HolobiomicsLab 
mass-spectral-data-formatting
Summary
Prepare mass spectral data (peaks, precursor m/z, and optional metadata) into standardized dictionary structures required by entropy-based spectral search algorithms. This skill ensures compatibility with Flash Entropy Search and other entropy similarity computations across diverse input file formats.
When to use
When you have raw mass spectral data in .mgf, .msp, .mzML, or .lbm2 file formats and need to search against a spectral library using entropy similarity or Flash Entropy Search. Also apply this skill before building spectral library indices or computing entropy-based compound identification.
When NOT to use
- Input is already a validated list of spectrum dictionaries with 'precursor_mz' and 'peaks' keys—proceed directly to search or index building.
- Working with data formats that do not encode m/z or intensity information (e.g., aggregated feature tables, quantitation matrices without spectral detail).
- Spectral data that lacks precursor m/z annotation (required for Flash Entropy Search's precursor-based filtering).
Inputs
- Mass spectral data file (.mgf, .msp, .mzML, .lbm2 format)
- Raw m/z and intensity arrays (lists or numpy arrays)
- Precursor m/z value (float)
Outputs
- Formatted spectral library (list of dictionaries with 'id', 'precursor_mz', 'peaks', and optional metadata)
- Query spectrum dictionary (with 'precursor_mz' and 'peaks' keys)
- Validated peak arrays (numpy float32 or list of [m/z, intensity] pairs)
How to apply
Parse the input spectral file (using the Entropy Search GUI or manual parsing in Python/R/C++) to extract peaks as [m/z, intensity] pairs and the precursor m/z value. Construct a dictionary for each spectrum with required keys 'precursor_mz' and 'peaks', plus optional 'id' and arbitrary metadata keys. Store peaks as a list of lists or numpy array (dtype=float32 preferred for performance). Validate that all precursor_mz values are positive floats and peaks are non-negative intensity pairs. This formatting step is mandatory before calling FlashEntropySearch.build_index() or entropy_search.search().
Related tools
- Entropy Search GUI (User-friendly interface to load, parse, and format .mgf, .msp, .mzML, and .lbm2 spectral files for entropy-based library searching) — https://github.com/YuanyueLi/EntropySearch/releases
- MSEntropy (Python package) (Provides programmatic API (FlashEntropySearch class) that accepts pre-formatted spectral dictionaries and builds indices from formatted spectral libraries) — https://github.com/YuanyueLi/MSEntropy
- MS Viewer web app (Real-time visualization and formatting of two MS/MS spectra prior to entropy similarity calculation)
Examples
```python
import numpy as np
from ms_entropy import FlashEntropySearch
spectral_library = [{
"id": "Spectrum_1",
"precursor_mz": 150.0,
"peaks": np.array([[100.0, 1.0], [101.0, 1.0], [103.0, 1.0]], dtype=np.float32)
}]
query_spectrum = {"precursor_mz": 150.0, "peaks": np.array([[100.0, 1.0], [101.0, 1.0], [102.0, 1.0]], dtype=np.float32)}
entropy_search = FlashEntropySearch()
entropy_search.build_index(spectral_library)
results = entropy_search.search(precursor_mz=query_spectrum['precursor_mz'], peaks=query_spectrum['peaks'])
## Evaluation signals
- Each spectrum dictionary contains exactly the keys 'precursor_mz' and 'peaks', plus optional metadata keys; absence of either required key raises an error on build_index() or search().
- Peaks are stored as a 2D array or list of [m/z, intensity] pairs with m/z in ascending order or within expected mass range (e.g., 50–2000 m/z for small molecules).
- Precursor m/z values are positive floats matching the mass of the queried compound; out-of-range or null values should trigger validation warnings.
- When passed to FlashEntropySearch.build_index(), the formatted library is successfully indexed without parsing errors or type mismatches.
- Search results are returned as a dict with keys 'hybrid_search', 'identity_search', 'neutral_loss_search', 'open_search', each containing an array of similarity scores matching the number of spectra in the library.
## Limitations
- The 'precursor_mz' and 'peaks' keys are required; other keys are optional but may affect downstream filtering or metadata propagation. Missing 'precursor_mz' will cause Flash Entropy Search to fail.
- Peak intensity normalization and m/z tolerance are not handled by the formatting step itself; these preprocessing steps must be performed upstream or by the search algorithm.
- File format support (.mgf, .msp, .mzML, .lbm2) is limited to formats recognized by the Entropy Search GUI or custom parsers; non-standard or legacy formats require custom parsing code.
- Large spectral libraries (>100k spectra) may require significant memory when loaded as Python lists of dictionaries; consider streaming or batching for very large datasets.
## Evidence
- [readme] Suppose you have a spectral library, you need to format it like this: [{"id": "Demo spectrum 1", "precursor_mz": 150.0, "peaks": [[100.0, 1.0], [101.0, 1.0], [103.0, 1.0]]} ...]: "Suppose you have a spectral library, you need to format it like this: [{"id": "Demo spectrum 1", "precursor_mz": 150.0, "peaks": [[100.0, 1.0], [101.0, 1.0], [103.0, 1.0]]}]"
- [readme] Note that the `precursor_mz` and `peaks` keys are required, the reset of the keys are optional.: "Note that the `precursor_mz` and `peaks` keys are required, the reset of the keys are optional."
- [readme] To search one spectral file against another spectral file or a spectral library, use the Entropy Search GUI. The GUI supports `.mgf`, `.msp`, `.mzML`, and `.lbm2` file formats.: "The GUI supports `.mgf`, `.msp`, `.mzML`, and `.lbm2` file formats."
- [readme] query_spectrum = {"precursor_mz": 150.0, "peaks": np.array([[100.0, 1.0], [101.0, 1.0], [102.0, 1.0]], dtype=np.float32)}: "query_spectrum = {"precursor_mz": 150.0, "peaks": np.array([[100.0, 1.0], [101.0, 1.0], [102.0, 1.0]], dtype=np.float32)}"
- [other] Load the query spectrum (m/z and intensity arrays) and specify the target mass spectral library using MSEntropy.: "Load the query spectrum (m/z and intensity arrays) and specify the target mass spectral library using MSEntropy."