RNA-seq and iCLIP-seq of cells that harbor retinitis pigmentosa mutations
RNA-seq (gene expression) · human · 31 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
- 31 samples: 29 distinct titles, 2 of them repeatedGroups 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
- Retinitis pigmentosa-linked mutations impair the snRNA unwinding activity of SNRNP200 and reduce pre-mRNA binding of PRPF8Cellular and molecular life sciences : CMLS · 2025
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 GSE213807_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 PRJNA882468 and SRA study SRP398282. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 31 sample titles: 29 distinct titles with little repetition. 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.
- GSM6594612 RNA-seq_RPE_WT_rep1
- GSM6594613 RNA-seq_RPE_WT_rep2
- GSM6594614 RNA-seq_RPE_Y2334N_rep1
- GSM6594615 RNA-seq_RPE_Y2334N_rep2
- GSM6594616 iCLIP_PRPF8_HeLa_WT_noUV_CCGG_Rep1
- GSM6594617 iCLIP_PRPF8_HeLa_WT_GGTT_Rep1
- GSM6594618 iCLIP_PRPF8_HeLa_WT_TTGT_Rep2
- GSM6594619 iCLIP_PRPF8_HeLa_WT_TGGC_Rep3
- GSM6594620 iCLIP_PRPF8_HeLa_F2314L_noUV_CCAC_Rep1
- GSM6594621 iCLIP_PRPF8_HeLa_F2314L_GGTC_Rep1
- GSM6594622 iCLIP_PRPF8_HeLa_F2314L_GGCA_Rep2
- GSM6594623 iCLIP_PRPF8_HeLa_Y2334N_noUV_CCGG_Rep1
- GSM6594624 iCLIP_PRPF8_HeLa_Y2334N_GGTT_Rep1
- GSM6594625 iCLIP_PRPF8_HeLa_Y2334N_TTGT_Rep2
- GSM6594626 iCLIP_PRPF8_HeLa_Y2334N_TGGC_Rep3
- GSM6594627 iCLIP_BRR2_HeLa_WT_noUV_CCGG_Rep1
- GSM6594628 iCLIP_BRR2_HeLa_WT_GGTT_Rep1
- GSM6594629 iCLIP_BRR2_HeLa_WT_TTGT_Rep2
- GSM6594630 iCLIP_BRR2_HeLa_WT_TGGC_Rep3
- GSM6594631 iCLIP_BRR2_HeLa_S1087L_noUV_CCGG_Rep1
- GSM6594632 iCLIP_BRR2_HeLa_S1087L_GGTT_Rep1
- GSM6594633 iCLIP_BRR2_HeLa_S1087L_TTGT_Rep2
- GSM6594634 iCLIP_BRR2_HeLa_S1087L_TGGC_Rep3
- GSM6594635 iCLIP_BRR2_HeLa_R1090L_GGTT_Rep1
- GSM6594636 iCLIP_BRR2_HeLa_R1090L_TTGT_Rep2
- GSM6594637 iCLIP_BRR2_HeLa_R1090L_TGGC_Rep3
- GSM6594638 iCLIP_PRPF8_RPE_WT_noUV_CCGG_Rep1
- GSM6594639 iCLIP_PRPF8_RPE_WT_CGGA_Rep1
- GSM6594640 iCLIP_PRPF8_RPE_WT_TATT_Rep2
- GSM6594641 iCLIP_PRPF8_RPE_Y2334N_CGGA_Rep1
- GSM6594642 iCLIP_PRPF8_RPE_Y2334N_TATT_Rep2
- GSE213806 Transcriptome-wide mapping of RNA:protein interactions of PRPF8 and SNRNP200 (BRR2) with retinitis pigmentosa (RP) mutations by iCLIP 27 samples shares pigmentosa, retinitis, mutations
- GSE259407 Anti-EGFR aptamer exhibits direct anti-cancer effects in NSCLC cells harboring EGFR L858R mutations 36 samples shares mutations, harbor
- GSE297199 The landscape of gain and loss of function mutations in melanoma [RNA-seq] 242 samples shares mutations, rna-seq
- GSE287297 Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia [RNA-seq] 70 samples shares mutations, rna-seq
- GSE328821 U2AF1 mutations rescue deleterious exon skipping induced by KRAS mutations [RNA-Seq] 53 samples shares mutations, rna-seq
- GSE303838 MECP2 Mutations Rewire Human ESC Fate and Bias Cortical Lineage Commitment [RNA-Seq] 48 samples shares mutations, rna-seq
- GSE293771 H3F3A K27M Mutations Drive a Repressive Transcriptome by Modulating Chromatin Accessibility, Independent of H3K27me3 in Diffuse Midline Glioma [RNA-seq] 36 samples shares mutations, rna-seq
- GSE324093 ESR1 mutations shape the clonal dynamics, genetic diversity and cellular states of acquired resistance to CDK4/6 inhibitors [RNA-seq] 32 samples shares mutations, 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.