BMAL1-HIF2α heterodimers contribute to ccRCC [RNA-seq_FigS4]
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 × 786O, shControl, biol vs 3 × 786O, shBMAL1, biol vs 3 × 786O + VHL (WT8), shControl, biol vs 3 × 786O + VHL (WT8), shBMAL1, biolGroups 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
- BMAL1 and ARNT enable circadian HIF2α responses in clear cell renal cell carcinomaNature communications · 2025
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 GSE269339_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 PRJNA1121252 and SRA study SRP512428. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.
- GSM8313407 786O, shControl, biolrep 1 786O, shControl, biol
- GSM8313408 786O, shControl, biolrep 2 786O, shControl, biol
- GSM8313409 786O, shControl, biolrep 3 786O, shControl, biol
- GSM8313410 786O, shBMAL1, biolrep 1 786O, shBMAL1, biol
- GSM8313411 786O, shBMAL1, biolrep 2 786O, shBMAL1, biol
- GSM8313412 786O, shBMAL1, biolrep 3 786O, shBMAL1, biol
- GSM8313413 786O + VHL (WT8), shControl, biolrep 1 786O + VHL (WT8), shCon…
- GSM8313414 786O + VHL (WT8), shControl, biolrep 2 786O + VHL (WT8), shCon…
- GSM8313415 786O + VHL (WT8), shControl, biolrep 3 786O + VHL (WT8), shCon…
- GSM8313416 786O + VHL (WT8), shBMAL1, biolrep 1 786O + VHL (WT8), shBMA…
- GSM8313417 786O + VHL (WT8), shBMAL1, biolrep 2 786O + VHL (WT8), shBMA…
- GSM8313418 786O + VHL (WT8), shBMAL1, biolrep 3 786O + VHL (WT8), shBMA…
- GSE290779 BMAL1-HIF2α heterodimers contribute to ccRCC [RNA-seq] 17 samples shares bmal1-hif2, heterodimers, contribute, rna-seq, ccrcc
- GSE290669 BMAL1-HIF2α heterodimers contribute to ccRCC 20 samples shares bmal1-hif2, heterodimers, contribute, ccrcc
- GSE269334 BMAL1-HIF2α heterodimers contribute to ccRCC [ChIP-seq] 15 samples shares bmal1-hif2, heterodimers, contribute, ccrcc
- GSE295126 VGLL1 contributes to both the transcriptome and epigenome of the developing trophoblast compartment [RNA-Seq] 43 samples shares contribute, rna-seq
- GSE298146 SOX4 Mediated Differentiation of Specific T Lymphocyte Subsets at the Maternal-Fetal Interface Contributes to Preterm Labor [RNA-Seq] 34 samples shares contribute, rna-seq
- GSE244979 Conserved transcriptional response to adversity-related inflammation contributes to Meniere’s disease progression [RNA-Seq 2] 28 samples shares contribute, rna-seq
- GSE229019 Merkel cell polyomavirus small tumor antigen contributes to immune evasion by interfering with type I interferon signaling [RNA-Seq] 28 samples shares contribute, rna-seq
- GSE286147 Two parallel neural ectoderm populations contribute to the developing brain [RNA-seq] 18 samples shares contribute, rna-seq
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.