Irene M. Kaplow

The paper identifies extensive interactions among C2H2 zinc finger proteins at chromatin loop anchors using ChIP-seq, interactome data, and chromatin interaction maps.

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

This study compares cis-regulatory elements in immortalized liver cell lines to primary liver tissue using ATAC-seq, finding significant differences in transcription factor binding and gene regulation.

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

The paper identifies a gene regulatory element that modulates MYH6 expression and influences cardiomyocyte response to stress.

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

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

The paper uses CNN regression models to predict the impact of AD-associated variants on open chromatin in different cell types, finding greater effects in peripheral cells than in microglia or neurons.

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

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 curates TE content in 248 mammalian genomes, finding trends in recent TE accumulation and associations with dietary habits.

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 uses evolutionary constraint scores to identify functional genomic regions and finds they are enriched for variants associated with common diseases.

The paper identifies bats as a hotspot for horizontal transfer of DNA transposons, finding over 200 putative elements and shared transposons across mammals and reptiles.

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 uses a deep learning model to analyze ChIP-seq data and identifies binding motifs for CTCF zinc fingers 1-2, revealing GAG and ATT motifs.

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.

The paper identifies off-target effects in CRISPR-Cas9 screens for non-coding elements and shows that strict filtering with specificity scores improves the identification of essential regulatory regions.

This study fine-maps cis-regulatory variants across diverse human populations by analyzing chromatin accessibility QTLs and their effects on gene expression and long-range interactions.