Code and Datasets for MassNet: Database Search Workflows, Retention Time Prediction, and PSM Rescoring

This repository compiles the core resources used to construct the MassNet dataset, including:

1) FASTA sequence files for each species, used for database searching;

2) Standardized database search workflows based on FragPipe and Sage engines for unified processing of raw DDA-MS data and high-confidence peptide identification.

Additionally, the repository provides the following data resources and supporting tools for downstream AI tasks:

1) Retention time (RT) prediction task: training and validation datasets constructed from FragPipe and Sage results, along with corresponding RT prediction model outputs;

2) Peptide-spectrum match (PSM) rescoring task: PSM datasets for training and evaluation results;

Dataset construction tools: complete code and documentation for generating the above task-specific datasets.

For detailed model training procedures and usage instructions, please refer to the following official repositories:
DeepLC: https://github.com/CompOmics/DeepLC
DDA-BERT: https://github.com/guomics-lab/DDA-BERT

All resources provided in this repository enable full reproduction of the core experimental and analytical results reported in the manuscript "MassNet: billion-scale AI-ready mass spectrometry corpus enabling scalable deep Learning in proteomics".