Leucegene: AML sequencing (part 1-6, GRCh38 reference)
RNA-seq (gene expression) · human · 452 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
- 452 samples: 452 samples, each with a distinct title — no replicated groupsGroups 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
- IL1RAP is an immunotherapeutic target for normal karyotype triple-mutated acute myeloid leukemiaBiomarker research · 2025
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 GSE232129_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 PRJNA971105. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from the first 400 of 452 sample titles: 400 distinct titles with little repetition. 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.
- GSM7315671 02H053 [re-analysis]
- GSM7315672 02H066 [re-analysis]
- GSM7315673 03H041 [re-analysis]
- GSM7315674 03H116 [re-analysis]
- GSM7315675 03H119 [re-analysis]
- GSM7315676 04H024 [re-analysis]
- GSM7315677 04H112 [re-analysis]
- GSM7315678 04H133 [re-analysis]
- GSM7315679 05H050 [re-analysis]
- GSM7315680 05H149 [re-analysis]
- GSM7315681 05H163 [re-analysis]
- GSM7315682 05H181 [re-analysis]
- GSM7315683 06H028 [re-analysis]
- GSM7315684 06H144 [re-analysis]
- GSM7315685 07H062 [re-analysis]
- GSM7315686 07H135 [re-analysis]
- GSM7315687 07H160 [re-analysis]
- GSM7315688 09H043 [re-analysis]
- GSM7315689 09H083 [re-analysis]
- GSM7315690 09H111 [re-analysis]
- GSM7315691 09H113 [re-analysis]
- GSM7315692 09H115 [re-analysis]
- GSM7315693 10H052 [re-analysis]
- GSM7315694 10H056 [re-analysis]
- GSM7315695 10H068 [re-analysis]
- GSM7315696 10H072 [re-analysis]
- GSM7315697 10H089 [re-analysis]
- GSM7315698 10H092 [re-analysis]
- GSM7315699 10H095 [re-analysis]
- GSM7315700 10H101 [re-analysis]
- GSM7315701 10H115 [re-analysis]
- GSM7315702 10H166 [re-analysis]
- GSM7315703 11H006 [re-analysis]
- GSM7315704 11H009 [re-analysis]
- GSM7315705 11H021 [re-analysis]
- GSM7315706 11H046 [re-analysis]
- GSM7315707 11H058 [re-analysis]
- GSM7315708 11H072 [re-analysis]
- GSM7315709 11H083 [re-analysis]
- GSM7315710 11H095 [re-analysis]
+ 412 more — browse all 452 samples with per-sample file links →
- GSE232130 Leucegene: AML sequencing (GRCh38 reference) 691 samples shares grch38, aml, leucegene, reference
- GSE297413 Genomic and transcriptomic determinants of clinical outcomes in patients with AML and DNMT3A mutations 264 samples shares aml
- GSE307747 RNA sequencing of PDX-AML samples using prime-seq 25 samples shares aml
- GSE289786 Improved Prediction of Venetoclax and Azacitidine Efficacy in Acute Myeloid Leukemia through Machine Learning [AML patients] 142 samples shares aml
- GSE327386 RNA-Seq of paired diagnosis and relapse AML specimens with healthy controls 130 samples shares aml
- GSE338135 NRAS knockdown reverses monocytic cell state and resensitizes resistant FLT3-AML cells to gilteritinib 75 samples shares aml
- GSE271137 Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AML 75 samples shares aml
- GSE260773 RNA-seq Analysis of Acute myeloid leukemia (AML) primary samples 69 samples shares aml
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.