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Rosenski, J.

Publications and source records attributed to Rosenski, J..

3 recordsLinked to original sources

The genetic basis for DNA methylation variation across tissues and development

The mechanisms by which genetic variation shapes the epigenome across cell types and developmental stages have remained elusive. Here, we define a unifying developmental framework for DNA methylation programming, grounded in genome-wide methylation and genetic variation data from both mouse and human. In mice, we identify thousands of differentially methylated regions (DMRs) linked to sequence polymorphisms that disrupt transcription factor binding. These DMRs are programmed either during implantation or later in organogenesis, revealing two distinct periods of epigenetic regulation. Extending this logic to humans, we analyze our atlas of over 200 WGBS samples from 39 purified cell types and map 33,574 regions where common SNPs control allele-specific methylation. These include both early-established and cell-type-specific loci, many of which colocalize with eQTLs, enhancers, silencers, and disease-associated variants. Our results uncover a widespread mechanism by which genetic variation influences the regulatory landscape, linking sequence, methylation, and transcription across tissues. This cross-species atlas of sequence-dependent methylation not only clarifies the logic and timing of epigenetic programming, but also provides a foundational resource for deciphering non-coding variants in development, complex disease, and regenerative medicine.

genomics↗

wgbstools: A computational suite for DNA methylation sequencing data representation, visualization, and analysis

Next-generation methylation-aware sequencing of DNA sheds light on the fundamental role of methylation in cellular function in health and disease. These data are commonly represented at a single CpG resolution, while single-molecule fragment-level analysis is often overlooked. Here, we present wgbstools, an extensive computational suite tailored for methylation sequencing data. wgbstools allows fast access and ultra-compact anonymized representation of high-throughput methylome data, obtained through various library preparation and sequencing methods. Additionally, wgbstools contains state-of-the-art algorithms for genomic segmentation, biomarker identification, genetic and epigenetic data integration, and more. wgbstools offers fragment-level analysis and informative visualizations, across multiple genomic regions and samples.

bioinformatics↗

Atlas of imprinted and allele-specific DNA methylation in the human body

Allele-specific DNA methylation, determined genetically or epigenetically, is involved in gene regulation and underlies multiple pathologies. Yet, our knowledge of this phenomenon is partial, and largely limited to blood lineages. Here, we present a comprehensive atlas of allele-specific DNA methylation, using deep whole-genome sequencing across 39 normal human cell types. We identified 325k genomic regions, covering 6% of the genome and containing 11% of all CpG sites, that show a bimodal distribution of methylated and unmethylated molecules. In 34K of these regions, genetic variations at individual alleles segregate with methylation patterns, thus validating allele-specific methylation. We also identified 460 regions showing parentally-imprinted methylation, the majority of which were not previously reported. Surprisingly, sequence-dependent and parent-dependent methylation patterns are often restricted to specific cell types, revealing unappreciated variation in the human allele-specific methylation across the human body. The atlas provides a resource for studying allele-specific methylation and regulatory mechanisms underlying imprinted expression in specific human cell types. HighlightsO_LIA comprehensive atlas of allele-specific methylation in primary human cell types C_LIO_LI325k genomic regions show a bimodal pattern of of hyper- and hypo-methylation of DNA C_LIO_LIAllele-specific methylation at 34k genomic regions C_LIO_LITissue-specific effects at known imprinting control regions (ICRs) C_LIO_LI100s of novel loci exhibiting parentally-imprinted methylation C_LIO_LIParentally-imprinting methylation is often cell-type-specific C_LI

genomics↗