immunotype 1.0.4

Peptide-based HLA typing from immunopeptidomics data

Peptide-based HLA typing from immunopeptidomics data

immunotype predicts HLA class I alleles directly from immunopeptidomics data — no separate HLA typing experiment needed. immunotype combines a graph neural network with a curated mono-allelic lookup table in an ensemble model, achieving 87.2% accuracy at protein-level resolution across diverse human tissues.

🚀 Quick Start

Installation

# Core CLI functionality
pip install immunotype

Command Line Interface

 Usage: immunotype [OPTIONS] PEPTIDE_INPUT TYPING_OUTPUT

 Predict HLA typing from immunopeptide sequences.
 This tool uses graph neural networks and lookup tables to predict HLA allele typing from immunopeptidomics data. Provide a peptide input file and optionally customize the HLA alleles to consider.

╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *  PEPTIDE_INPUT       PATH                   TSV input file. Either a single column of peptides or two columns with sample IDs and peptides. [required]                │
│ *  TYPING_OUTPUT       PATH                   Path to save the typing output. [required]                                                                                │
│    --prob-output       PATH                   Save detailed HLA probabilities to specified TSV file.                                                                    │
│    --hla-input         PATH                   Path to the HLA input file containing alleles to consider. [default: <immunotype-package-path>/data/selected_alleles.csv] │
│    --max-n-peptides    INTEGER                Maximum number of peptides to predict at once. [default: 50000]                                                           │
│    --batch-size        INTEGER                How many samples should be predicted simultaneously. [default: 1]                                                         │
│    --prediction-model  [ensemble|gnn|lookup]  Select which model to use. [default: ensemble]                                                                            │
│    --use-gpu                                  Run prediction on GPU instead of CPU.                                                                                     │
│    --version                                  Show the version and exit.                                                                                                │
│    --help                                     Show this message and exit.                                                                                               │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# Basic prediction
immunotype src/immunotype/examples/single_sample_input.tsv test_single_sample_typing.tsv

# With probability details
immunotype src/immunotype/examples/single_sample_input.tsv test_single_sample_typing.tsv --prob-output test_single_sample_probabilities.tsv

# Custom settings
immunotype src/immunotype/examples/single_sample_input.tsv test_single_sample_typing.tsv --batch-size 100 --prediction-model gnn

# Explore all CLI options
immunotype --help

App Interface

Try it online without installing — the hosted app on Hugging Face Spaces. Or run it locally:

# Install with app dependencies
pip install immunotype[app]

# Run the Gradio app
immunotype-app

Python API

import pandas as pd
from immunotype import predict

# Prepare data
peptides = pd.DataFrame({
    'peptide': ['ALDGRETD', 'ASDSGKYL'],
    'sample': ['sample1', 'sample1']
})

alleles = pd.DataFrame({
    'allele': ['HLA-A*02:01', 'HLA-B*07:02', 'HLA-C*07:02']
})

# Make predictions
predictions, typing = predict(
    peptide_df=peptides,
    allele_df=alleles
)

📥 Input Formats

Single-column peptides:

ALDGRETD
ASDSGKYL
AVDPTSGQ

Multi-sample format:

sample	peptide
1	ALDGRETD
1	ASDSGKYL
2	AVDPTSGQ

📤 Output

Typing (default): Top 2 alleles per locus (A, B, C) per sample.

sample	typing
sample_0	HLA-A*32:01;HLA-A*68:01;HLA-B*15:01;HLA-B*44:02;HLA-C*03:03;HLA-C*07:04

Probabilities (--prob-output): Per-allele scores from each prediction mode.

sample	locus	allele	probability_gnn	probability_lookup	probability
sample_0	A	HLA-A*01:01	0.0005	0.0000	0.0003
sample_0	A	HLA-A*02:01	0.0000	0.1945	0.0584

📝 Authors

• Matteo Pilz
• Jonas Scheid

📚 Citation

If you use immunotype in your research, please cite our preprint:

Pilz M, Scheid J, Bauer A, Lemke S, Sachsenberg T, Bauer J, Nelde A, Stadelmaier J, Walter A, Rammensee H-G, Nahnsen S, Kohlbacher O, Walz JS. Deep learning enables direct HLA typing from immunopeptidomics data. bioRxiv (2026). doi:10.64898/2026.04.08.717021

@article{immunotype,
  title   = {Deep learning enables direct HLA typing from immunopeptidomics data},
  author  = {Pilz, Matteo and Scheid, Jonas and Bauer, Alina and Lemke, Steffen and Sachsenberg, Timo and Bauer, Jens and Nelde, Annika and Stadelmaier, Josua and Walter, Axel and Rammensee, Hans-Georg and Nahnsen, Sven and Kohlbacher, Oliver and Walz, Juliane Sarah},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.04.08.717021},
  url     = {https://doi.org/10.64898/2026.04.08.717021}
}