Role of the SAF-A SAP domain in X inactivation, transcription, splicing, and cell proliferation [RNA-seq]
RNA-seq (gene expression) · human · 28 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
- 28 samples: 12 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
- Role of the SAF-A/HNRNPU SAP domain in X chromosome inactivation, nuclear dynamics, transcription, splicing, and cell proliferationPLoS genetics · 2025
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 GSE277221_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 PRJNA1161384 and SRA study SRP532797. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 28 samples.
How we read this: 12 replicated groups read from 28 sample titles; they account for 28 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.
- GSM8516652 RPE1_rep1 RPE1
- GSM8516653 RPE1_rep2 RPE1
- GSM8516654 RPE1_rep3 RPE1
- GSM8516655 RPE1_rep4 RPE1
- GSM8516656 RPE1, 24h_rep1 RPE1, 24h
- GSM8516657 RPE1, 24h_rep2 RPE1, 24h
- GSM8516658 RPE1, 48h_rep1 RPE1, 48h
- GSM8516659 RPE1, 48h_rep2 RPE1, 48h
- GSM8516660 RPE1, 72h_rep1 RPE1, 72h
- GSM8516661 RPE1, 72h_rep2 RPE1, 72h
- GSM8516662 SAFA depletion_rep1 SAFA depletion
- GSM8516663 SAFA depletion_rep2 SAFA depletion
- GSM8516664 SAFA depletion_rep3 SAFA depletion
- GSM8516665 SAFA depletion_rep4 SAFA depletion
- GSM8516666 SAFA depletion, 24h_rep1 SAFA depletion, 24h
- GSM8516667 SAFA depletion, 24h_rep2 SAFA depletion, 24h
- GSM8516668 SAFA depletion, 48h_rep1 SAFA depletion, 48h
- GSM8516669 SAFA depletion, 48h_rep2 SAFA depletion, 48h
- GSM8516670 SAFA depletion, 72h_rep1 SAFA depletion, 72h
- GSM8516671 SAFA depletion, 72h_rep2 SAFA depletion, 72h
- GSM8516672 SAFA depletion, SAFA-AA-GFP_rep1 SAFA depletion, SAFA-AA…
- GSM8516673 SAFA depletion, SAFA-AA-GFP_rep2 SAFA depletion, SAFA-AA…
- GSM8516674 SAFA depletion, SAFA-DD-GFP_rep1 SAFA depletion, SAFA-DD…
- GSM8516675 SAFA depletion, SAFA-DD-GFP_rep2 SAFA depletion, SAFA-DD…
- GSM8516676 SAFA depletion, SAFA-SAP-GFP_rep1 SAFA depletion, SAFA-SA…
- GSM8516677 SAFA depletion, SAFA-SAP-GFP_rep2 SAFA depletion, SAFA-SA…
- GSM8516678 SAFA depletion, SAFA-WT-GFP_rep1 SAFA depletion, SAFA-WT…
- GSM8516679 SAFA depletion, SAFA-WT-GFP_rep2 SAFA depletion, SAFA-WT…
- GSE319253 Eicosapentaenoic Acid Reprograms Cerebrovascular Metabolism and Impairs Repair after Brain Injury, with Relevance to Chronic Traumatic Encephalopathy [human_brain_OnurE_RNA-seq] 24 samples shares sap, rna-seq
- GSE269826 Conserved patterns of transcriptional dysregulation, heterogeneity and cell states in clear cell kidney cancer [scRNA-seq] 79 samples shares transcription, rna-seq
- GSE310664 Analysis of experimental colorectal liver metastases identifies distinct proliferation independent transcriptional programs in micro- versus macrometastases 22 samples shares transcription, proliferation
- GSE189357 Spatiotemporal transcriptional atlas of lung adenocarcinoma from adenocarcinoma in situ to invasive carcinoma [scRNA-seq] 9 samples shares transcription, rna-seq
- GSE318861 Quantitative analysis of Tat-dependent and host-driven HIV transcription by ChIP-seq and RNA-seq 52 samples shares transcription, rna-seq
- GSE307817 A MEF2C transcription factor network regulates proliferation of glomerular endothelial cells in diabetic kidney disease 12 samples shares transcription, proliferation
- GSE234552 Tracking of donor and recipient immune and leukemia phenotypes after allogeneic stem cell transplantation using mitochondrial DNA mutations. [ASAP-Seq 2] 30 samples shares sap
- GSE234555 Tracking of donor and recipient immune and leukemia phenotypes after allogeneic stem cell transplantation using mitochondrial DNA mutations. [ASAP-Seq 3] 14 samples shares sap
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.