Jason Ernst

This study examines epigenomic and spatial changes during human reprogramming, finding distinct molecular and spatial patterns in productive versus unsuccessful trajectories.

MissenseHMM annotates missense variants with states derived from joint modeling of pathogenicity scores, improving associations with clinical and experimental data.

GECSI predicts chromatin states from gene expression data, outperforming existing methods and providing annotations for 449 additional epigenomes.

map3C improves the quality of multiomic single-cell Hi-C data and aids in identifying structural variant locations.

This study creates a 3D multi-omic atlas of developing human basal ganglia and inhibitory neurons, revealing lineage-specific 3D genomic dynamics and regulatory programs associated with neuropsychiatric disease risk.

The paper develops LEPAE, a method to quantify pairwise associations between genomic loci using epigenomic and TF binding data, showing it captures biologically meaningful relationships.

The paper uses long-read sequencing to profile native 5hmC and 5mC in human neurons, showing 5hmC enrichment in transcribed regions and enhancers.

The paper uses a stacked chromatin state model to identify global patterns of histone modifications across individuals and applies them to predict transregulators and identify trans-QTL in disease contexts.

The paper identifies associations of de novo noncoding variants with autism by integrating gene expression, sequence, and sex information using a new analytical framework.

The paper develops CMImpute, a method to impute DNA methylation data for unprofiled species-tissue combinations, achieving strong correlation with observed values and imputing 19,786 new combinations.

The paper infers the fitness effects of noncoding mutations in humans using genomic annotations and polymorphism data, finding distinct selection patterns across different noncoding regions.

ChromActivity predicts and annotates regulatory activity across the genome using epigenomic data and functional datasets, generating cell-type-specific annotations and activity tracks.

The paper evaluates demultiplexing methods for joint snRNA/snATAC experiments under ambient contamination and introduces a new metric to assess their performance.

The paper shows that alternative splicing of DPF2, regulated by PTBP1, affects chromatin organization and transcriptional regulation during stem cell maintenance and neuronal differentiation.

The paper identifies causal genes at QTLs associated with fear-related behavior using complementation testing and finds six genes, including four with known synaptic roles and one long non-coding RNA.

ChromGene annotates genes based on epigenomic patterns across gene bodies and flanking regions, generating annotations for over 100 human cell and tissue types.

The paper develops a universal chromatin state annotation for mouse by integrating data from 901 datasets across 26 cell types, comparing it to human annotations.

This study examines DNA methylation changes in human chondrocytes across ontogeny and develops an epigenetic clock showing STAT3 agonist reduces epigenetic age in aged chondrocytes.

CSREP summarizes chromatin state annotations within sample groups and identifies differential annotations across groups using probabilistic modeling and logistic regression.

The paper develops a mammalian methylation array to profile methylation levels at conserved sequences across species, enabling cross-species comparisons and epigenetic biomarker development.

The paper develops a universal chromatin state annotation of the human genome by integrating over 1000 epigenomic datasets across 100 cell types using an enhanced ChromHMM model.

The paper develops DNA methylation-based age estimators for vervet monkeys and shows evolutionary conservation of epigenetic aging in primates.

This paper develops epigenetic clocks for rhesus macaques using methylation data from multiple tissues, showing high cross-species correlation with vervet monkeys.

The paper annotates SARS-CoV-2 genome bases with conservation states using CoV alignments to identify mutation-enriched regions.

The paper develops a method to identify conserved functional genomic regions between human and mouse using integrated epigenomic and transcriptomic data.

The paper develops CNEP to predict constrained non-exonic bases using epigenomic and transcription factor data, and introduces CSS-CNEP to improve prediction for underperforming bases.

The paper extends the ConsHMM method to annotate conservation states for multiple genomes and human genetic variation, providing a web interface for visualization.

This study develops pan-primate epigenetic clocks and sex predictors using DNA methylation data from 37 primate species, achieving high accuracy for age estimation across species.

The paper develops {chi}-SCNN to predict high-resolution sources of Hi-C interactions using ChIP-seq, DNase-seq, and histone mark data, showing enrichment for conserved bases and eQTLs.

The paper introduces ConsHMM, a method to annotate the human genome into conservation states using multivariate hidden Markov models, showing these states provide more predictive information than standard constraint measures.