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Widespread specific intron-retention events in nuclear RNA complexes identified by sedimentation analysis of pluripotent cellular extracts

GSE284687 Homo sapiens (human) RNA-seq (gene expression) 63 samples Submitted 2025/08/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 63 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
63 samples: 63 samples, each with a distinct title — no replicated groupsGroups 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
Widespread specific intron retention events in nuclear RNA complexes identified by sedimentation analysis of pluripotent cellular extractsGenome research · 2025
Summary (written by the submitters)
Many essential cellular processes require RNA to interact with protein(s) to form ribonucleic protein complexes (RNPs). For example, all cellular proteins are produced by the ribosome - a large and stable RNP, gene splicing requires a choreography of numerous small and large RNPs, even the replication of telomeric DNA requires an RNP.
Read the full summary ↓
All these examples are stable RNPs that exhibit specific sedimentation rates (e.g., in a sucrose gradient) based on the composition of RNA and protein. In this study we aimed to identify RNAs components of RNPs on a transcriptome-wide scale. Using sucrose-gradient followed by sequencing we identified 1,057 RNA transcripts that are likely to be components of cellular RNPs. Our approach determined that RNP are not due to the cellular abundance nor length of the constituent RNA transcripts. The RNPs identified were predominantly nuclear, containing a specified retained intron and resistant to RNA degradation. Collectively our study reveals a widespread phenomena of a specific intron being retained in a stable nuclear RNP.
Published in
Widespread specific intron retention events in nuclear RNA complexes identified by sedimentation analysis of pluripotent cellular extracts
Pereira IT, Mamede I, Amaral PP et al. · Genome research 2025 · PMID 40858361 · doi:10.1101/gr.280431.125
This dataset
Open GSE284687 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE284687_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1197257 and SRA study SRP551703. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40858361) ↗
Study design
63 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 63 sample titles: 63 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.

Samples in this study

+ 23 more — browse all 63 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.