Epigenomic profiles of SLE resting and transitional B cells
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 150 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
- 150 samples: 150 samples, each with a distinct title — no replicated groupsGroups 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.
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 GSE263717_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 PRJNA1098766 and SRA study SRP498757. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 150 sample titles: 150 distinct titles with little repetition. 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.
- GSM8180322 SLE.0.3621.rN
- GSM8180323 SLE.0.3651.T1T2
- GSM8180324 SLE.0.1597.rN
- GSM8180325 SLE.0.1597.T1T2
- GSM8180326 SLE.0.1738.rN
- GSM8180327 SLE.0.1738.T1T2
- GSM8180328 SLE.2711.871.rN
- GSM8180329 SLE.2711.871.T1T2
- GSM8180330 SLE.3202.2491.rN
- GSM8180331 SLE.3202.2491.T1T2
- GSM8180332 SLE.3039.3553.rN
- GSM8180333 HCD.918.1215.rN
- GSM8180334 HCD.918.1215.T1T2
- GSM8180335 HCD.646.1234.rN
- GSM8180336 HCD.646.1234.T1T2
- GSM8180337 HCD.796.1235.rN
- GSM8180338 HCD.796.1235.T1T2
- GSM8180339 HCD.1171.1296.rN
- GSM8180340 HCD.1171.1296.T1T2
- GSM8180341 SLE.2913.559.rN
- GSM8180342 SLE.2913.559.T1T2
- GSM8180343 SLE.2911.2209.rN
- GSM8180344 SLE.2911.2209.T1T2
- GSM8180345 SLE.2932.3240.rN
- GSM8180346 SLE.2932.3240.T1T2
- GSM8180347 SLE.2891.420.rN
- GSM8180348 SLE.2891.420.T1T2
- GSM8180349 SLE.2880.3039.rN
- GSM8180350 SLE.2880.3039.T1T2
- GSM8180351 SLE.2900.3328.rN
- GSM8180352 SLE.2900.3328.T1T2
- GSM8180353 SLE.2898.3314.rN
- GSM8180354 SLE.2898.3314.T1T2
- GSM8180355 SLE.2893.428.rN
- GSM8180356 HCD.875.1610.rN
- GSM8180357 HCD.875.1610.T1T2
- GSM8180358 HCD_1059_309_rN
- GSM8180359 HCD_1059_309_T1T2
- GSM8180360 HCD_1176_1842_rN
- GSM8180361 HCD_1176_1842_T1T2
+ 110 more — browse all 150 samples with per-sample file links →
- GSE262846 Transcriptomic Analysis of SLE Disease Activity in Resting Naive (rN) and Transitional (T1T2) B cells 88 samples shares sle, transitional, resting
- GSE263716 Epigenetic Analysis of SLE Disease Activity in Resting Naive (rN) and Transitional (T1T2) B cells 62 samples shares sle, transitional, resting
- GSE275610 scRNA-seq and snATAC-seq reveal differentiation trajectories of several pancreatic islet induction protocols 341 samples shares sle
- GSE335464 Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals [single cell] 28 samples shares sle
- GSE270484 Patchseq Data of T1D Donor Islets vs ND Matched Controls 958 samples shares sle
- GSE319130 Multi-omic analysis reveals molecular subtypes with distinct immunological phenotypes in SLE [RNA-Seq] 804 samples shares sle
- GSE232381 RNA-seq of Peripheral Blood Cells Identifies Molecular Network Linked to Lymphocyte Subset Aberrations and Active Lupus Nephritis in SLE 25 samples shares sle
- GSE298578 Single cell RNA profiling of blood CD4+ T cells identifies distinct helper and dysfunctional regulatory clusters in children with SLE (5' PBMC validation dataset) 14 samples shares sle
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.