Dataset object serialization and deserialization

dataset-object-serialization-and-deserialization

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Summary

Convert mass spectrometry data from multiple input identifier formats (Task ID, USI, FBMN) into standardized in-memory dataset objects, and persist them as JSON for downstream visualization and analysis. This skill bridges disparate data entry points with a unified internal representation.

When to use

Apply this skill when you have mass spectrometry data arriving through heterogeneous input formats (Task ID from GNPS, Universal Spectrum Identifiers, or Feature-Based Molecular Networking identifiers) and need to load, validate, and store them as a single standardized dataset object for interactive peak alignment visualization and filtering.

When NOT to use

• Input data is already a deserialized in-memory dataset object (skip loader routing; proceed directly to validation)
• Mass spectrometry data is in a format not supported by Task ID, USI, or FBMN loaders (e.g., raw mzML or mzXML files without a GNPS Task ID wrapper)
• The identifier format cannot be reliably parsed or validated against expected patterns (malformed USI, invalid Task ID)

Inputs

• GNPS Task ID string (format: task ID and component number)
• Universal Spectrum Identifier (USI) list (format: mzspec:GNPS2:TASK-<task-id>-nf_output/clustering/specs_ms.mgf:scan:<scan-number>)
• FBMN Task ID and component numbers
• Remote API endpoints (GNPS API, GNPS2 spectra service)

Outputs

• Standardized dataset object (in-memory Python dict/object with aligned peaks, m/z, intensity, scan metadata)
• JSON serialization of dataset (written to ./temp/sets/<session-id>.json)
• Validated dataset object ready for peak alignment visualization

How to apply

First, parse and validate the incoming identifier to determine its format (Task ID, USI, or FBMN). Route the validated identifier to the appropriate loader function: the Task ID loader calls the GNPS API to fetch molecular networking results, the USI loader retrieves individual spectra from the GNPS2 infrastructure, or the FBMN loader accesses feature-based networking clusters. Execute the selected loader to deserialize the remote or cached mass spectrometry data into a standardized dataset object containing aligned peaks, m/z values, and intensity metadata. Validate the loaded object for completeness (presence of required fields: peaks, m/z ranges, intensity values, and spectrum identifiers). Serialize the validated dataset to JSON format in a temporary storage location (SETS_TEMP_PATH) for session-specific retrieval and rendering by the visualization interface.

Related tools

• Flask (Web framework routing multi-format input identifiers to appropriate loaders)
• usi.py (USI parsing and spectrum retrieval utilities for Universal Spectrum Identifier deserialization) — github.com/Wang-Bioinformatics-Lab/NetworkFamily_MultipleAlignment_Website
• alignment.py (Core module for dataset object construction and peak alignment data structure validation) — github.com/Wang-Bioinformatics-Lab/NetworkFamily_MultipleAlignment_Website
• dash_get_sets.py (Data input interface handling identifier ingestion and triggering loader selection) — github.com/Wang-Bioinformatics-Lab/NetworkFamily_MultipleAlignment_Website
• pandas (Data manipulation for peak alignment tabulation and intensity metadata normalization)
• requests (HTTP client for remote GNPS API and GNPS2 spectrum service calls during loader execution)

Evaluation signals

• Loaded dataset object contains non-empty 'peaks' array with m/z and intensity tuples for each spectrum
• All required metadata fields are present: spectrum identifiers (scan numbers or USI), precursor m/z, and alignment set memberships
• JSON serialization round-trip: deserialize the written JSON file and verify that the reconstructed object matches the original in-memory object (field-by-field equality)
• Peak m/z values are within physically plausible range (typically 50–2000 m/z for small molecules; application-dependent) and intensities are non-negative
• Spectrum ordering and alignment set assignments remain consistent across loaders for the same underlying data

Limitations

• Loader routing is identifier-format dependent; if an identifier does not match Task ID, USI, or FBMN patterns, routing fails and no dataset object is created
• Remote API calls (GNPS, GNPS2) may fail or timeout if the external service is unavailable; no fallback deserialization from local cache is documented
• JSON serialization does not preserve Python object types (e.g., NumPy arrays, custom classes); deserialized objects may require type coercion before downstream analysis
• No changelog documented; version compatibility between loader implementations and remote API schemas is not tracked

Evidence

• [other] Execute the selected loader to retrieve and deserialize the mass spectrometry data into a standardized dataset object.: "Execute the selected loader to retrieve and deserialize the mass spectrometry data into a standardized dataset object."
• [other] Route to the appropriate loader: Task ID loader, USI loader, or FBMN loader based on identifier classification.: "Route to the appropriate loader: Task ID loader, USI loader, or FBMN loader based on identifier classification."
• [intro] users to input mass spectrometry data through various formats (Task ID, USI, or FBMN): "users to input mass spectrometry data through various formats (Task ID, USI, or FBMN)"
• [readme] Click 'Process and Save JSON' to generate alignment data: "Click "Process and Save JSON" to generate alignment data"
• [readme] SETS_TEMP_PATH: Temporary storage for processed JSON files (./temp/sets): "SETS_TEMP_PATH: Temporary storage for processed JSON files (./temp/sets)"
• [readme] Each USI should follow the format: mzspec:GNPS2:TASK-<task-id>-nf_output/clustering/specs_ms.mgf:scan:<scan-number>: "Each USI should follow the format: mzspec:GNPS2:TASK-<task-id>-nf_output/clustering/specs_ms.mgf:scan:<scan-number>"