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SEC13 functions in the NMD pathway and together with core NMD factors regulates the unfolded protein response

GSE329468 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2026/05/06 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 45 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
45 samples: 13 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.
Summary (written by the submitters)
The nonsense-mediated decay pathway (NMD) is an RNA quality control mechanism that regulates the stability of target RNAs. We previously identified SEC13 (nuclear pore and COPII coat complex component), as a novel NMD factor in C. elegans and in HeLa cells; however, its mechanistic role in the NMD pathway remains unexplored. SEC13 is a component of several cellular complexes, including the COPII vesicle coat, the nuclear pore complex (NPC) and the nutrient sensing GATOR2 complex.
Read the full summary ↓
Transcriptomic profiling revealed that SEC13 co-regulates NMD targets with UPF1 and UPF2, acting on substrates translated both in the cytoplasm and at the ER. Importantly its role in NMD seems to operate independently of its function in other cellular complexes.
This dataset
Open GSE329468 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 GSE329468_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 PRJNA1459306 and SRA study SRP695942. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
13 conditions, mostly in triplicate
siNTC ×5 siSEC13 ×5 siUPF2 ×5 siNTC Cytoplasm ×3 siNTC Membrane ×3 siNUP133 ×3 siSEC13 Cytoplasm ×3 siSEC13 Membrane ×3 +5 more

Supports a between-group comparison across 45 samples.

How we read this: 13 replicated groups read from 45 sample titles; they account for 45 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

+ 5 more — browse all 45 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.