Biomolecular class label assignment

biomolecular-class-label-assignment

Summary

Assign biomolecular class labels (e.g., lipid, peptide, carbohydrate) to unidentified TWIM-MS features based on their physico-chemical properties, without requiring prior feature identification. This enables downstream class-specific collision cross section (CCS) calculations and multi-omic data interpretation.

When to use

You have raw or processed TWIM-MS data with arrival time and m/z values for multiple features, but lack prior structural identification (e.g., from spectral libraries or databases). You need to classify features by biomolecular class to enable class-specific downstream analysis such as CCS calibration or metabolomic/lipidomic interpretation across multiple omic layers.

When NOT to use

• Input features are already structurally identified (e.g., from spectral library matching or high-confidence MS/MS annotation); use those identities directly instead of re-assigning classes.
• Data is not from traveling-wave ion mobility mass spectrometry; MOCCal is specific to TWIM platforms and arrival time / drift time conventions.
• Your goal is to assign specific molecular structures or identities rather than broad biomolecular classes; this skill assigns class labels, not compound identities.

Inputs

• TWIM-MS feature table in RawDT format (raw arrival time and m/z values)
• TWIM-MS feature table in UserDT format (processed calibration data with arrival time and m/z)
• Feature identifiers (one per feature)

Outputs

• Tabular biomolecular class assignment result (one row per feature)
• Feature identifier column
• Assigned class label column (e.g., lipid, peptide, carbohydrate, small molecule)

How to apply

Load experimental TWIM-MS data in either RawDT format (raw arrival times and m/z, requiring DEIMoS pre-processing) or UserDT format (processed calibration data). Execute MOCCal's biomolecular class-assignment algorithm, which internally evaluates physico-chemical properties (derived from m/z, charge state, and ion mobility) of each feature to assign it to a biomolecular class. The algorithm operates without requiring external identifications or spectral library matches. Compile the output into a tabular result with one row per feature, indexed by feature identifier, containing assigned class labels. Validate that all input features received a class assignment and that the assigned classes are appropriate for downstream class-specific CCS calculations.

Related tools

• MOCCal (Core Python application performing biomolecular class assignment via physico-chemical property evaluation on TWIM-MS features) — https://github.com/HinesLab/MOCCal
• DEIMoS (pnnl version 1.3.2) (Required upstream preprocessing tool for RawDT workflow; converts raw TWIM data to calibrated arrival times and m/z) — http://github.com/pnnl/deimos

Evaluation signals

• All input features receive a non-null, non-ambiguous class assignment (coverage = 100%)
• Assigned classes are consistent with expected biomolecular composition of the sample (e.g., lipid-rich sample should have high proportion of lipid-class features)
• Output table has correct structure: one row per input feature, with feature ID and class columns present and non-empty
• Class assignments are stable across repeated runs on the same input data (deterministic execution)
• Downstream class-specific CCS calculations execute successfully using the assigned classes without field-structure errors

Limitations

• Assignment is based on physico-chemical properties (m/z, charge, mobility) rather than structural information; broad class labels do not guarantee specificity within a class (e.g., two structurally distinct lipids may be indistinguishable by class assignment alone).
• TWIM platforms record arrival time (time to detector) rather than drift time (time in cell); MOCCal uses these terms interchangeably for convenience, but the distinction affects interpretation if migrating to other ion mobility platforms.
• No changelog is available for this tool, limiting visibility into past behavior changes or bug fixes.
• Class assignment algorithm specifics are not detailed in the available documentation; validation against independent structural databases is recommended for novel or unexpected classes.

Evidence

• [other] MOCCal performs experimental data biomolecular class assignment as a core functionality alongside CCS calibration and class-specific CCS calculations.: "MOCCal performs experimental data biomolecular class assignment as a core functionality alongside CCS calibration and class-specific CCS calculations."
• [other] Execute MOCCal's biomolecular class-assignment algorithm to classify each feature based on its physico-chemical properties.: "Execute MOCCal's biomolecular class-assignment algorithm to classify each feature based on its physico-chemical properties."
• [intro] MOCCal offers class assignment and CCS calculations without need for identifying the features first: "MOCCal offers class assignment and CCS calculations without need for identifying the features first"
• [other] Load experimental TWIM-MS data (arrival time and m/z values) from RawDT or UserDT input format.: "Load experimental TWIM-MS data (arrival time and m/z values) from RawDT or UserDT input format."
• [other] Compile assigned class labels into a tabular output indexed by feature identifier, with one row per feature and columns for feature ID and assigned class.: "Compile assigned class labels into a tabular output indexed by feature identifier, with one row per feature and columns for feature ID and assigned class."
• [readme] TWIM platforms record the time at which the ion reaches the detector (arrival time) rather than the time an ion spends within the TWIM cell (drift time).: "TWIM platforms record the time at which the ion reaches the detector (arrival time) rather than the time an ion spends within the TWIM cell (drift time)."