A potent and selective small-molecule degrader of ENL suppresses leukemia progression in vivo (RNA-Seq)
RNA-seq (gene expression) · human · 42 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
- 42 samples: 15 conditions, mostly in triplicateGroups 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
- A potent and selective ENL degrader suppresses oncogenic gene expression and leukemia progressionScience advances · 2024
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 GSE245926_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 PRJNA1030400 and SRA study SRP467593. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 42 samples.
How we read this: 15 replicated groups read from 42 sample titles; they account for 42 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.
- GSM7851294 MV411 DMSO 24h rep1 MV411 DMSO 24h
- GSM7851295 MV411 DMSO 24h rep2 MV411 DMSO 24h
- GSM7851296 MV411 DMSO 24h rep3 MV411 DMSO 24h
- GSM7851297 MV411 DMSO 6h rep1 MV411 DMSO 6h
- GSM7851298 MV411 DMSO 6h rep2 MV411 DMSO 6h
- GSM7851299 MV411 DMSO 6h rep3 MV411 DMSO 6h
- GSM7851300 MV411 MS41N 24h rep1 MV411 MS41N 24h
- GSM7851301 MV411 MS41N 24h rep2 MV411 MS41N 24h
- GSM7851302 MV411 MS41N 24h rep3 MV411 MS41N 24h
- GSM7851303 MV411 MS41N 6h rep1 MV411 MS41N 6h
- GSM7851304 MV411 MS41N 6h rep2 MV411 MS41N 6h
- GSM7851305 MV411 MS41N 6h rep3 MV411 MS41N 6h
- GSM7851306 MV411 MS41 24h rep1 MV411 MS41 24h
- GSM7851307 MV411 MS41 24h rep2 MV411 MS41 24h
- GSM7851308 MV411 MS41 24h rep3 MV411 MS41 24h
- GSM7851309 MV411 MS41 6h rep1 MV411 MS41 6h
- GSM7851310 MV411 MS41 6h rep2 MV411 MS41 6h
- GSM7851311 MV411 MS41 6h rep3 MV411 MS41 6h
- GSM7851312 MV411 PFI6 24h rep1 MV411 PFI6 24h
- GSM7851313 MV411 PFI6 24h rep2 MV411 PFI6 24h
- GSM7851314 MV411 PFI6 24h rep3 MV411 PFI6 24h
- GSM7851315 MV411 PFI6 6h rep1 MV411 PFI6 6h
- GSM7851316 MV411 PFI6 6h rep2 MV411 PFI6 6h
- GSM7851317 MV411 PFI6 6h rep3 MV411 PFI6 6h
- GSM7851318 SEMK2 DMSO rep1 SEMK2 DMSO
- GSM7851319 SEMK2 DMSO rep2 SEMK2 DMSO
- GSM7851320 SEMK2 DMSO rep3 SEMK2 DMSO
- GSM7851321 SEMK2 MS41 rep1 SEMK2 MS41
- GSM7851322 SEMK2 MS41 rep2 SEMK2 MS41
- GSM7851323 SEMK2 MS41 rep3 SEMK2 MS41
- GSM7852643 MV411 Negsg rep1 MV411 Negsg
- GSM7852644 MV411 Negsg rep2 MV411 Negsg
- GSM7852645 MV411 ENLsg1 rep1 MV411 ENLsg1
- GSM7852646 MV411 ENLsg1 rep2 MV411 ENLsg1
- GSM7852647 MV411 ENLsg2 rep1 MV411 ENLsg2
- GSM7852648 MV411 ENLsg2 rep2 MV411 ENLsg2
- GSM8252658 MV411_invivo_Veh_rep1 MV411_invivo_Veh
- GSM8252659 MV411_invivo_Veh_rep2 MV411_invivo_Veh
- GSM8252660 MV411_invivo_Veh_rep3 MV411_invivo_Veh
- GSM8252661 MV411_invivo_MS41_rep1 MV411_invivo_MS41
+ 2 more — browse all 42 samples with per-sample file links →
- GSE328391 Airway immune signatures of protection and disease progression in recent human tuberculosis household contacts [bulk RNA-seq] 125 samples shares progression, rna-seq
- GSE326221 Acquired genetic and cell state changes in IDH-mutant glioma progression [snRNA-seq] 75 samples shares progression, rna-seq
- GSE202379 Single Nuclei RNA-Seq to map progression of non-alcoholic fatty liver disease (NAFLD) in patients II 59 samples shares progression, rna-seq
- GSE339456 Integrated bulk and spatial transcriptomic analysis identifies progression-associated molecular signatures in biopsy-proven hypertensive nephropathy [RNA-seq] 35 samples shares progression, rna-seq
- GSE261713 Linking single cell genomes and transcriptomes at scale to decode breast cancer progression [scRNA-seq] 35 samples shares progression, rna-seq
- GSE285009 Mechanistic Investigation of Five Small-Molecule Inhibitors in the Treatment of Pemphigus Vulgaris via RNA-Seq Analysis 34 samples shares small-molecule, rna-seq
- GSE292130 Longitudinal changes in DNA methylation in IDH-mutant glioma fuel disease progression through altered cell state differentiation [scRNA-seq] 31 samples shares progression, rna-seq
- GSE289499 Uncovering Stage-Specific Biomarkers in Colorectal Cancer: RNA-Seq Analysis of Adenoma and Carcinoma Progression 29 samples shares progression, rna-seq
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.