Ion Species Annotation Assignment

Reconstruct the mass-difference open-search step to assign candidate adduct/fragment species

Summary

A refinement step that uses exact mass differences to disambiguate and annotate ion-species relationships identified through temporal correlation in direct-injection mass spectrometry. This skill applies precise mass matching against a reference library of known in-source fragments and adducts to assign chemical identities to feature pairs and quantify mass accuracy.

When to use

After temporal correlation has identified feature pairs with matching intensity profiles across time-resolved MS scans, but before final candidate validation. Apply this skill when you need to distinguish between competing ion-species hypotheses (e.g., [M+O+H]+ vs. [M+H2O+H]+) and assign chemical structure labels to correlated feature pairs in datasets lacking chromatographic separation (e.g., direct-injection DBDI-MS or plasma ionization time-series).

When NOT to use

• Input is already a chromatographically separated dataset (LC-MS/MS); temporal correlation is unnecessary and mass difference refinement may not improve specificity.
• Feature pairs have been identified by methods other than temporal correlation (e.g., isotope pattern matching or spectral similarity alone); this skill is designed specifically to refine correlation-based candidates.
• User-supplied adduct/fragment reference table is unavailable or incomplete; the skill cannot function without domain-specific modification definitions.

Inputs

• Feature pair table (m/z values and indices from temporal correlation step)
• User-definable adduct/fragment reference table (mass-difference library with theoretical Δm/z values and chemical labels)
• Mass spectrometric resolution parameter (ppm tolerance, typically 2 ppm)
• Intensity profile data for the feature pair (optional, for validation)

Outputs

• Refined candidate annotation table (feature pairs, observed Δm/z, matched adduct/fragment labels, mass accuracy in ppm)
• Mass accuracy metrics per annotation
• Filtered feature pairs meeting mass tolerance thresholds

How to apply

Load the correlation-identified feature pairs (m/z values and intensity profiles from the preceding correlation step) and a user-supplied adduct/fragment mass-difference reference table listing known modifications (e.g., [M-H2O+H]+, [M+nO+H]+) with their exact theoretical mass differences. For each feature pair, calculate the observed mass difference (m/z_feature_B − m/z_feature_A). Match the observed mass difference against the reference list using exact mass matching within a user-specified ppm tolerance (typically 2 ppm for high-resolution systems). Assign adduct or fragment labels to feature pairs where a match is found; record the measured mass accuracy (observed Δm/z vs. theoretical value). Output a refined candidate table with feature pair identities, observed mass differences, matched adduct/fragment annotations, and mass accuracy metrics to enable downstream filtering by mass error thresholds.

Related tools

• DBDIpy (Implements the mass-difference refinement step via identify_adducts() function; handles alignment, imputation, correlation, and mass-difference matching for DBDI-MS datasets) — https://github.com/leopold-weidner/DBDIpy
• Python (Core language for executing mass-difference calculations and exact mass matching against reference tables)
• matchms (Integrated spectral processing library used by DBDIpy for data import and preprocessing of .mgf, .mzML, .mzXML formats) — https://github.com/matchms/matchms

Examples

adduct_rule = pd.DataFrame({'deltamz': [47.984744], 'motive': ['O3']}); search_res = dbdi.identify_adducts(df=specs_imputed, masses=feature_mz, custom_adducts=adduct_rule, method='pearson', threshold=0.9, mass_error=2)

Evaluation signals

• All observed mass differences fall within the user-specified ppm tolerance of their matched theoretical values; mass accuracy histogram should be centered near 0 ppm with width ≤ tolerance.
• Feature pairs assigned to different adduct/fragment labels show distinct observed Δm/z values consistent with chemical modification definitions (e.g., [M+O+H]+ pairs have Δm/z ≈ 16 Da, [M+H2O+H]+ pairs ≈ 18 Da).
• No ambiguous assignments: each observed mass difference matches at most one reference adduct/fragment within tolerance; features with mass differences matching multiple entries are flagged or rejected.
• Output table row count equals or is less than input feature pair count (no spurious duplicates introduced).
• Mass accuracy distribution does not show systematic bias (median ppm error close to zero); large positive or negative drift suggests miscalibration or reference table error.

Limitations

• Skill assumes that the reference adduct/fragment library is complete and accurate; missing modification entries will result in false-negative assignments. Users must curate the reference table for their ionization method and analyte class.
• Mass accuracy of assignment is limited by the mass spectrometer resolution and calibration stability; instruments with poor mass accuracy (>5 ppm drift) may fail to match true adducts or accept false positives if ppm tolerance is set too broadly.
• Feature pairs with very small or very large mass differences (e.g., loss of <1 Da or gain of >100 Da) may fall outside typical adduct/fragment definitions and remain unassigned; the skill does not perform de novo discovery of novel modifications.
• This refinement step cannot distinguish between adduct ions that have identical or near-identical mass differences (e.g., [M+O+H]+ and [M+C2H4]+, both ~16 Da); downstream MS2 spectral similarity or chromatographic data is needed for disambiguation.

Evidence

• [other] DBDIpy implements a second refinement step in which exact mass differences are compared against a user-definable list of in-source fragments (e.g., [M-H2O+H]+) and adducts (e.g., [M+nO+H]+) to refine the nature of potential ion-species candidates identified by temporal correlation.: "DBDIpy implements a second refinement step in which exact mass differences are compared against a user-definable list of in-source fragments (e.g., [M-H2O+H]+) and adducts (e.g., [M+nO+H]+)"
• [other] For each feature pair, calculate the observed mass difference (m/z_feature_B − m/z_feature_A). Match the observed mass difference against the reference adduct/fragment list using exact mass matching (within user-specified ppm tolerance).: "For each feature pair, calculate the observed mass difference (m/z_feature_B − m/z_feature_A). Match the observed mass difference against the reference adduct/fragment list using exact mass matching"
• [readme] (exact) mass differences are used to refine the nature of potential candidates.: "(exact) mass differences are used to refine the nature of potential candidates"
• [readme] Mass spectrometric data from direct injection analysis is hard to interpret as missing chromatographic separation complicates identification of fragments and adducts generated during the ionization process: "Mass spectrometric data from direct injection analysis is hard to interpret as missing chromatographic separation complicates identification of fragments and adducts"
• [other] Assign adduct or fragment labels to feature pairs where a match is found, and record the mass accuracy.: "Assign adduct or fragment labels to feature pairs where a match is found, and record the mass accuracy."