealing distinct subtypes with unique receptor expression and disease risk contributions.

TOGA integrates gene annotation and orthology inference across hundreds of genomes, improving ortholog detection and creating large comparative gene resources.

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

The paper investigates molecular rhythm alterations in the prefrontal cortex and nucleus accumbens of individuals with opioid use disorder, finding distinct rhythmic transcript patterns and associations with sleep and neurotransmitter systems.

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

This study identifies transcriptional changes in opioid use disorder brains, linking neuroinflammation and synaptic remodeling through RNA-seq and integrative analyses.