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Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle [RNAseq]

GSE231290 Homo sapiens (human) RNA-seq (gene expression) 51 samples Submitted 2024/07/03 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 51 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
51 samples: 25 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
Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycleMolecular cell · 2024
Summary (written by the submitters)
Dissecting the myriad regulatory mechanisms controlling eukaryotic transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm.
Read the full summary ↓
These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered roles for DDX3X and PABPC4 in nuclear export. For hundreds of genes, most transcripts were degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, a machine learning model identified additional molecular features that underlie the diverse life cycles of mammalian mRNAs.
Published in
Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle
Ietswaart R, Smalec BM, Xu A et al. · Molecular cell 2024 · PMID 38964322 · doi:10.1016/j.molcel.2024.06.008
This dataset
Open GSE231290 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 GSE231290_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 PRJNA963053 and SRA study SRP435121. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38964322) ↗
Study design
25 conditions, mostly in duplicate
DDX3-G325E Cytoplasmic RNAseq ×2 DDX3-G325E Nuclear RNAseq ×2 DDX3-R326H Cytoplasmic RNAseq ×2 DDX3-R326H Nuclear RNAseq ×2 DDX3-R351W Cytoplasmic RNAseq ×2 DDX3-R351W Nuclear RNAseq ×2 DDX3-R376C Nuclear RNAseq ×2 DDX3 R534S Cytoplasmic RNAseq ×2 +18 more

Supports a between-group comparison across 50 samples.

How we read this: 25 replicated groups read from 51 sample titles; they account for 50 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

+ 11 more — browse all 51 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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