SpectraVision-Data

The paper is under review.

[Github Repo] | [Data Collection] | [Models]

Abstract

Converting irregularly spaced mass spectrometry (MS/MS) data into computable representation is a foundational architectural choice that sets the upper bound for both computational efficiency and model accuracy. Existing MS/MS representations for machine-learning (ML) models can be divided into two parts: (1) fine-grained binned vectors that preserve spectra resolution but are sparse; (2) transformer-sequence encoders that can avoid sparse binning but must process variable-length inputs with continuous, shift-sensitive m/z coordinates. Critically, both strategies trade computational efficiency for representation quality, creating significant time and memory bottlenecks. In this paper, we present SpectraVision, which encodes MS/MS spectra into compact image-tensors, preserves high-resolution m/z information and supports efficient learning. Efficacy of these image-tensors to extract spatial features from the spectrum image is demonstrated with a CNN model and a Vision Transformer to validate the ability of image-tensors to preserve long-range spectral dependencies. Both CNN and ViT models then exhibit superior Top-k peptide-retrieval accuracy when compared with binned-Vector and transformer-sequence encoders. An added advantage of SpectraVision based models, necessary for training large models, is usage of 87% fewer parameters (~6.4M versus 51.8M) than binned-vector approach. Zero-shot evaluation on multiple species, non-tryptic peptides, and immunopeptides showcases the robustness of vision-based models to extend and improve ML models for MS-based proteomics.

Dataset

• training_data/: 30 MassiveKB H5 files, 1,938,843 spectra.
• val_data/Massive-KB-v1_val.h5: 107,713 validation spectra.
• inference_data/Massive-KB-v1_test_sorted.h5: 107,713 held-out spectra.
• inference_data/PXD017407_library_from_fdr_peptides_sorted.h5: 2,301 additional spectra.

Each H5 file contains peaks ([N, 200, 2] m/z and intensity), sequence, charge, precursor_mass (precursor m/z), and protein. Peptide sequences can include the lowercase modification codes described in the code repository.

Download

With Python:

python -m pip install huggingface_hub
python - <<'PY'
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="SaeedLab/SpectraVision-Data",
    repo_type="dataset",
    local_dir="SpectraVision-Data",
)
PY

Or with the Hugging Face CLI:

hf download SaeedLab/SpectraVision-Data --repo-type dataset --local-dir SpectraVision-Data

License

This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of this model and its derivatives, which include models trained on outputs from the model or datasets created from the model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.

Cite

The paper is under review. As soon as it is accepted, we will update this section.

If you use Massive-KB data, please also cite:

Wang, Mingxun, Jian Wang, Jeremy Carver, Benjamin S. Pullman, Seong Won Cha, and Nuno Bandeira. Assembling the Community-Scale Discoverable Human Proteome. Cell systems (2018).

If you use PXD017407 data, please also cite:

Stopfer LE, Mesfin JM, Joughin BA, Lauffenburger DA, White FM. Multiplexed relative and absolute quantitative immunopeptidomics reveals MHC I repertoire alterations induced by CDK4/6 inhibition. Nat Commun. 2020 11(1):2760 DOI: 10.1038/s41467-020-16588-9

Contact

For any additional questions or comments, contact Fahad Saeed (fsaeed@fiu.edu).