← BioTransfer GEO Dataset Finder
GEO series

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens

GSE332840 Homo sapiens (human) RNA-seq (gene expression) 22 samples Submitted 2026/06/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 22 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
22 samples: 22 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
Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigensNature communications · 2026
Summary (written by the submitters)
Accurate predictions of T cell receptor (TCR) specificity remain an important open problem in immunology, with broad implications for vaccine design, optimal immunotherapy, and improved management of autoimmune diseases. However, diversity in peptide antigens and TCR sequences at the level of individual patient repertoires remains a formidable computational challenge.
Read the full summary ↓
Here, we develop a joint experimental and computational approach for predicting the antigen specificity of clinically-derived TCR sequences. Our model is trained on a combination of experimentally pre-identified and in silico-predicted TCR-pMHC structures using AlphaFold3. We apply our structural model in the clinical setting of hematopoietic stem cell transplant (HSCT) and demonstrate that our model is able to effectively discern the specificity of previously unseen donor and patient-derived TCR sequences against tumor associated and viral antigens. Model performance was further enhanced through the integration of sequence-based clustering and structurally diverse training templates. Our results highlight the predictive capabilities of structurally guided machine learning frameworks, trained on a minority test dataset, for antigen specificity prediction on unseen TCR sequences and their potential impact on a wide range of immunological applications.
Published in
Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens
Ghoreyshi ZS, Tubo N, Zammataro L et al. · Nature communications 2026 · PMID 42337259 · doi:10.1038/s41467-026-74236-0
This dataset
Open GSE332840 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 GSE332840_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 PRJNA1469154 and SRA study SRP702726. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42337259) ↗
Study design
22 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 22 sample titles: 22 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
Datasets on a similar topic

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.