DarkSpec

DarkSpec is a curated collection of 4.5 million unlabeled tandem mass spectra selected from PRIDE for semi-supervised de novo peptide sequencing. It provides quality-controlled spectra that can be used without peptide identification labels.

DarkSpec accompanies SemiNovo, a framework for learning de novo sequencing models from labeled and unlabeled spectra.

Dataset summary

Property	Value
Number of spectra	4,500,000
Peaks per spectrum	150
Spectrum dtype	float32
Precursor charge range	1-10
Peptide labels	None
Peak-array size	Approximately 5.4 GB

The dataset contains no peptide sequences, modified sequences, protein accessions, database-search scores, or other identification labels.

Files and schema

DarkSpec/
├── manifest.json
├── precursor.npy
└── spectra.npy

File	Shape	Dtype	Description
spectra.npy	(4,500,000, 150, 2)	float32	Fragment m/z and normalized intensity
precursor.npy	(4,500,000, 2)	float32	Precursor m/z and precursor charge
manifest.json	-	JSON	Shape and preprocessing metadata

For spectra.npy, spectra[i, :, 0] stores m/z values and spectra[i, :, 1] stores intensities. Spectra with fewer than 150 retained peaks are zero padded.

Download

pip install -U huggingface_hub
hf download PanLiu/DarkSpec \
  --repo-type dataset \
  --local-dir DarkSpec

Loading

Memory mapping is recommended:

import json
import numpy as np

root = "DarkSpec"
spectra = np.load(f"{root}/spectra.npy", mmap_mode="r")
precursors = np.load(f"{root}/precursor.npy", mmap_mode="r")

with open(f"{root}/manifest.json") as handle:
    manifest = json.load(handle)

mz = spectra[0, :, 0]
intensity = spectra[0, :, 1]
precursor_mz, precursor_charge = precursors[0]

print(spectra.shape)
print(precursors.shape)
print(manifest)

Preprocessing

The released array store applies the following fixed preprocessing:

• precursor charge between 1 and 10;
• fragment m/z between 50.52564895 and 2500 Da;
• peaks within 2 Da of precursor m/z removed;
• relative intensity threshold of 0.01;
• at least 20 valid peaks required;
• at most 150 peaks retained;
• retained peaks sorted by m/z;
• duplicate-like spectra reduced using 32-bit spectral SimHash grouping.

The exact frozen preprocessing metadata is also stored in manifest.json.

Intended use

DarkSpec is intended for:

• semi-supervised de novo peptide sequencing;
• self-supervised spectrum representation learning;
• robustness and domain-shift studies for tandem mass spectra;
• reproducible benchmarking of unlabeled-spectrum learning methods.

It is not intended to provide peptide identifications or to replace database-search validation.

Limitations

• DarkSpec is unlabeled, so individual peptide identities are unknown.
• PRIDE acquisition protocols, instruments, collision settings, and biological sources are heterogeneous.
• Quality filtering reduces obvious invalid spectra but does not guarantee that every retained spectrum is identifiable.
• Models trained on DarkSpec should still be evaluated on held-out labeled benchmarks.

Code

Training and evaluation code is available at:

https://github.com/grandOrgan/Seminovo

License

The released dataset package is provided under the MIT License. Users remain responsible for following applicable terms associated with the original PRIDE source projects.