John A. Capra

The paper identifies human-specific regulatory variants associated with traits distinguishing modern humans from archaic hominins by analyzing TFBS gains and losses in cCREs.

EXCAVATE-HT identifies targetable genomic variants for allele-specific CRISPR editing by analyzing population data and generating gRNA libraries.

VUStruct analyzes genetic variants of unknown significance by integrating 3D protein structure data and machine learning to improve clinical and research interpretation of rare genetic disorders.

This paper identifies therapeutic opportunities for over 500 genes with dominant and dispensable disease alleles by enabling mutation-agnostic gene therapy through allele-specific targeting.

The paper compares Neanderthal introgression maps from 12 algorithms, finding core agreement but substantial heterogeneity, and recommends using multiple maps for robust conclusions.

This paper demonstrates haplotype editing with CRISPR/Cas9 to target NEFL missense mutations in iPSC-derived motor neurons, showing therapeutic potential and challenges in disrupting mutant allele expression.

This study uses tensor decomposition on EHR data to identify latent subtypes of preterm birth and links them to genetic risk factors for comorbid diseases.

This study uses machine learning to assess how 3D genome structure affects height by analyzing GWAS data and predicting variant impacts on chromatin contacts.

The paper creates a catalog of proxy variants linked to ancient genotyped variants to improve the study of genetic variants in ancient DNA.

This study introduces novel methods for comparing chromatin contact maps and evaluates their performance using Hi-C data and in silico maps, highlighting the need for biologically informed approaches.

The paper develops a method to identify deleterious noncoding variants by analyzing changes in CTCF binding activity across genomic loci and finds that loss of CTCF binding is associated with rare variants.

The paper uses deep mutational scanning to analyze MET kinase domain mutations and their resistance to 11 inhibitors, identifying resistance and sensitizing mutations and improving prediction with a protein language model.

The paper evaluates methylation clocks in genetically diverse populations and finds they are less accurate in admixed cohorts and fail to consistently predict age acceleration in Alzheimer's disease cases.

The paper uses sequence-based machine learning to predict 3D genome contact maps in bonobos and chimpanzees, identifying divergent folding patterns and variants affecting genome structure.

The paper uses machine learning to infer 3D chromatin contact patterns across diverse human populations and identifies regions with significant 3D divergence not explained by sequence variation.

The paper develops a disease-specific variant prioritization method that improves the association of non-coding variants with diseases by aggregating tissue/cell-type specific scores.

The paper identifies cis and trans regulatory element divergence between humans and rhesus macaques using ATAC-STARR-seq, finding that most divergent elements show changes in both mechanisms.

The paper identifies cell-specific transposable element (TE) gene expression associations with SLE phenotypes, finding 731 differentially expressed TEs linked to antiviral pathways and IFN signaling.

The paper uses machine learning to analyze Neanderthal and Denisovan genomes, identifying splicing variants and their potential impact on phenotypic differences.

This study maps the functional effects of thousands of KCNE1 variants using high-throughput methods, identifying loss-of-function and loss-of-surface expression variants and showing concordance with electrophysiological and computational data.

This study identifies genetic loci associated with gestational duration and preterm delivery, showing maternal and fetal genetic effects and antagonistic pleiotropy with birth weight.

The paper shows that nucleoporins, specifically Elys, are required for ORC2 loading onto chromatin and proper replication initiation in Drosophila.

The paper uses personalized structural biology to analyze de novo KCNC2 variants in a DEE patient, revealing distinct functional effects of two nearby variants.

This study conducts a genome-wide association study to identify genetic variants linked to musical beat synchronization, finding high polygenicity with 69 significant loci and heritability estimates of 13%-16%.

The paper maps and characterizes R-loops during Drosophila embryogenesis, showing developmental plasticity in their abundance and localization without changes in transcriptional program.

POKEMON improves statistical power for detecting associations between rare missense variants and phenotypes by analyzing their spatial distribution on protein structures.

The paper uses ancient DNA and machine learning to study gene regulation changes in human populations and their link to environmental adaptation.

This paper introduces DiGePred, a machine learning tool that identifies potential digenic disease gene pairs, achieving high precision and recall in evaluating rare genetic disorders.

The paper introduces COSMIS, a framework to quantify 3D spatial constraint on amino acid sites in the human proteome, showing it outperforms existing metrics and links constraint to disease relevance.