Fix code, update citation references.

README.md

@@ -14,7 +14,7 @@ tags:
 ## Winnow HeLa Single Shot Probability Calibrator
 
 **Winnow** recalibrates confidence scores and provides FDR control for *de novo* peptide sequencing (DNS) workflows.  
-This repository contains the calibrator trained on HeLa Single Shot data as referenced in our paper: TODO.
+This repository contains the calibrator trained on HeLa Single Shot data as referenced in our paper: [De novo peptide sequencing rescoring and FDR estimation with Winnow](https://arxiv.org/abs/2509.24952).
 
 - Intended inputs: spectrum input data and corresponding MS/MS PSM results produced by InstaNovo
 - Outputs: calibrated per-PSM probabilities in `calibrated_confidence`.
@@ -38,9 +38,10 @@ from winnow.scripts.main import filter_dataset
 from winnow.fdr.nonparametric import NonParametricFDRControl
 
 # 1) Download model files
+helaqc_model = Path("helaqc_model")
 snapshot_download(
     repo_id="InstaDeepAI/winnow-helaqc-model",
-    allow_patterns=["*.pkl"]),
+    allow_patterns=["*.pkl"],
     repo_type="model",
     local_dir=helaqc_model,
 )
@@ -50,8 +51,8 @@ calibrator = ProbabilityCalibrator.load(helaqc_model)
 
 # 3) Load your dataset (InstaNovo-style config)
 dataset = InstaNovoDatasetLoader().load(
-    "path_to_spectrum_data.parquet",
-    "path_to_instanovo_predictions.csv",
+    data_path="path_to_spectrum_data.parquet",
+    predictions_path="path_to_instanovo_predictions.csv",
 )
 dataset = filter_dataset(dataset)  # standard Winnow filtering
 
@@ -118,5 +119,40 @@ winnow predict \
 
 ## Citation
 
-If you use Winnow or this model, please cite:
-TODO
+If you use `winnow` in your research, please cite our preprint: [De novo peptide sequencing rescoring and FDR estimation with Winnow](https://arxiv.org/abs/2509.24952)
+
+```bibtex
+@article{mabona2025novopeptidesequencingrescoring,
+      title={De novo peptide sequencing rescoring and FDR estimation with Winnow},
+      author={Amandla Mabona and Jemma Daniel and Henrik Servais Janssen Knudsen and Rachel Catzel
+      and Kevin Michael Eloff and Erwin M. Schoof and Nicolas Lopez Carranza and Timothy P. Jenkins
+      and Jeroen Van Goey and Konstantinos Kalogeropoulos},
+      year={2025},
+      eprint={2509.24952},
+      archivePrefix={arXiv},
+      primaryClass={q-bio.QM},
+      url={https://arxiv.org/abs/2509.24952},
+}
+```
+
+If you use the `InstaNovo` model to generate predictions, please also cite: [InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments](https://doi.org/10.1038/s42256-025-01019-5)
+
+```bibtex
+@article{eloff_kalogeropoulos_2025_instanovo,
+        title        = {InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale
+                        proteomics experiments},
+        author       = {Eloff, Kevin and Kalogeropoulos, Konstantinos and Mabona, Amandla and Morell,
+                        Oliver and Catzel, Rachel and Rivera-de-Torre, Esperanza and Berg Jespersen,
+                        Jakob and Williams, Wesley and van Beljouw, Sam P. B. and Skwark, Marcin J.
+                        and Laustsen, Andreas Hougaard and Brouns, Stan J. J. and Ljungars,
+                        Anne and Schoof, Erwin M. and Van Goey, Jeroen and auf dem Keller, Ulrich and
+                        Beguir, Karim and Lopez Carranza, Nicolas and Jenkins, Timothy P.},
+        year         = 2025,
+        month        = {Mar},
+        day          = 31,
+        journal      = {Nature Machine Intelligence},
+        doi          = {10.1038/s42256-025-01019-5},
+        issn         = {2522-5839},
+        url          = {https://doi.org/10.1038/s42256-025-01019-5}
+}
+```