Michael Scherer

This study shows that loss of the Y chromosome in lung adenocarcinoma causes epigenetic and transcriptomic changes that increase lineage plasticity and poor clinical outcomes.

This study identifies epigenetic changes in AT2 cells from COPD patients that disrupt IFN and WNT pathways, impairing lung regeneration.

This study integrates spatial and multi-omic data from bladder cancer patients to show L1 retrotransposition drives genomic instability, ecDNA formation, and a viral mimicry response.

This paper introduces EPI-clone, a method using DNA methylation to trace clonal lineages in hematopoiesis without genetic engineering, showing clonal complexity declines with age in mice and expands in humans.

Pyjacker detects enhancer hijacking in AML, identifying MNX1 activation via 7q deletion as a novel oncogenic mechanism.

The paper introduces epiCHAOS, a metric to quantify cell-to-cell epigenomic heterogeneity in single-cell data and shows its utility in assessing stemness and plasticity.

The paper identifies del(5q) AML as an epigenetically distinct subgroup driven by KDM3B haploinsufficiency, linked to DNA hypermethylation and H3K9me1/2 dysregulation.

Figeno generates publication-quality genomic figures with support for multi-region views, long reads, and epigenomic data.

scTAM-seq provides high-confidence single-cell DNA methylation analysis with high throughput, resolving B-cell differentiation dynamics and integrating with protein and mutation data.

DECONbench benchmarks deconvolution methods for quantifying tumor cell-type heterogeneity by providing datasets, tools, and performance evaluation.

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 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.

This study evaluates DNA methylation deconvolution methods for cell-type proportion inference, identifies critical steps for accuracy, and develops a benchmark pipeline.