Identifying gene expression changes in fibroblasts treated with an inhibitor targeting MLL1-MENIN complex and depleted for MEN1 gene during reprogramming using RNA-seqeuncing
RNA-seq (gene expression) · human · 24 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
- 24 samples: 8 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.
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 GSE239991_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 PRJNA1001889 and SRA study SRP453162. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 24 samples.
How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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.
- GSM7679508 OSKM_VTP_1 OSKM_VTP
- GSM7679509 OSKM_VTP_2 OSKM_VTP
- GSM7679510 OSKM_VTP_3 OSKM_VTP
- GSM7679511 OSKM_DMSO_1 OSKM_DMSO
- GSM7679512 OSKM_DMSO_2 OSKM_DMSO
- GSM7679513 OSKM_DMSO_3 OSKM_DMSO
- GSM7679514 pre_OSKM_VTP_1 pre_OSKM_VTP
- GSM7679515 pre_OSKM_VTP_2 pre_OSKM_VTP
- GSM7679516 pre_OSKM_VTP_3 pre_OSKM_VTP
- GSM7679517 pre_OSKM_DMSO_1 pre_OSKM_DMSO
- GSM7679518 pre_OSKM_DMSO_2 pre_OSKM_DMSO
- GSM7679519 pre_OSKM_DMSO_3 pre_OSKM_DMSO
- GSM7679520 OSKM_NT1_1 OSKM_NT1
- GSM7679521 OSKM_NT1_2 OSKM_NT1
- GSM7679522 OSKM_NT1_3 OSKM_NT1
- GSM7679523 OSKM_92_1 OSKM_92
- GSM7679524 OSKM_92_2 OSKM_92
- GSM7679525 OSKM_92_3 OSKM_92
- GSM7679526 pre_OSKM_NT1_1 pre_OSKM_NT1
- GSM7679527 pre_OSKM_NT1_2 pre_OSKM_NT1
- GSM7679528 pre_OSKM_NT1_3 pre_OSKM_NT1
- GSM7679529 pre_OSKM_92_1 pre_OSKM_92
- GSM7679530 pre_OSKM_92_2 pre_OSKM_92
- GSM7679531 pre_OSKM_92_3 pre_OSKM_92
- GSE272002 LSD1 inhibitor, TAS1440, disrupts INSM1-LSD1 complex activating tumor-suppressive pathways via transcriptional reprogramming in neuroendocrine SCLC 324 samples shares reprogramming, inhibitor
- GSE280564 Reprogramming of normal fibroblasts into ovarian cancer-associated fibroblasts via non-vesicular paracrine signaling induces an activated fibroblast phenotype 12 samples shares reprogramming, fibroblasts
- GSE271896 Deep longitudinal immune profiling reveals reprogramming of memory T cells that links to B cell dysregulation with age 868 samples shares reprogramming
- GSE275067 Deep longitudinal immune profiling reveals reprogramming of memory T cells that links to B cell dysregulation with age. 252 samples shares reprogramming
- GSE286918 Metabolic reprogramming and ferroptosis occurring in granulosa cells from patients with diminished ovarian reserve 116 samples shares reprogramming
- GSE232076 In vitro reconstitution of epigenetic reprogramming in the human germ line [RNA-Seq] 89 samples shares reprogramming
- GSE285037 Faecalibacterium prausnitzii induces an anti-inflammatory response and a metabolic reprogramming in human monocytes 72 samples shares reprogramming
- GSE341455 Skeletal muscle reprogramming of metabolic, ribosomal and developmental pathways contributes to Roux-en-Y gastric bypass-induced adaptation in women 70 samples shares reprogramming
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.