Fragmentation Pattern Annotation

fragmentation-pattern-annotation

Summary

Annotate unknown MS/MS spectra by matching their fragmentation patterns (m/z and intensity pairs) against spectral libraries using similarity scoring algorithms. This enables compound identification in metabolomics and pharmaceutical screening when spectra are matched to reference libraries with known metadata.

When to use

When you have an experimental MS/MS spectrum (query spectrum as m/z–intensity pairs) and need to identify the compound by comparing its fragmentation pattern to a spectral library. Triggered by: precursor m/z is known, MS/MS fragmentation data is available in mzML/mzXML format or as extracted m/z–intensity matrix, and a reference library (local or public: GNPS, MASSBANK, DrugBANK) is accessible. Essential when confidentiality or data governance prevents use of public-only databases.

When NOT to use

• Query spectrum is from negative-ion or mixed-polarity mode and library is positive-ion only (e.g., pre-compiled GNPS_MASSBANK_PROCESSED_POS_CONSENSUS contains 'all in positive ion mode').
• Raw data is in vendor-proprietary format (Thermo, Waters, Bruker) without prior conversion to mzML/mzXML format.
• No suitable reference library is available or library contains insufficient spectral diversity for the compound class of interest.

Inputs

• Query MS/MS spectrum as two-column matrix (m/z, intensity pairs)
• Precursor m/z value (float, monoisotopic mass)
• Reference spectral library in GNPS format or mzML/mzXML raw chromatogram files
• Library search parameters: polarity, similarity method, min_frag_match, min_score threshold

Outputs

• Ranked list of candidate library matches with similarity scores (Cosine or dot-product)
• Annotated spectrum metadata: compound name, molecular formula, INCHI, INCHIKEY, library accession
• Mirror plot visualization of query spectrum vs. best-matching library spectrum
• Match confidence score and number of matching fragments

How to apply

Load or download a reference spectral library (e.g., GNPS-style local library or pre-compiled GNPS_MASSBANK_PROCESSED_POS_CONSENSUS database). Read the experimental query spectrum as a two-column matrix (m/z, intensity). Collect query parameters including precursor m/z (prec_mz), polarity, similarity method (typically Cosine), minimum fragment matches (min_frag_match, often ≥6), and minimum similarity score threshold (min_score, typically ≥0.6–0.7 for Cosine). Execute library search using cosine similarity or spectral dot-product to compute match scores between query and all candidate library spectra. Rank candidates by similarity score, filter by threshold and precursor m/z match (use_prec=TRUE for classical approach), and retrieve top-ranked hits with associated compound metadata (name, molecular formula, INCHI, accession). Validate results by visual inspection (mirror plot) and INCHIKEY confirmation against expected compound.

Related tools

• MergeION2 (R package that implements library search algorithms (Cosine similarity, spectral dot-product) and provides library_query() function for spectrum matching, visualization (library_visualizer), and compound annotation) — https://github.com/daniellyz/MergeION2
• GNPS (Public spectral library source; MergeION merges data into GNPS-style format and queries GNPS database for annotation)
• MASSBANK (Public spectral database source for library search; merged into pre-compiled reference libraries)
• DrugBANK (Public pharmaceutical structure database queried for drug compound annotation)

Examples

query.sp = read.csv('example_cinnarizine.txt', header = F, sep = '\t'); params.query.sp = list(prec_mz = 369.232, use_prec = T, polarity = 'Positive', method = 'Cosine', min_frag_match = 6, min_score = 0); search_result = library_query(input_library = library1c, query_spectrum = query.sp, params.query.sp = params.query.sp)

Evaluation signals

• Cosine similarity score is ≥0.7 (or user-defined min_score threshold) for top-ranked match, indicating high spectral concordance.
• Precursor m/z of matched library spectrum matches query precursor m/z within instrument tolerance (typically <5 ppm).
• INCHIKEY and molecular formula of top match correspond to known compound structure or expected metabolite/drug.
• Mirror plot visualization shows significant peak overlap and fragment ion matching between query and library spectra.
• Number of matching fragments (min_frag_match) exceeds threshold, typically ≥6 major peaks aligned between spectra.

Limitations

• Annotation accuracy depends on spectral library quality, coverage, and metadata completeness; confidential in-house databases may lack sufficient reference spectra.
• Fragmentation patterns vary with ionization mode, collision energy, and instrument type; library must be acquired under comparable conditions to query spectrum.
• Isomeric compounds often produce similar fragmentation patterns, leading to ambiguous or incorrect matches; visual inspection and retention time or additional orthogonal data needed for confirmation.
• Pre-compiled spectral databases are currently restricted to positive-ion mode ESI-MS/MS; negative-ion queries require local library building from appropriate reference standards.

Evidence

• [intro] Several library search algorithms are available, allowing users to search and annotate an unknown spectrum in their local database or public databases: "Several library search algorithms are available, allowing users to search and annotate an unknown spectrum in their local database or public databases (i.e. drug structures in GNPS, MASSBANK and"
• [other] Library search algorithm computes similarity scores using cosine similarity or spectral dot-product between query and candidate spectra: "Apply library search algorithm to compute similarity scores (e.g., cosine similarity or spectral dot-product) between query spectrum and candidate library spectra"
• [other] Matched spectra are ranked by similarity threshold and annotated with metadata: "Rank and filter matched spectra by similarity threshold to identify best-matching library entries. 5. Retrieve and annotate matched spectra with metadata (compound name, molecular formula, INCHI,"
• [readme] Pre-compiled database contains 11,642 metabolites and is positive-ion mode only: "We have pre-compiled a small molecule spectral database containing MS/MS spectra of 11,642 metabolites, natural products and drugs. The database combines public repositories such as GNPS, MassBank"
• [readme] Precursor m/z and use_prec parameter control matching strategy: "Only important parameters to check are the prec_mz, which indicates the precursor mass, and use_prec, which forces precursor mass match in the search output by setting to TRUE (the classical"
• [readme] Query spectrum should be read as a two-column matrix: "The MS/MS spectrum should be read as well into the R environment as a two-column matrix"