QuantSeq from Nutlin-3a-treated MCF-7, U2OS, and RPE-1 cells
RNA-seq (gene expression) · human · 20 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
- 20 samples: 20 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.
- Paper
- Gene regulation by convergent promotersNature genetics · 2025
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 GSE223513_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 PRJNA926620 and SRA study SRP418721. 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 20 sample titles: 20 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.
- GSM6957842 MCF-7_DMSO_1_QuantSeq
- GSM6957843 MCF-7_DMSO_2_QuantSeq
- GSM6957844 MCF-7_DMSO_3_QuantSeq
- GSM6957845 MCF-7_DMSO_4_QuantSeq
- GSM6957846 MCF-7_Nutlin-3a_1_QuantSeq
- GSM6957847 MCF-7_Nutlin-3a_2_QuantSeq
- GSM6957848 MCF-7_Nutlin-3a_3_QuantSeq
- GSM6957849 MCF-7_Nutlin-3a_4_QuantSeq
- GSM6957850 RPE-1_DMSO_1_QuantSeq
- GSM6957851 RPE-1_DMSO_2_QuantSeq
- GSM6957852 RPE-1_DMSO_3_QuantSeq
- GSM6957853 RPE-1_Nutlin-3a_1_QuantSeq
- GSM6957854 RPE-1_Nutlin-3a_2_QuantSeq
- GSM6957855 RPE-1_Nutlin-3a_3_QuantSeq
- GSM6957856 U2OS_DMSO_1_QuantSeq
- GSM6957857 U2OS_DMSO_2_QuantSeq
- GSM6957858 U2OS_DMSO_3_QuantSeq
- GSM6957859 U2OS_Nutlin-3a_1_QuantSeq
- GSM6957860 U2OS_Nutlin-3a_2_QuantSeq
- GSM6957861 U2OS_Nutlin-3a_3_QuantSeq
- GSE223512 CAGE-seq from Nutlin-3a-treated MCF-7, U2OS, and RPE-1 cells 20 samples shares mcf-7, rpe-1, u2os, nutlin-3a-treated
- GSE250017 ATAC-seq from Nutlin-3a-treated MCF-7 cells 8 samples shares mcf-7, nutlin-3a-treated
- GSE297342 Assessing the Effects of Silver Nanoparticles on ARPE-19 Cells via High-Throughput Phenotypic Profiling with the Cell Painting Assay. 88 samples shares rpe-1
- GSE290998 RNA-sequencing of HCMV-infected ARPE-19 and MCF10A cells 48 samples shares rpe-1
- GSE280432 RNA sequencing of non-targetting ASOs on HEK293 and U2OS cells 36 samples shares u2os
- GSE224502 RNA-Seq profiles of pancreatic adenocarcinoma PANC-1 and osteosarcoma U2OS cells with Pin1 knocking down 18 samples shares u2os
- GSE264653 Effect of MITF depletion on gene expression in U2OS cells 18 samples shares u2os
- GSE241306 Effect of TOP1 knockdown and/or RNase H OE on the transcriptional profile of MCF-7 cells [CEL_Seq] 16 samples shares mcf-7
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.