USP7 inhibitors disrupt EBNA1 interactome and EBV tumorigenesis
RNA-seq (gene expression) · human · 12 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
- 12 samples: 3 × SNU719_DMSO vs 3 × SNU719_GNE vs 3 × AGS_GNE vs 2 × AGS_DMSOGroups 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 GSE284839_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 PRJNA1200707 and SRA study SRP552742. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 11 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 11 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.
- GSM8693778 SNU719_DMSO_1 SNU719_DMSO
- GSM8693779 SNU719_DMSO_2 SNU719_DMSO
- GSM8693780 SNU719_DMSO_3 SNU719_DMSO
- GSM8693781 SNU719_GNE_1 SNU719_GNE
- GSM8693782 SNU719_GNE_2 SNU719_GNE
- GSM8693783 SNU719_GNE_3 SNU719_GNE
- GSM8693784 AGS_DMSO_1 AGS_DMSO
- GSM8693785 AGS_DSMO_2
- GSM8693786 AGS_DMSO_2 AGS_DMSO
- GSM8693787 AGS_GNE_1 AGS_GNE
- GSM8693788 AGS_GNE_2 AGS_GNE
- GSM8693789 AGS_GNE_3 AGS_GNE
- GSE315867 EBNA1 inhibitors reveal CDC7 and POU2F1 as direct functional targets in EBV+ epithelial cancers [RNA-Seq] 18 samples shares ebna1, ebv, inhibitors
- GSE315868 EBNA1 inhibitors reveal CDC7 and POU2F1 as direct functional targets in EBV+ epithelial cancers 22 samples shares ebna1, ebv, inhibitors
- GSE289776 Genome stability disrupted by the circRBM39-RPA2 axis represses breast tumorigenesis 12 samples shares tumorigenesis, disrupt
- GSE317339 EBV INFECTION OUTCOMES DETERMINED BY MONOCYTE AND TREG-DRIVEN IMMUNE DYNAMICS IN AN EX VIVO PBMC MODEL 60 samples shares ebv
- GSE255092 Interaction between HIV-1 Tat and EBV Zta favours immune escape of B cells by downregulating HLA-ABC expression 56 samples shares ebv
- GSE317338 EBV INFECTION OUTCOMES DETERMINED BY MONOCYTE AND TREG-DRIVEN IMMUNE DYNAMICS IN AN EX VIVO PBMC MODEL [RNA-Seq Mock] 40 samples shares ebv
- GSE263490 (RNA-seq-SHSY5Y) Hao-Fountain syndrome protein USP7 mediates neuronal differentiation via BCOR-ncPRC1.1 38 samples shares usp7
- GSE267750 TBX21+CD21loCXCR3+B cell expansion with an EBV-associated phenotype in clinically isolated syndrome conversion to multiple sclerosis 32 samples shares ebv
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.