Predict glycan structures from mass spectrometry glycomics data. Beat our deep learning model (CandyCrunch) or provide expert manual annotations.
Given Excel files containing LC-MS/MS glycomics runs (thousands of spectra per file), predict the glycan structure for each detected peak (Alternatively, raw files are available here: XXX). Input files contain m/z, retention time, fragmentation peak dictionaries, and intensity data. Your job is to output glycan structures in (preferably) IUPAC-condensed notation, as well as where they're found (m/z + retention time).
Your predictions are matched to ground truth spectra using mass (±0.5 Da) and retention time (±1.0 min) tolerance. Scoring uses a soft F1 metric where exact structural matches get 1.0 and partial matches get cosine similarity based on motif fingerprints. False positives and false negatives are penalized. See evaluation/evaluate_submission.py for the exact implementation.
Training: Full annotated dataset at https://zenodo.org/records/10997110 (multiple glycomics runs, hundreds of thousands of annotated spectra)
Public Test: Files in data/public_test/ with ground truths as _solution.csv files
Private Test: Hidden, scored only at competition end. Files are named in the format ID_GlycanClass_GlycanDerivatization.xlsx
Your predictions must be Excel files matching the input filenames, with this exact structure:
| Column | Type | Description |
|---|---|---|
| m/z | float | Observed mass-to-charge ratio |
| charge | int | Signed charge (e.g., -1 for negative mode) |
| RT | float | Retention time in minutes |
| top1_pred | str | Predicted glycan in IUPAC-condensed notation |
Index should be integer row numbers. Additional columns (confidence scores, alternative predictions) are allowed but ignored.
Example row:
m/z: 1235.19, charge: -1, RT: 17.63, top1_pred: Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc
- Prepare your prediction CSV files following the format above
- Validate locally:
python validation/check_format.py your_predictions/ - Go to Issues and select "Submit Predictions"
- Enter your GitHub username and attach your CSV files
- Submit the issue
A bot will automatically create a PR, run evaluation, and update the leaderboard. Check the issue for status updates.
CandyCrunch (our model) achieves F1=0.XX on public test. Code: https://github.com/BojarLab/CandyCrunch
To generate baseline predictions:
from candycrunch.prediction import wrap_inference
preds = wrap_inference("data/public_test/example.xlsx", glycan_class='N')
preds.to_csv("submissions/baseline/public/example_submission.csv")See leaderboard/public.md for current rankings on public test set.
Final rankings on private test set will be revealed after competition closes on [DATE].
You don't need code. Annotate spectra in Excel, format as above, submit. Many of the best glycomics annotations come from expert knowledge, not algorithms.
Open an issue or see the full evaluation code in evaluation/.