Soumya Kundu

The paper characterizes atherosclerosis trajectories in vascular smooth muscle cells using single-cell data and identifies causal transcriptomic and epigenomic mechanisms of CAD risk.

The paper studies how dietary metabolites interact with genetic variants to influence colon cancer risk through transcriptional regulation.

The paper identifies causal genetic mechanisms at CAD GWAS loci using CRISPR screens and chromatin analysis, revealing enhancer-gene links and ancestry/sex-specific risk factors.

The paper identifies CXCL12 as a genetic driver of coronary artery anatomy variation through GWAS and mouse models, showing its role in fetal coronary development.

The paper predicts the regulatory effects of non-coding variants across brain and heart cellular contexts using deep learning, identifying rare variants with significant developmental impacts.

Learning and removing Tn5/DNase-I sequence bias lets a small base-resolution CNN (ChromBPNet) recover TF motifs and footprints from accessibility data and rival Enformer at predicting chromatin QTL effects.

This study creates a gene regulation map of the human fetal heart to interpret genetic variants associated with congenital heart disease and cardiac traits, identifying valvular interstitial cells as key regulators.

This paper links CAD GWAS variants to causal genes in SMCs using single-cell chromatin accessibility and Hi-C data, creating a scV2E2G map for disease mechanism discovery.

This paper introduces a consensus variant-to-function score to prioritize disease-causing variants, showing improved performance in heritability enrichment and fine-mapping.

The paper identifies novel transcripts, eQTLs, and caQTLs in African populations and fine-maps GWAS signals for complex diseases.

This study uses single-cell chromatin accessibility analysis to identify developmental trajectories and non-coding mutations associated with congenital heart disease.

The paper uses single-cell ATAC-seq to analyze coronary artery cells from CAD patients, identifying regulatory mechanisms and risk variants associated with disease.

The paper identifies vascular site-specific epigenomic and transcriptomic features and links them to disease risk loci using single-cell data and GWAS integration.

This paper develops a method to generate neuronal cells from adult fibroblasts and uses it to study gene expression and epigenetic changes in 22q11.2 deletion syndrome.

The paper uses single-cell epigenomic data to identify functional SNPs associated with Alzheimer's and Parkinson's disease, revealing novel gene-disease associations and regulatory mechanisms.

This paper introduces a phylogenetic reconciliation framework to model both additive and replacing horizontal gene transfers, evaluates its impact on traditional methods, and proposes a heuristic algorithm for DTRL reconciliation.