DUX4 activates a transcriptional network involving Krüppel-like factors KLF17 and KLF18 [KLF17_KLF18_OE]
RNA-seq (gene expression) · human · 9 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
- 9 samples: 3 × MB135 KLF17 vs 3 × MB135 KLF18 vs 3 × MB135 GFPGroups 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 GSE299603_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 PRJNA1275669 and SRA study SRP592087. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 9 samples.
How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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.
- GSM9041918 MB135 KLF17 Rep1 MB135 KLF17
- GSM9041919 MB135 KLF17 Rep2 MB135 KLF17
- GSM9041920 MB135 KLF17 Rep3 MB135 KLF17
- GSM9041921 MB135 KLF18 Rep1 MB135 KLF18
- GSM9041922 MB135 KLF18 Rep2 MB135 KLF18
- GSM9041923 MB135 KLF18 Rep3 MB135 KLF18
- GSM9041924 MB135 GFP Rep1 MB135 GFP
- GSM9041925 MB135 GFP Rep2 MB135 GFP
- GSM9041926 MB135 GFP Rep3 MB135 GFP
- GSE299601 DUX4 activates a transcriptional network involving Krüppel-like factors KLF17 and KLF18 [ChIP-Seq] 6 samples shares klf17, klf18, dux4, activates, involving, ppel-like, network, factors
- GSE316237 Differential RNA-seq analysis of MB135iDUX4 with siRNA mediated knockdown of KLF17 and KLF18 18 samples shares klf17, klf18, dux4
- GSE316240 Differential RNA-seq analyses in MB135iDUX4_KLF18 KO_i3xFLAG-KLF18_AAVSI short and long cell lines 18 samples shares klf18, dux4
- GSE316239 Differential RNA-seq analysis of MB135iDUX4 with CRISPR/Cas9-mediated KLF18 knockouts 12 samples shares klf18, dux4
- GSE316242 Differential RNA-seq analyses in MB135iDUX4_KLF18 KO_i3xFLAG-KLF18_AAVSI long and VNTR∆ cell lines 12 samples shares klf18, dux4
- GSE5827 NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth 28 samples shares activates, network
- GSE263611 IFN-γ activates an immune-like regulatory network in the cardiac vascular endothelium 16 samples shares activates, network
- GSE288566 Cup-like nuclei is a hallmark of IGH:DUX4 acute lymphoblastic leukemia and reveals cytoplasmic mitochondria accumulation 57 samples shares dux4
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.