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Profiling circular RNAs across human embryonic stem cell differentiation identifies the role of circACVR2A(4-6) in neuroectoderm lineage differentiation [Total_circACRVA]

GSE245984 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/10/01 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
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)
Differentiation of human stem cells into any of the three germ cell layers is accompanied by and dependent upon a myriad of transcriptional changes. Members of a family of largely non-coding RNA transcripts, called circular RNAs (circRNAs), which arise via alternative splicing of pre-mRNAs have been shown to play important roles in stemness and cell differentiation.
Read the full summary ↓
Herein, human embryonic stem cells were differentiated into five cell types and circRNAs and mRNAs were profiled by high-throughput sequencing. Highly expressed circRNAs whose expression was regulated across these lineages were silenced at the RNA level. One circRNA, circACVR2A(2-4), was found to induce rapid phenotypic and transcriptional transition to neurectoderm cell lineage. Interestingly, its interaction with cyclin H within the cyclin-dependent kinase (CDK) activating kinase (CAK) complex was found to be required for maintaining CDK phosphorylation homeostasis, influencing the activity of RNA polymerase II and cell cycle progression.
This dataset
Open GSE245984 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 GSE245984_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 PRJNA1030925 and SRA study SRP467833. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Samples in this study

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