Functional Characterization of Cooperating MGA Mutations in RUNX1::RUNX1T1 Acute Myeloid Leukemia [RNA-seq]
RNA-seq (gene expression) · human · 29 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
- 29 samples: 8 conditions, mostly with about 4 replicates eachGroups 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
- Functional characterization of cooperating MGA mutations in RUNX1::RUNX1T1 acute myeloid leukemiaLeukemia · 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 GSE253748_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 PRJNA1066867 and SRA study SRP484822. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 29 samples.
How we read this: 8 replicated groups read from 29 sample titles; they account for 29 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.
- GSM8027359 RNA-seq of Molm13 cells with homozygous deletion of MGA using CRISPR C2 RNA-seq of Molm13 cells…
- GSM8027360 RNA-seq of Molm13 cells with homozygous deletion of MGA using CRISPR C3 RNA-seq of Molm13 cells…
- GSM8027361 RNA-seq of Molm13 cells with homozygous deletion of MGA using CRISPR C4 RNA-seq of Molm13 cells…
- GSM8027362 RNA-seq of Molm13 cells with WT MGA C2 RNA-seq of Molm13 cells…
- GSM8027363 RNA-seq of Molm13 cells with WT MGA C3 RNA-seq of Molm13 cells…
- GSM8027364 RNA-seq of Molm13 cells with WT MGA C4 RNA-seq of Molm13 cells…
- GSM8027365 RNA-seq of cKit+ cells from Mga(+/+) C57/B6 mice #1 RNA-seq of cKit+ cells …
- GSM8027366 RNA-seq of cKit+ cells from Mga(+/+) C57/B6 mice #2 RNA-seq of cKit+ cells …
- GSM8027367 RNA-seq of cKit+ cells from Mga(+/+) C57/B6 mice #3 RNA-seq of cKit+ cells …
- GSM8027368 RNA-seq of cKit+ cells from Mga(+/+) C57/B6 mice #4 RNA-seq of cKit+ cells …
- GSM8027369 RNA-seq of cKit+ cells from Mga(+/-) C57/B6 mice #1 RNA-seq of cKit+ cells …
- GSM8027370 RNA-seq of cKit+ cells from Mga(+/-) C57/B6 mice #2 RNA-seq of cKit+ cells …
- GSM8027371 RNA-seq of cKit+ cells from Mga(+/-) C57/B6 mice #3 RNA-seq of cKit+ cells …
- GSM8027372 RNA-seq of cKit+ cells from Mga(+/-) C57/B6 mice #4 RNA-seq of cKit+ cells …
- GSM8027373 RNA-seq of cKit+ cells from Mga(-/-) C57/B6 mice #1 RNA-seq of cKit+ cells …
- GSM8027374 RNA-seq of cKit+ cells from Mga(-/-) C57/B6 mice #2 RNA-seq of cKit+ cells …
- GSM8027375 RNA-seq of cKit+ cells from Mga(-/-) C57/B6 mice #3 RNA-seq of cKit+ cells …
- GSM8027376 RNA-seq of cKit+ cells from Mga(-/-) C57/B6 mice #4 RNA-seq of cKit+ cells …
- GSM8027377 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(+/+) HSCs #1 RUNX1T1+ cells expresse…
- GSM8027378 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(+/+) HSCs #2 RUNX1T1+ cells expresse…
- GSM8027379 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(+/+) HSCs #3 RUNX1T1+ cells expresse…
- GSM8027380 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(+/-) HSCs #1 RUNX1T1+ cells expresse…
- GSM8027381 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(+/-) HSCs #2 RUNX1T1+ cells expresse…
- GSM8027382 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(+/-) HSCs #3 RUNX1T1+ cells expresse…
- GSM8027383 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(+/-) HSCs #4 RUNX1T1+ cells expresse…
- GSM8027384 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(-/-) HSCs #1 RUNX1T1+ cells expresse…
- GSM8027385 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(-/-) HSCs #2 RUNX1T1+ cells expresse…
- GSM8027386 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(-/-) HSCs #3 RUNX1T1+ cells expresse…
- GSM8027387 RNA-seq of RUNX1::RUNX1T1+ cells expressed in Mga(-/-) HSCs #4 RUNX1T1+ cells expresse…
- GSE253753 Functional Characterization of Cooperating MGA Mutations in RUNX1::RUNX1T1 Acute Myeloid Leukemia 126 samples shares runx1t1, runx1, mga, cooperating, mutations, leukemia, myeloid
- GSE253750 Functional Characterization of Cooperating MGA Mutations in RUNX1::RUNX1T1 Acute Myeloid Leukemia [Cut&Run] 87 samples shares runx1t1, runx1, mga, cooperating, mutations, leukemia, myeloid
- GSE248942 Spatiotemporal control of SMARCA5 by a MAPK-RUNX1 axis distinguishes mutant KRAS-driven pancreatic malignancy from tissue regeneration (ATAC-Seq) 123 samples shares runx1
- GSE251674 Redistribution of PU.1 partner transcription factor RUNX1 binding secures cell survival during leukemogenesis 79 samples shares runx1
- GSE248944 Spatiotemporal control of SMARCA5 by a MAPK-RUNX1 axis distinguishes mutant KRAS-driven pancreatic malignancy from tissue regeneration (CUT&RUN) 14 samples shares runx1
- GSE267455 IRF2BP2 affects T-ALL cell proliferation by cooperating with master transcription factors 19 samples shares cooperating
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.