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| pretty_name: MSNet Datasets |
| language: en |
| tags: |
| - proteomics |
| - mass-spectrometry |
| - deep-learning |
| - bioinformatics |
| - peptide-identification |
| - license mit |
| - size_categories 500M<n |
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| --- |
| |
| # MSNet Datasets |
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| ## Dataset Description |
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| MSNet Datasets is a large-scale, standardized, and AI-ready collection of mass spectrometry (MS) data designed for machine learning applications in computational proteomics. |
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| Instead of hosting raw data directly, this Hugging Face dataset serves as an **entry point and interface**, providing standardized access to externally hosted MSNet resources. |
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| The dataset addresses key limitations of existing public repositories, such as heterogeneous metadata, inconsistent processing pipelines, and lack of benchmarking standards, by offering a unified and reproducible data representation. |
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| ## Background |
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| Deep learning has become integral to modern proteomics, supporting tasks such as: |
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| * Fragment ion intensity prediction |
| * Retention time (RT) prediction |
| * Peptide–spectrum match (PSM) rescoring |
| * De novo peptide sequencing |
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| Despite the abundance of publicly available MS data, most repositories primarily store raw files with inconsistent metadata and processing standards, making them difficult to use directly in machine learning workflows. |
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| ## Motivation |
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| Current proteomics datasets often suffer from: |
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| * Incomplete or inconsistent metadata |
| * Heterogeneous preprocessing pipelines |
| * Limited diversity in experimental conditions |
| * Lack of standardized benchmarks |
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| MSNet Datasets provides a unified, curated, and ML-ready interface to facilitate reproducible research and fair model evaluation. |
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| ## Data Sources |
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| The dataset is curated from: |
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| * Public proteomics datasets (ProteomeXchange) |
| * Large-scale projects (e.g., π-HuB) |
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| A total of **114 large-scale datasets** are included, covering diverse biological contexts, instrument platforms, and fragmentation strategies. |
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| ## Data Processing |
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| All datasets are systematically reprocessed using a reproducible workflow: |
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| * Metadata standardized using SDRF |
| * Uniform reanalysis of raw MS data |
| * Peptide-spectrum match (PSM) generation |
| * Peak annotation including: |
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| * b⁺, b²⁺, y⁺, y²⁺ ions |
| * With and without neutral losses |
| * Multiple mass tolerance settings applied during annotation |
| * Harmonization into a unified structure |
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| ## Data Format |
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| Processed data are stored in **Parquet format**, enabling: |
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| * Efficient storage and compression |
| * Fast I/O for large-scale data |
| * Compatibility with PyTorch, TensorFlow, and other ML frameworks |
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| ## Dataset Structure |
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| Each entry corresponds to a peptide-spectrum match (PSM) and includes: |
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| | Column | Description | |
| | --------------- | -------------------------------- | |
| | spectrum_id | Spectrum identifier | |
| | mz_array | m/z values | |
| | intensity_array | Intensity values | |
| | precursor_mz | Precursor m/z | |
| | charge | Charge state | |
| | peptide | Peptide sequence | |
| | modifications | Post-translational modifications | |
| | rt | Retention time | |
| | instrument | Instrument type | |
| | fragmentation | Fragmentation method | |
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| ## Access |
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| ⚠️ **Note:** Hugging Face does **not host the raw data**. |
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| Instead, data can be accessed through the following official resources: |
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| * **Web portal:** https://msnet.ncpsb.org.cn or https://quantms.org/datasets |
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| ## Official Loader |
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| We provide an official data loader for seamless integration: |
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| 👉 https://github.com/PHOENIXcenter/pi-MSnet |
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| Supports: |
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| * PyTorch |
| * TensorFlow |
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| ## Usage |
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| ```python |
| from datasets import load_dataset |
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| # This dataset provides metadata / interface only |
| dataset = load_dataset("your-username/msnet") |
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| print(dataset) |
| ``` |
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| For full data access, please use the official loader. |
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| ## Use Cases |
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| * Training deep learning models for proteomics |
| * PSM rescoring and confidence estimation |
| * De novo peptide sequencing |
| * Retention time and intensity prediction |
| * Benchmarking computational methods |
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| ## Next step |
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| * PTM coverage is continuously expanding |
| * Some modalities (e.g., DIA, XL-MS) are fully integrated |
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| ## Citation |
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| If you use MSNet Datasets, please cite the corresponding π-MSNet publication. |
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| ## License |
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| MIT License |
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