Codon Specific Readthrough as a Mechanism of BRCA2 Restoration in Acquired PARP Inhibitor and Chemotherapy Resistance [RNA-seq]
RNA-seq (gene expression) · human · 10 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
- 10 samples: 5 conditions, mostly in duplicateGroups 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 GSE243141_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 PRJNA1016425 and SRA study SRP460346. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 5 replicated groups read from 10 sample titles; they account for 10 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.
- GSM7779863 ABTr1-1 ABTr1
- GSM7779864 ABTr1-2 ABTr1
- GSM7779865 ABTr2-1 ABTr2
- GSM7779866 ABTr2-2 ABTr2
- GSM7779867 ABTr3-1 ABTr3
- GSM7779868 ABTr3-2 ABTr3
- GSM7779869 ABTr4-1 ABTr4
- GSM7779870 ABTr4-2 ABTr4
- GSM7779871 PEO1-1 PEO1
- GSM7779872 PEO1-2 PEO1
- GSE243143 Codon Specific Readthrough as a Mechanism of BRCA2 Restoration in Acquired PARP Inhibitor and Chemotherapy Resistance 13 samples shares brca2, parp, chemotherapy, readthrough, restoration, resistance, inhibitor, specific
- GSE212435 Targeting STAT3-FoxO1 axis reverses de novo and acquired PARP inhibitor resistance independent of BRCA status 31 samples shares parp, resistance, inhibitor
- GSE281517 Lysosomal sequestration of PARP inhibitors drives heterogeneous accumulation in ovarian cancer to increase efficacy [RNA-seq] 72 samples shares parp, inhibitor
- GSE222556 Neoadjuvant PARPi or Chemotherapy in Ovarian Cancer Informs Targeting Effector Treg Cells for Homologous-Recombination-Deficient Tumors [10x scRNA-seq] 67 samples shares parp, chemotherapy
- GSE322780 PARP1 Suppression Drives ROS Resistance in Aneuploid Cancer Cells 46 samples shares parp, resistance
- GSE288275 Inhibition of the STAT3/Fanconi anemia axis is synthetic lethal with PARP inhibitor in breast cancer 24 samples shares parp, inhibitor
- GSE246085 Copper inhibits ATR and synergizes with PARP inhibitors (RNA-seq) 24 samples shares parp, inhibitor
- GSE285827 PARP inhibitors differentially regulate immune responses in distinct genetic backgrounds of high-grade serous tubo-ovarian carcinoma 18 samples shares parp, inhibitor
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.