Genome-wide maps of enhancer regulation connect risk variants to disease genes
RNA-seq (gene expression) · human · 16 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 16 samples: 8 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
- Genome-wide enhancer maps link risk variants to disease genesNature · 2021
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 GSE285157_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1201317 and SRA study SRP553101. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 16 samples.
How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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.
- GSM8697447 THP-1_dCas9-KRAB_ctrl1 THP-1_dCas9-KRAB_ctrl
- GSM8697448 THP-1_dCas9-KRAB_ctrl2 THP-1_dCas9-KRAB_ctrl
- GSM8697449 THP-1_dCas9-KRAB_stim1 THP-1_dCas9-KRAB_stim
- GSM8697450 THP-1_dCas9-KRAB_stim2 THP-1_dCas9-KRAB_stim
- GSM8697451 U937_ctrl1 U937_ctrl
- GSM8697452 U937_ctrl2 U937_ctrl
- GSM8697453 U937_stim1 U937_stim
- GSM8697454 U937_stim2 U937_stim
- GSM8697455 Jurkat_ctrl1 Jurkat_ctrl
- GSM8697456 Jurkat_ctrl2 Jurkat_ctrl
- GSM8697457 Jurkat_stim1 Jurkat_stim
- GSM8697458 Jurkat_stim2 Jurkat_stim
- GSM8697459 BJAB_ctrl1 BJAB_ctrl
- GSM8697460 BJAB_ctrl2 BJAB_ctrl
- GSM8697461 BJAB_stim1 BJAB_stim
- GSM8697462 BJAB_stim2 BJAB_stim
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- GSE126044 Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer 16 samples shares genome-wide, enhancer
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- GSE142751 Genome-wide maps of chromatin state in 142 cancer cell lines [cell line] 855 samples shares genome-wide, maps
- GSE288172 Influence of NIPBL Knockdown-Induced Alterations in Cohesin Loop Extrusion on CTCF Loops, Enhancer-Promoter Connectivity, and PRC Domain Contacts During hESC Differentiation into Pancreatic Lineages 263 samples shares enhancer, connect
- GSE263804 Chromatin accessibility mapping for functional GWAS risk variants of neuropsychiatric and neurodegenerative disorders in hiPSC-derived neural cells 141 samples shares variants, risk
- GSE309187 Poising and connectivity of emergent human developmental enhancers in the transition from naive to primed pluripotency 26 samples shares enhancer, connect
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.