← BioTransfer GEO Dataset Finder
GEO series

Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning

GSE308298 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 33 samples Submitted 2026/03/25 Platform GPL20795Platform GPL24676Platform GPL21697
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 33 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
33 samples: 12 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learningNature communications · 2026
Summary (written by the submitters)
Most genetic variants in the human genome reside in non-coding regions, where they can perturb regulatory elements, influence gene expression, and contribute to various phenotypes and diseases. However, identifying such functionally relevant genetic variation remains challenging. Here we integrate personal genomics, allele-specific gene regulation, and deep learning predictions to map the impact of non-coding variation in its native allelic and regulatory context.
Read the full summary ↓
We identify and validate hundreds of cell-type-specific transcription factor binding events disrupted by genetic variants, providing mechanistic insights underlying allele-specific gene regulation. Using this framework, we discover a rare variant that modulates PIK3R5 gene expression by disrupting an OCT2 binding site within a distal enhancer. Our study establishes a generalisable strategy for interpreting non-coding regulatory variation, enabling systematic dissection of variant effects across diverse biological systems and offering an alternative framework to investigate the disease mechanisms.
Published in
Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning
Magnitov MD, van der Weide RH, Witvliet AF et al. · Nature communications 2026 · PMID 42056104 · doi:10.1038/s41467-026-72392-x
This dataset
Open GSE308298 in finder → Find more human ChIP / ATAC / CUT&Tag datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE308298_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1330152 and SRA study SRP621079. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42056104) ↗
Study design
12 conditions, mostly in triplicate
10X_LinkedReads_HG ×3 HiC_HG ×3 ATACseq_NA12878 ×3 ATACseq_NA18983 ×3 ATACseq_HG01241 ×3 ATACseq_HG02601 ×3 ATACseq_HG03464 ×3 TTseq_NA12878 ×2 +6 more

Supports a between-group comparison across 31 samples.

How we read this: 12 replicated groups read from 33 sample titles; they account for 31 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.