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Allele-specific genomics decodes gene targets and mechanisms of the non-coding genome

GSE269513 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2025/09/17 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
Samples
18 samples: 6 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.
Paper
Allele-specific genomics decodes gene targets and mechanisms of the non-coding genomeNucleic acids research · 2025
Summary (written by the submitters)
A substantial proportion of disease variants reside in non-coding RNAs (ncRNAs), which serve as important regulatory elements. However, most ncRNA targets remain unknown, hindering our understanding of complex diseases. We found allele-specific ncRNAs enriched near allelic protein-coding genes, suggesting that allele-specific expression could be used to predict ncRNA-targets.
Read the full summary ↓
We applied this concept to mouse organs, revealing 397 enhancing or repressive ncRNA-to-target associations. Next, we applied this strategy to tissues from nearly 1,000 individuals (GTEx). Given the outbred nature of humans, each individual harbors a unique allele-specific landscape, allowing novel ncRNA-to-target discovery with each individual. We uncovered 2,291 ncRNA-to-targets along with their mechanisms, which we benchmarked against sample-matched eQTLs. Further GWAS integration assigned 30.59% of variants overlapping informative ncRNA to their protein-coding targets. As more sequencing data and risk variants become available, this strategy will continue to elucidate targets and mechanisms, ultimately decoding the entire cis-acting landscape of the non-coding genome.
Published in
Allele-specific genomics decodes gene targets and mechanisms of the non-coding genome
Hasenbein TP, Hoelzl S, Engelhardt S et al. · Nucleic acids research 2025 · PMID 41118577 · doi:10.1093/nar/gkaf912
This dataset
Open GSE269513 in finder → Find more mouse 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 GSE269513_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 PRJNA1122169 and SRA study SRP513019. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41118577) ↗
Study design
6 conditions, mostly in triplicate
Br_9w_RNA_BxC_XX ×3 He_9w_RNA_BxC_XX ×3 Ki_9w_RNA_BxC_XX ×3 Li_9w_RNA_BxC_XX ×3 Lu_9w_RNA_BxC_XX ×3 Sp_9w_RNA_BxC_XX ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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