A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [ChIP-seq]
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 16 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
- 16 samples: 7 conditions, mostly in duplicateGroups 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
- BRAIN-MAGNET: A functional genomics atlas for interpretation of non-coding variantsCell · 2026
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE263337_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1096727 and SRA study SRP500037. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 14 samples.
How we read this: 7 replicated groups read from 16 sample titles; they account for 14 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.
- GSM8189773 NSC, H3K27ac, ChIP, rep1 NSC, H3K27ac, ChIP,
- GSM8189774 NSC, H3K27ac, ChIP, rep2 NSC, H3K27ac, ChIP,
- GSM8189775 NSC, H3K4me1, ChIP, rep1 NSC, H3K4me1, ChIP,
- GSM8189776 NSC, H3K4me1, ChIP, rep2 NSC, H3K4me1, ChIP,
- GSM8189777 NSC, SOX2, ChIP, rep1 NSC, SOX2, ChIP,
- GSM8189778 NSC, SOX2, ChIP, rep2 NSC, SOX2, ChIP,
- GSM8189779 NSC, YY1, ChIP, rep1 NSC, YY1, ChIP,
- GSM8189780 NSC, YY1, ChIP, rep2 NSC, YY1, ChIP,
- GSM8189781 NSC, input, ChIP, rep1 NSC, input, ChIP,
- GSM8189782 NSC, input, ChIP, rep2 NSC, input, ChIP,
- GSM8189783 ESC, SOX2, ChIP, rep1 ESC, SOX2, ChIP,
- GSM8189784 ESC, SOX2, ChIP, rep2 ESC, SOX2, ChIP,
- GSM8189785 ESC, input, SOX2
- GSM8189786 ESC, YY1, ChIP, rep1 ESC, YY1, ChIP,
- GSM8189787 ESC, YY1, ChIP, rep2 ESC, YY1, ChIP,
- GSM8189788 ESC, input, YY1
- GSE263335 A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [STARR-RNA-seq] 16 samples shares interpretation, convolutional, facilitates, non-coding, regulatory, enhanced, networks, clinical
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- GSE302468 Long non-coding RNA triplex-dependent regulation of melanoma gene networks 24 samples shares non-coding, networks
- GSE275368 A ONECUT1 regulatory, non-coding region in pancreatic development and diabetes [RNA-seq] 24 samples shares non-coding, regulatory
- GSE251726 JMJD1C facilitates long distance genomic interactions by promoting condensates formation of key leukemic transcription factors in multiple AML cells [ChIP-seq] 18 samples shares facilitates
- GSE203644 Mesoscale chromatin confinement facilitates target search of pioneer transcription factors in live cells [ChIP-seq] 17 samples shares facilitates
- GSE274484 The Long Non-coding RNA Lnc-RAINY Regulates Genes Involved in Radiation Susceptibility Through DNA:DNA:RNA triplex-forming Interactions and has Tumor Therapeutic Potential in Non-small Cell Lung Cancers [ATAC-seq] 16 samples shares non-coding
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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.