Comprehensive mapping of long-range interactions reveals folding principles of the human genome.
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 8 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
- 8 samples: 2 × Hi-C (HindIII,GM,2) vs 2 × Hi-C (NcoI,GM,1) vs 2 × Hi-C (HindIII,K562,1)Groups 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.
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 GSE18199_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 PRJNA119555 and SRA study SRP001261. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 6 samples.
How we read this: 3 replicated groups read from 8 sample titles; they account for 6 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.
- GSM455133 Hi-C (HindIII,GM,1)
- GSM455134 Hi-C (HindIII,GM,Biological Repeat)
- GSM455135 Hi-C (HindIII,GM,2) 2 Hi-C (HindIII,GM,2)
- GSM455136 Hi-C (HindIII,GM,2) 3 Hi-C (HindIII,GM,2)
- GSM455137 Hi-C (NcoI,GM,1) 1 Hi-C (NcoI,GM,1)
- GSM455138 Hi-C (NcoI,GM,1) 2 Hi-C (NcoI,GM,1)
- GSM455139 Hi-C (HindIII,K562,1) 7 Hi-C (HindIII,K562,1)
- GSM455140 Hi-C (HindIII,K562,1) 8 Hi-C (HindIII,K562,1)
- GSE279180 Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions [snRNA-seq] 16 samples shares interactions, mapping
- GSE317257 Mapping Endothelial-Macrophage Interactions in Diabetic Vasculature: Role of TREM2 in Vascular Inflammation and Ischemic Respons 35 samples shares interactions, mapping
- GSE293428 Mapping Endothelial-Macrophage Interactions in Diabetic Vasculature: Role of TREM2 in Vascular Inflammation and Ischemic Response [scRNA-Seq] 18 samples shares interactions, mapping
- GSE296495 Single-cell mapping of regulatory DNA:Protein interactions 14 samples shares interactions, mapping
- GSE318564 Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions [scRNA-seq] 13 samples shares interactions, mapping
- GSE102093 Direct DNA-RNA interactions dynamically decorate core functional elements within the human genome 40 samples shares interactions
- GSE308298 Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning 33 samples shares mapping
- GSE276856 Selective interactions at pre-replication complexes categorize baseline and dormant origins 211 samples shares interactions
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.