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Splicing fidelity infuences the fate of mRNA molecules

GSE302375 synthetic construct; Homo sapiens RNA-seq (gene expression) 101 samples Submitted 2026/02/26 Platform GPL30173Platform GPL15520Platform GPL24676Platform GPL19424
At a glance

RNA-seq (gene expression) · synthetic construct; Homo sapiens · 101 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
synthetic construct; Homo sapiens
Samples
101 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
CRYPTID-exon: streamlined detection of cryptic exons from RNA-seq databioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
RNA splicing has historically been thought to be highly efficient and accurate, with little opportunity for deviation from regulated alternative splicing decisions. This dogma has been challenged by recent observations that suggest that biological noise may contribute substantially to transcriptome diversity. However, quantitative understanding of stochastic variations in splicing is challenging because these transcripts are likely subject to rapid degradation.
Read the full summary ↓
Here, we use ultra-deep sequencing across RNA compartments to track splicing intermediates in human cells and see abundant cryptic splicing associated with genomic features that promote splicing noise. We observe pervasive usage of low-fidelity splice sites, likely due to stochasticity in recruitment or binding of the spliceosome. These sites are most likely degraded in the nucleus rather than targeted by translation-dependent degradation processes, suggesting widespread surveillance and quality control of non-productive RNA transcripts. Our findings provide unprecedented insights into the propensity for error in RNA processing mechanisms to buffer mechanistic noise.
Published in
CRYPTID-exon: streamlined detection of cryptic exons from RNA-seq data
Khokhar ES, Brokaw K, Kartje ZJ et al. · bioRxiv : the preprint server for biology 2025 · PMID 41446134 · doi:10.64898/2025.12.17.694983
This dataset
Open GSE302375 in finder → Find more 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 GSE302375_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 PRJNA1290274 and SRA study SRP600180. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41446134) ↗
Study design
31 conditions, mostly in duplicate
HighFidelity_nuclear_amplicon ×12 HighFidelity_polyA_amplicon ×11 LowFidelity_nuclear_amplicon ×7 K562_nascent ×4 K562_nuclear ×4 K562_polyA ×4 KNS60_nascent ×4 KNS60_nuclear ×4 +26 more

Supports a between-group comparison across 98 samples.

How we read this: 31 replicated groups read from 101 sample titles; they account for 98 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.

Samples in this study

+ 61 more — browse all 101 samples with per-sample file links →

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