In vivo double knockout CAR-T screen identifies synergistic gene pairs that enhance anti-tumor immunity.
RNA-seq (gene expression) · human · 67 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
- 67 samples: 19 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 GSE272887_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 PRJNA1139168 and SRA study SRP521679. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 59 samples.
How we read this: 19 replicated groups read from 67 sample titles; they account for 59 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.
- GSM8414026 Donor3_Tumor1 Donor3_Tumo
- GSM8414027 Donor3_Tumor2 Donor3_Tumo
- GSM8414028 Donor3_Tumor3 Donor3_Tumo
- GSM8414029 Donor3_Tumor4 Donor3_Tumo
- GSM8414030 Donor3_Tumor5 Donor3_Tumo
- GSM8414031 Donor3_Control
- GSM8414032 Donor5_Control
- GSM8414033 Donor6_Control
- GSM8414034 Plasmid
- GSM8414035 Donor5_Tumor1 Donor5_Tumo
- GSM8414036 Donor5_Tumor2 Donor5_Tumo
- GSM8414037 Donor5_Tumor3 Donor5_Tumo
- GSM8414038 Donor6_Tumor1 Donor6_Tumo
- GSM8414039 Donor6_Tumor2 Donor6_Tumo
- GSM8414040 Donor6_Tumor3 Donor6_Tumo
- GSM8414041 D_0_1 D_0
- GSM8414042 D_0_2 D_0
- GSM8414043 D_0_3 D_0
- GSM8414044 D_6_1 D_6
- GSM8414045 D_6_2 D_6
- GSM8414046 D_6_3 D_6
- GSM8414047 D_24_1 D_24
- GSM8414048 D_24_2 D_24
- GSM8414049 D_24_3 D_24
- GSM8414050 D_72_1 D_72
- GSM8414051 D_72_2 D_72
- GSM8414052 D_72_3 D_72
- GSM8414053 N_0_1 N_0
- GSM8414054 N_0_2 N_0
- GSM8414055 N_0_3 N_0
- GSM8414056 N_6_1 N_6
- GSM8414057 N_6_2 N_6
- GSM8414058 N_6_3 N_6
- GSM8414059 N_24_1 N_24
- GSM8414060 N_24_2 N_24
- GSM8414061 N_24_3 N_24
- GSM8414062 N_72_1 N_72
- GSM8414063 N_72_2 N_72
- GSM8414064 N_72_3 N_72
- GSM8414065 S_0_1 S_0
+ 27 more — browse all 67 samples with per-sample file links →
No close match found for this topic, so these are simply the most-viewed datasets of the same kind.
- GSE127957 Loss of CHD1 promotes chromatin dysregulation leading to heterogeneous mechanisms of resistance to hormone therapy in prostate cancer [shRNA screen] 408 samples
- GSE236530 Enzyme-mediated methylation and alkynylation enables transcriptome-wide identification of pseudouridine modifications 50 samples
- GSE243312 Pateamine A mediates RNA sequence-selective translation repression by anchoring eIF4A and DDX3 to GNG motifs 36 samples
- GSE71962 Urinary miRNA biomarkers for early detection of pancreatic adenocarcinoma (PDAC) 29 samples
- GSE125179 microRNA modulated networks of adaptive and innate immune response in pancreatic ductal adenocarcinoma 28 samples
- GSE73691 Screening and validation of lncRNAs and circRNAs as miRNA sponges 23 samples
- GSE79383 A microfluidics-based in vitro model of the gastrointestinal human-microbe interface 18 samples
- GSE269036 Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity [RNA-Seq] 16 samples
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.