Jörn Walter

This study shows that the carbocyclic antimetabolite cAzadC increases TET2 expression and 5hmdC levels, leading to reactivation of anti-tumor responses in AML models.

ESMM improves epigenomic state annotation by integrating DNA methylation and chromatin marks, enhancing the detection of heterochromatic regions and regulatory element boundaries.

The paper shows TET3 and MECP2 interact to regulate neuronal maturation, with TET3 critical for functional development and MECP2 inhibiting TET3 activity.

The paper benchmarks DNA methylation sequencing workflows against an experimental gold-standard, identifying superior workflows and developing a sustainable benchmarking platform.

The paper identifies synthetic dosage lethality targets for PLK1-overexpressing cancers using genome-wide screens and validates key hits to show their therapeutic potential.

EpiSegMix improves chromatin state segmentation by using a flexible hidden Markov model with duration modeling, outperforming existing tools in predicting gene expression.

The paper investigates age-related changes in endothelial cells of mice, identifying gene and chromatin alterations linked to increased intracerebral hemorrhage risk.

The paper shows that chromatin accessibility influences cell-type specific intron retention and confirms intrinsic intron features affect splicing.

GwEEP quantifies DNA modifying enzyme efficiencies using HPoxBS data, revealing dynamic changes in methylation and hydroxylation during cellular reprogramming and development.

The paper introduces MAGAR, a framework to identify methylation quantitative trait loci (methQTLs) and distinguish common from cell-type-specific effects using DNA methylation and genotyping data.

This study performs a meta-analysis of DNA methylation data from Alzheimer's disease patients to identify differentially methylated loci across brain regions and cortex.

This paper introduces a reference-free deconvolution protocol for DNA methylomes to identify hidden cell populations in complex samples, applied to lung cancer data from TCGA.

CRUP predicts condition-specific regulatory units by analyzing histone modification ChIP-seq data and linking enhancers to target genes in rheumatoid arthritis.

The paper compares DNase I-Seq, ATAC-Seq, and NOMe-Seq to identify assay-specific features and their impact on chromatin accessibility and gene expression prediction.

The paper identifies DNA methylation differences in monozygotic twins discordant for multiple sclerosis, highlighting treatment effects and genetic confounders.

The paper integrates single cell RNA-seq with bulk epigenomic data to identify distinct regulatory states in bidirectional promoters and their associations with structural features and TF binding.

The paper identifies partially methylated domains (PMDs) as widespread epigenomic features across human and mouse cells, showing cell-type specificity and links to gene expression, replication timing, and proliferation.

normR normalizes ChIP-seq data to improve enrichment calling in different histone modification regimes.

This paper introduces TEPIC, a method that combines open-chromatin data with TF binding affinities to predict gene expression, achieving results comparable to TF ChIP-seq.