The novel RNA polymerase I transcription inhibitor PMR-116 exploits a critical therapeutic vulnerability in a broad-spectrum of high MYC malignancies [ChIP-Seq]
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 45 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
- 45 samples: 10 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.
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 GSE302076_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 PRJNA1288469 and SRA study SRP599194. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 30 samples.
How we read this: 10 replicated groups read from 45 sample titles; they account for 30 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.
- GSM9095383 input 1 n1
- GSM9095384 input 2 n1
- GSM9095385 input 3 n1
- GSM9095386 input 4 n1
- GSM9095387 input 5 n1
- GSM9095388 input 6 n2
- GSM9095389 input 7 n2
- GSM9095390 input 8 n2
- GSM9095391 input 9 n2
- GSM9095392 input 10 n2
- GSM9095393 input 11 n3
- GSM9095394 input 12 n3
- GSM9095395 input 13 n3
- GSM9095396 input 14 n3
- GSM9095397 input 15 n3
- GSM9095398 UBF vehicle n=1 UBF vehicle n=
- GSM9095399 UBF 1uM PMR n=1 UBF 1uM PMR n=
- GSM9095400 UBF 10uM PMR n=1 UBF 10uM PMR n=
- GSM9095401 UBF 1uM CX n=1 UBF 1uM CX n=
- GSM9095402 UBF 10uM CX n=1 UBF 10uM CX n=
- GSM9095403 UBF vehicle n=2 UBF vehicle n=
- GSM9095404 UBF 1uM PMR n=2 UBF 1uM PMR n=
- GSM9095405 UBF 10uM PMR n=2 UBF 10uM PMR n=
- GSM9095406 UBF 1uM CX n=2 UBF 1uM CX n=
- GSM9095407 UBF 10uM CX n=2 UBF 10uM CX n=
- GSM9095408 UBF vehicle n=3 UBF vehicle n=
- GSM9095409 UBF 1uM PMR n=3 UBF 1uM PMR n=
- GSM9095410 UBF 10uM PMR n=3 UBF 10uM PMR n=
- GSM9095413 UBF 1uM CX n=3 UBF 1uM CX n=
- GSM9095419 UBF 10uM CX n=3 UBF 10uM CX n=
- GSM9095424 E43 Pol vehicle n=1 E43 Pol vehicle n=
- GSM9095428 E43 Pol 1uM PMR n=1 E43 Pol 1uM PMR n=
- GSM9095433 E43 Pol 10uM PMR n=1 E43 Pol 10uM PMR n=
- GSM9095437 E43 Pol 1uM CX n=1 E43 Pol 1uM CX n=
- GSM9095441 E43 Pol 10uM CX n=1 E43 Pol 10uM CX n=
- GSM9095442 E43 Pol vehicle n=2 E43 Pol vehicle n=
- GSM9095443 E43 Pol 1uM PMR n=2 E43 Pol 1uM PMR n=
- GSM9095444 E43 Pol 10uM PMR n=2 E43 Pol 10uM PMR n=
- GSM9095445 E43 Pol 1uM CX n=2 E43 Pol 1uM CX n=
- GSM9095446 E43 Pol 10uM CX n=2 E43 Pol 10uM CX n=
+ 5 more — browse all 45 samples with per-sample file links →
- GSE267470 Dual targeting of processive transcription for Myc-driven circuitry in medulloblastoma [CUT&RUN] 12 samples shares myc, transcription
- GSE280280 Reprogramming of Cellular Plasticity via ETS and MYC Core-regulatory circuits During Response to MAPK Inhibition in BRAF-mutant colorectal cancer 36 samples shares myc
- GSE278073 Targeting VRK2 Enhances the Efficacy of Anti-PD-1 Therapy in Hepatocellular Carcinoma by Reducing Direct Phosphorylation and Stability of MYC: CUT-TAG sequencing for MYC in hepatocellular carcinoma cells 16 samples shares myc
- GSE288604 Whole blood transcriptomics characterize molecular phenotypes of pulmonary Mycobacterium avium complex disease 200 samples shares myc
- GSE331167 Photoproximity labeling of c-Myc reveals SLK as a cancer-specific co-regulator 47 samples shares myc
- GSE303446 Beyond CD30 Targeting: Brentuximab Vedotin Triggers Immunogenic Cell Death and Antitumor Immunity in Mycosis Fungoides, Overcoming Resistance via BCL2 Inhibition 13 samples shares myc
- GSE284519 TRIM33 loss reduces Androgen Receptor transcriptional output and H2BK120 ubiquitination [ChIP-seq] 93 samples shares transcription
- 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
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.