Yuval Dor

This paper detects neuronal DNA in cfDNA using non-CpG methylation patterns, finding elevated levels in neurological conditions and brain-dead donors.

The paper identifies average polycomb CpG methylation as a hallmark of cellular aging using single-cell whole-genome methylation data, showing cell-to-cell variability and accelerated aging in faster proliferating cells.

The paper identifies genetic variants influencing DNA methylation across tissues and development in mice and humans, revealing two distinct epigenetic programming periods and linking methylation to regulatory elements and disease variants.

The paper identifies a systematic over-conversion error in EM-seq that introduces false-positive methylation signals in cfDNA analysis.

The paper shows that DNA methylation changes at clustered CpG sites encode chronological age with high accuracy using deep learning on blood samples.

The paper creates a comprehensive atlas of allele-specific DNA methylation across 39 human cell types, identifying 325k genomic regions and 460 novel imprinted loci with cell-type-specific patterns.

The paper introduces GP-age, a DNA methylation-based age prediction model using 30 CpG sites and a cohort-based machine learning approach, achieving high accuracy on both array and sequencing data.

The paper estimates the fraction of DNA from dying cells that becomes detectable in plasma, finding less than 10% of DNA from dying cells is detectable, with ratios varying widely among cell types.

The paper constructs a human DNA methylation atlas using whole-genome bisulfite sequencing of 39 cell types, revealing cell type-specific methylation patterns and regulatory elements.

The paper identifies megakaryocytes and erythroblasts as major contributors to circulating cell-free DNA (cfDNA) and shows their DNA can serve as biomarkers for blood cell production disorders.

This study demonstrates the ability to detect cell-of-origin and cancer-specific methylation features from cell-free DNA using Oxford Nanopore sequencing.

The paper uses immune cell type-specific methylation patterns in circulating cell-free DNA to monitor immune dynamics and find that immune-derived cfDNA levels reflect immune homeostasis and predict immune responses to perturbations.

The paper introduces cfChIP-seq to analyze cell-free nucleosomes and shows it can detect disease-related transcriptional changes and patient-specific features in cancer.

The paper develops a method to determine the tissue origins of circulating cell-free DNA using a methylation atlas and validates it in healthy and disease states.

The paper identifies methylation patterns specific to intestinal epithelial cells to detect cell-free DNA in plasma, showing increased levels in advanced CRC compared to IBD and PDAC.