Yuval Ebenstein

The paper compares True-5mC methylation entropy analysis using nanopore sequencing to bisulfite sequencing in kidney cancer and mouse brain, showing bisulfite introduces systematic biases affecting biological interpretation.

This paper develops a mutation-independent liquid biopsy method using DNA methylation to monitor high-risk neuroblastoma through blood plasma-derived cfDNA.

This paper introduces a 'write-and-read' framework using synthetic DNA modifications to enable nanopore sequencing to detect chemically altered nucleotides and expand the sequence alphabet.

The paper uses long-read and single nucleus RNA-seq to identify novel SNCA transcript isoforms and their association with Parkinson's and Lewy body dementia.

This paper introduces a spectral CSD-ISM system for high-resolution, multi-color imaging with improved speed and resolution.

The paper uses optical genome and epigenome mapping to identify somatic alterations in early-stage clear cell renal cell carcinoma, revealing both known and novel genomic and epigenomic changes.

The paper compares optical genome mapping and nanopore sequencing for structural and epigenetic analysis of a kidney tumor, highlighting their strengths in variant detection and epigenetic profiling.

This paper develops a validated protocol for using Oxford Nanopore methylation sequencing on FFPE samples to classify brain tumors, enabling point-of-care diagnosis.

OM2Seq improves OGM data analysis by using a Transformer-encoder to rapidly and accurately map DNA fragments to a reference genome, outperforming existing methods in speed and accuracy.

This paper develops an information theory-based model to design optimal labeling patterns for optical genome mapping, improving accuracy by up to 10-fold in human and bacterial DNA.

DeepOM uses deep learning to improve alignment of single-molecule optical genome mapping images to genomic references, showing better performance for shorter DNA molecules.

The paper introduces a method to simultaneously analyze 5-methylcytosine and 5-hydroxymethylcytosine in clinical samples, showing reduced 5-hydroxymethylcytosine in colorectal cancer and chronic lymphocytic leukaemia.

The paper uses machine learning to identify age-related switch points in DNA methylation trends, revealing three sex-dependent waves of epigenetic aging.

This paper develops a chemoenzymatic method to label non-methylated CpGs in DNA, enabling single-molecule epigenetic profiling in human and plant genomes.

The paper deconvolves epigenetic age to identify CpG sites responsible for accelerated and decelerated aging, revealing inter-personal variability in aging patterns.

The paper demonstrates that a hybrid HMM-DNN approach outperforms both pure HMM and DNN methods for 5mC calling with incomplete training data.

The paper develops a method to deconvolve cell-type mixtures using enhancer-promoter methylation profiles from long-read data, achieving higher accuracy than promoter-based approaches.

The paper introduces RADD-Seq, a method to map DNA damage and repair sites genome-wide, showing oxidative damage distribution and repair prioritization in human cells.

This paper introduces a long-read optical mapping technology to profile 5-hydroxymethylcytosine (5-hmC) across the genome, revealing long-range epigenetic correlations and cell-to-cell variation.

R2OM2 creates fluorescent optical methylation profiles along chromosomal DNA for epigenetic profiling, comparable to WGBS with single-molecule resolution.

The paper introduces R2OM2, a method combining optical methylation profiling with genome mapping to analyze epigenetic variation in single cells and large structural aberrations.

The paper introduces a method to isolate and enrich large genomic regions for targeted analysis using Cas9 excision and pulsed field gel electrophoresis, enabling cost-effective detection of mutations and structural variants.