Mesoscale chromatin confinement facilitates target search of pioneer transcription factors in live cells [RNA-seq]
RNA-seq (gene expression) · human · 11 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
- 11 samples: 11 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.
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 GSE203640_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 PRJNA841659 and SRA study SRP376714. 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 11 sample titles: 11 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.
- GSM6181133 WT_ES
- GSM6181134 WT_APS
- GSM6181135 WT_DE
- GSM6181136 KO1_APS
- GSM6181137 KO1_DE
- GSM6181138 KO21_APS
- GSM6181139 KO21_DE
- GSM6181140 dC11_APS
- GSM6181141 dC11_DE
- GSM6181142 dCdN10_APS
- GSM6181143 dCdN10_DE
- GSE269826 Conserved patterns of transcriptional dysregulation, heterogeneity and cell states in clear cell kidney cancer [scRNA-seq] 79 samples shares transcription, rna-seq
- GSE189357 Spatiotemporal transcriptional atlas of lung adenocarcinoma from adenocarcinoma in situ to invasive carcinoma [scRNA-seq] 9 samples shares transcription, rna-seq
- GSE196619 Synthetic transcription factor overrides persistent repressive chromatin marks 120 samples shares transcription, chromatin
- GSE296192 Hypoxic regulation of chromatin and gene transcription 98 samples shares transcription, chromatin
- GSE302930 Epigenetic Atlas of Bladder Cancer Reveals Master Transcription Factors and Risk-Associated Regulatory Elements in Luminal and Basal-Squamous Molecular Subtypes 92 samples shares transcription, factors
- GSE301298 Functional redundancy between UTY and UTX in regulating the localization of transcription factors involved in pluripotency 88 samples shares transcription, factors
- GSE296190 Hypoxic regulation of chromatin and gene transcription [ChIP-seq] 84 samples shares transcription, chromatin
- GSE275721 Lineage master regulator and cancer-selective partner transcription factors rewire 3D genome topology for tumor-specific gene control 72 samples shares transcription, factors
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.