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Profiling of terminating ribosomes reveals translational control at stop codons

GSE270879 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2025/12/11 Platform GPL19057Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples: 23 distinct titles, 3 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.
Summary (written by the submitters)
Accurate termination of protein synthesis is paramount for the integrity of cellular proteome. Here we establish profiling of terminating ribosomes in mammalian cells and report a wide range of ribosome pausing at individual stop codons. We identify a sequence motif upstream of stop codons that contributes to termination pausing, which was confirmed by massively paralleled reporter assays.
Read the full summary ↓
Lack of termination pausing increases the chance of stop codon slippage, generating proteins with mixed C-terminal extensions. We demonstrate that the sequence- dependent termination pausing is a result of post-decoding mRNA scanning by the 3’ end of 18S rRNA. We further uncover tissue-specific termination pausing that correlates with the stoichiometry of Rps26, which constrains mRNA:rRNA interaction. Termination pausing represents a translational signature associated with translational control at stop codons.
This dataset
Open GSE270879 in finder → Find more 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 GSE270879_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 PRJNA1128682 and SRA study SRP516296. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
23 distinct titles, 3 of them repeated
Ribo_sh-ABCE1 ×3 Ribo_vecto ×2 Ribo_Rps26-OE ×2

How we read this: Read from 27 sample titles: 23 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
Datasets on a similar topic

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