← BioTransfer GEO Dataset Finder
GEO series

Effect of DHX29 depletion on mRNA stability in HEK293T cells

GSE266017 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/03/19 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 6 conditions, mostly in triplicateGroups 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
Human DHX29 detects nonoptimal codon usage to regulate mRNA stabilityScience (New York, N.Y.) · 2026
Summary (written by the submitters)
Synonymous codon usage controls global gene expression in both prokaryotic and eukaryotic species. Non-optimal codons are known to induce mRNA decay; however, the underlying molecular mechanism remains poorly understood in human cells. Through genome-wide CRISPR screening, we identified the RNA-binding protein DHX29 as a critical regulator of codon-dependent gene expression.
Read the full summary ↓
Cryogenic electron microscopy and selective ribosome profiling demonstrated that DHX29 directly interacts with the A-site entrance of the translating 80S ribosome, the binding site for the eEF1A•GTP•aminoacyl-tRNA ternary complex, suggesting a role in monitoring aminoacyl-tRNA sampling. Proteomic analysis further revealed that DHX29 recruits the GIGYF2•4EHP complex to mediate global suppression of non-optimal mRNAs. These findings establish a mechanistic link between synonymous codon usage and the regulation of gene expression.
Published in
Human DHX29 detects nonoptimal codon usage to regulate mRNA stability
Hia F, Wu Y, Yoshinaga M et al. · Science (New York, N.Y.) 2026 · PMID 41855277 · doi:10.1126/science.adw0288
This dataset
Open GSE266017 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 GSE266017_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 PRJNA1105348 and SRA study SRP504389. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41855277) ↗
Study design
6 conditions, mostly in triplicate
HEK293T cells, Control, 0 h, ×3 HEK293T cells, Control, 3 h, ×3 HEK293T cells, Control, 6 h, ×3 HEK293T cells, DHX29-KO, 0 h, ×3 HEK293T cells, DHX29-KO, 3 h, ×3 HEK293T cells, DHX29-KO, 6 h, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.