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m6A modification landscapes of human metapneumovirus, mumps virus, and host A549 cell RNAs revealed by single-base-resolution GLORI-seq [A549 mRNA]

GSE333610 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2026/07/10 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 15 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
15 samples: 5 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)
We infected human lung adenocarcinoma A549 cells with multiple nonsegmented negative-sense (NNS) RNA viruses, including Human metapneumovirus (hMPV), Mumps virus (MuV), Vesicular stomatitis virus (VSV), and Sendai virus (SeV), and evaluated the differences among these viruses in activating innate immune signaling pathways in epithelial cells through transcriptomic analyses of infected cells.
Read the full summary ↓
Furthermore, by integrating the effects of viral infection on the m6A landscape of A549 cell transcripts, we analyzed the correlation between m6A epitranscriptomic regulation and the activation of innate immunity–related transcripts, as well as the role of m6A in viral immune evasion.
This dataset
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.

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 GSE333610_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1471668 and SRA study SRP704472. Searching any of these in the dataset finder brings you back here.

Study design
5 conditions, mostly in triplicate
Mock ×3 hMPV ×3 MuV ×3 VSV ×3 SeV ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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
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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.