Sensitive monitoring of enhancer and noncoding RNA transcription via ribozyme-assisted RNA editing
RNA-seq (gene expression) · human · 71 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
- 71 samples: 31 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.
- Paper
- Sensitive monitoring of enhancer and noncoding RNA transcription via ribozyme-assisted RNA editingNature communications · 2026
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 GSE315648_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 PRJNA1398389 and SRA study SRP659834. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 71 samples.
How we read this: 31 replicated groups read from 71 sample titles; they account for 71 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.
- GSM9433925 REDDIT_hESC_ctrl_rep1 REDDIT_hESC_ctrl
- GSM9433926 REDDIT_hESC_ctrl_rep2 REDDIT_hESC_ctrl
- GSM9433927 REDDIT_NANOG_rep1 REDDIT_NANOG
- GSM9433928 REDDIT_NANOG_rep2 REDDIT_NANOG
- GSM9433929 REDDIT_MIR302_rep1 REDDIT_MIR302
- GSM9433930 REDDIT_MIR302_rep2 REDDIT_MIR302
- GSM9433931 REDDIT_ACTB_intron1_rep1 REDDIT_ACTB_intron1
- GSM9433932 REDDIT_ACTB_intron1_rep2 REDDIT_ACTB_intron1
- GSM9433933 REDDIT_ACTB_polyA_rep1 REDDIT_ACTB_polyA
- GSM9433934 REDDIT_ACTB_polyA_rep2 REDDIT_ACTB_polyA
- GSM9433935 REDDIT_SOX2_polyA_rep1 REDDIT_SOX2_polyA
- GSM9433936 REDDIT_SOX2_polyA_rep2 REDDIT_SOX2_polyA
- GSM9433937 REDDIT_mESC_Ctrl_rep1 REDDIT_mESC_Ctrl
- GSM9433938 REDDIT_mESC_Ctrl_rep2 REDDIT_mESC_Ctrl
- GSM9433939 REDDIT_Lncenc1_rep1 REDDIT_Lncenc1
- GSM9433940 REDDIT_Lncenc1_rep2 REDDIT_Lncenc1
- GSM9433941 MIR302_Dif_D0_rep1 MIR302_Dif_D0
- GSM9433942 MIR302_Dif_D0_rep2 MIR302_Dif_D0
- GSM9433943 MIR302_Dif_D5_rep1 MIR302_Dif_D5
- GSM9433944 MIR302_Dif_D5_rep2 MIR302_Dif_D5
- GSM9433945 MIR302_Dif_D12_rep1 MIR302_Dif_D12
- GSM9433946 MIR302_Dif_D12_rep2 MIR302_Dif_D12
- GSM9433947 MIR302_Dif_D20_rep1 MIR302_Dif_D20
- GSM9433948 MIR302_Dif_D20_rep2 MIR302_Dif_D20
- GSM9433949 MIR302_Dif_D14_high_rep1 MIR302_Dif_D14_high
- GSM9433950 MIR302_Dif_D14_high_rep2 MIR302_Dif_D14_high
- GSM9433951 MIR302_Dif_D14_low_rep1 MIR302_Dif_D14_low
- GSM9433952 MIR302_Dif_D14_low_rep2 MIR302_Dif_D14_low
- GSM9433953 WT_mESC_DMSO_7days_rep1 WT_mESC_DMSO_7days
- GSM9433954 WT_mESC_DMSO_7days_rep2 WT_mESC_DMSO_7days
- GSM9433955 WT_mESC_UNC0638_7days_rep1 WT_mESC_UNC0638_7days
- GSM9433956 WT_mESC_UNC0638_7days_rep2 WT_mESC_UNC0638_7days
- GSM9433957 WT_mESC_DMSO_48h_rep1 WT_mESC_DMSO_48h
- GSM9433958 WT_mESC_DMSO_48h_rep2 WT_mESC_DMSO_48h
- GSM9433959 WT_mESC_UNC0638_48h_rep1 WT_mESC_UNC0638_48h
- GSM9433960 WT_mESC_UNC0638_48h_rep2 WT_mESC_UNC0638_48h
- GSM9433961 WT_hESC_DMSO_rep1 WT_hESC_DMSO
- GSM9433962 WT_hESC_DMSO_rep2 WT_hESC_DMSO
- GSM9433963 WT_hESC_RepSox_rep1 WT_hESC_RepSox
- GSM9433964 WT_hESC_RepSox_rep2 WT_hESC_RepSox
+ 31 more — browse all 71 samples with per-sample file links →
- GSE240603 An eRNA Transcription Checkpoint For Diverse Signal-dependent Enhancer Activation Programs 144 samples shares transcription, enhancer
- GSE80154 Enhancer invasion shapes MYCN dependent transcriptional amplification in neuroblastoma 144 samples shares transcription, enhancer
- GSE240599 An eRNA Transcription Checkpoint For Diverse Signal-dependent Enhancer Activation Programs 130 samples shares transcription, enhancer
- GSE292300 A core transcriptional regulatory circuitry controls super-enhancer-driven activation of LGR5 in colorectal cancer: ChIP-seq 76 samples shares transcription, enhancer
- GSE266391 Suppressing Aberrant Hedgehog Pathway and Overcoming Resistance to Smoothened Antagonists via Targeting Super-enhancer-driven Transcriptional Dependencies 74 samples shares transcription, enhancer
- GSE266389 Suppressing Aberrant Hedgehog Pathway and Overcoming Resistance to Smoothened Antagonists via Targeting Super-enhancer-driven Transcriptional Dependencies [RNA-seq] 60 samples shares transcription, enhancer
- GSE292302 A core transcriptional regulatory circuitry controls super-enhancer-driven activation of LGR5 in colorectal cancer: RNA-seq 42 samples shares transcription, enhancer
- GSE245173 APE1 regulates the transcription of urea cycle genes through stabilizing DNA G-quadruplexes in lung adenocarcinoma 38 samples shares transcription
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.