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guidedNOMe-seq quantifies chromatin states at single allele resolution for hundreds of custom regions in parallel [ChIP-Seq]

GSE249662 Mus musculus Genome binding/occupancy profiling by high throughput sequencing 6 samples Submitted 2024/07/10 Platform GPL17021
Summary
Since the introduction of next generation sequencing technologies, the field of epigenomics has evolved rapidly. However, most commonly used assays are enrichment-based methods and thus only semi-quantitative. Nucleosome occupancy and methylome sequencing (NOMe-seq) allows for quantitative inference of chromatin states with single locus resolution, but this requires high sequencing depth and is therefore prohibitively expensive to routinely apply to organisms with large genomes. To overcome this limitation, we introduce guidedNOMe-seq, where we combine NOMe profiling with large scale sgRNA synthesis and Cas9-mediated region-of-interest (ROI) enrichment. To facilitate quantitative comparisons between multiple samples, we additionally develop an R package to standardize differential analysis of NOMe-seq data. We extensively benchmark guidedNOMe-seq in a proof-of-concept study, dissecting the interplay of ChAHP and CTCF on chromatin. In summary we introduce a cost-effective, scalable, and customizable targeted enrichment extension to the existing NOMe-seq protocol allowing genome-scale quantification of nucleosome occupancy and transcription factor binding at single allele resolution.
Published in
guidedNOMe-seq quantifies chromatin states at single allele resolution for hundreds of custom regions in parallel
Schwaiger M, Mohn F, Bühler M et al. · BMC genomics 2024 · PMID 39075377 · doi:10.1186/s12864-024-10625-3
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Direct links to NCBI, no account and no request form: the whole study as GSE249662_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 6 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1050142 and SRA study SRP476581. Searching any of these in the dataset finder brings you back here.

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