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Application of RNA-seq for single nucleotide variation identification patients with hereditary diseases: analysis of trasncriptomic data and perspective use

GSE273325 Homo sapiens (human) Single-nucleus RNA-seq 54 samples Submitted 2025/06/04 Platform GPL18460
At a glance

Single-nucleus RNA-seq · human · 54 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
54 samples: 54 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
Application of RNA-seq for single nucleotide variation identification in a cohort of patients with hypertrophic cardiomyopathyScientific reports · 2025
Summary (written by the submitters)
Background: Various DNA sequencing methods, including particular genes sequencing, whole genome sequencing or exome sequencing, are presently used to identify single nucleotide variations (SNVs) associated with inherited diseases. Most of the known mutations and polymorphisms associated with hereditary diseases are localized in the coding regions and cause an amino-acid substitution, appearance of new stop codon or open reading frame shifts.
Read the full summary ↓
These types of mutations can be identified using transcriptome sequencing. Full transcriptome sequencing is widely used to study diseases. The results of such investigations are usually available in public databases. This data could be tapped for identification of novel mutations and disease-associated variants. In this study, we are analyzing the potential use of full transcriptome sequencing for SNV identification, using RNA-seq of myocardium of patients with HCMP as an example. The goal of the current study is development of pipeline for SNV identification from RNA-seq data and application of this pipeline to identification of putative pathogenically significant SNV. Results: The algorithm of identification of SNV based on transcriptomic sequencing data has been developed. The algorithm was evaluated and the optimal quality threshold was determined based on allelic discrimination for the rs397516037 mutation (MYBPC3 c.3697C>T) among patients. A quality threshold of 75 was selected, at which false-positive and false-negative results for the rs397516037 variant were excluded. 43587 total SNVs with 75 and higher quality was identified in 48 transcriptomes of HCMP patients. In order to further verify the results obtained using the algorithm, 8 SNVs were selected in the MYBPC3 and MYH7 genes. Each of these 8 SNVs was confirmed by Sanger sequencing. Conclusions: The developed method has proven its effectiveness in identifying putative pathogenic variants in RNA-seq data. Its use will be especially useful in the context of large volumes of accumulated transcriptomic data that have not been analyzed in this way before. However, it is necessary to consider the limitations and scope of this method. The study was conducted on the basis of a disease and mode of inheritance are well suited for this method, but in other cases its applicability should be assessed individually.
Published in
Application of RNA-seq for single nucleotide variation identification in a cohort of patients with hypertrophic cardiomyopathy
Chumakova A, Vlasov I, Filatova E et al. · Scientific reports 2025 · PMID 40442228 · doi:10.1038/s41598-025-03226-x
This dataset
Open GSE273325 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 GSE273325_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1141432 and SRA study SRP522827. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40442228) ↗
Study design
54 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 54 sample titles: 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.