← BioTransfer GEO Dataset Finder
GEO series

Transcriptional and epigenetic changes from the genesis of the human male germline through to adulthood provide a map of prenatal and postnatal molecular drivers of differentiation and reveal the developmental origin of seminoma.

GSE256162 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 50 samples Submitted 2024/07/10 Platform GPL21697
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 50 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Testicular cancerThis series is part of our curated briefing on it.
Samples
50 samples: 50 samples, each with a distinct title — no replicated groupsGroups 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
Defining the cellular origin of seminoma by transcriptional and epigenetic mapping to the normal human germlineCell reports · 2024
Summary (written by the submitters)
Aberrancies in male germline development can lead to the formation of seminoma, a testicular germ cell tumor. Seminomas are biologically similar to primordial germ cells (PGCs) and many bear an isochromosome 12p [i(12p)] with two additional copies of the short arm of chromosome 12.
Read the full summary ↓
By mapping of seminoma transcriptomes and open chromatin landscape onto our normal human male germline trajectory, we find that seminoma resembles premigratory/migratory primordial germ cells, but exhibit enhanced germline and pluripotency programs and upregulate genes involved in apoptosis, angiogenesis, and MAPK/ERK pathways. By modeling seminoma using pluripotent stem cell-derived primordial germ cells from Pallister Killian syndrome patients mosaic for i(12p), we identify gene dosage effects that may contribute to transformation. As murine seminoma models do not exist, our analyses provide insights into genetic, cellular and signaling programs driving seminoma transformation and our in vitro platform will permit future evaluation of additional signals required for seminoma tumorigenesis.
Published in
Defining the cellular origin of seminoma by transcriptional and epigenetic mapping to the normal human germline
Cheng K, Seita Y, Whelan EC et al. · Cell reports 2024 · PMID 38861385 · doi:10.1016/j.celrep.2024.114323
This dataset
Open GSE256162 in finder → Find more human RNA-seq datasets → Testicular cancer landscape →
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 GSE256162_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1078349 and SRA study SRP490632. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38861385) ↗
Study design
50 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 50 sample titles: 50 distinct titles with little repetition. 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

+ 10 more — browse all 50 samples with per-sample file links →

More Testicular cancer datasets

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.