Chemical Similarity Clustering

chemical-similarity-clustering

Summary

Build a tree of mass-spectrometry features based on predicted molecular substructures to cluster and compare untargeted metabolomic LC-MS/MS profiles by chemical similarity. This enables phylogeny-aware diversity analysis of metabolites within and across samples.

When to use

You have untargeted LC-MS/MS metabolomic data (MS1 features with MS2 fragmentation spectra) preprocessed by MZmine2 into a feature table (BIOM format) and MGF file, and you want to construct a chemically-informed hierarchy of features for alpha/beta-diversity analysis or to group features by predicted molecular substructures rather than by mass or retention time alone.

When NOT to use

• Input is already a phylogenetic tree or feature abundance table without raw MS data; tree construction requires fragmentation spectra and molecular structure prediction.
• MS1 features lack MS2 spectra or have poor-quality fragmentation data; SIRIUS cannot reliably predict molecular formulas/substructures without sufficient spectral signal.
• You need exact molecular identities rather than chemically-informed clustering; q2-qemistree predicts substructure probabilities and performs de-novo annotation, not targeted compound matching.

Inputs

• MGF file (MS1 and MS2 spectra; imported as QIIME 2 MassSpectrometryFeatures artifact)
• Feature abundance table in BIOM format (imported as QIIME 2 FeatureTable[Frequency] artifact)
• SIRIUS installation (>=4.4.29) with path to executable
• Optional: MS2 spectral library matches (FBMN output; FeatureData[Molecules] artifact)

Outputs

• Rooted phylogeny tree (Phylogeny[Rooted]; Newick format)
• Filtered feature abundance table (FeatureTable[Frequency]) with only fingerprint-bearing features
• Feature metadata table (FeatureData[Molecules]) including parent mass, retention time, CSI SMILES, MS2 SMILES, and table origin
• Optional: ClassyFire chemical taxonomy annotations (kingdom, superclass, class, subclass, direct_parent)

How to apply

Preprocess MS data using MZmine2 to generate an MGF file (with both MS1 and MS2 spectra) and a feature abundance table (BIOM format). Import both into QIIME 2 artifact format. Execute the q2-qemistree pipeline: (1) compute fragmentation trees using SIRIUS with user-defined adducts (e.g., [M+H]+) and mass tolerance (typically 15 ppm); (2) rerank molecular formulas using ZODIAC with a confidence threshold (e.g., 0.95); (3) predict molecular substructures (fingerprints) via CSI:FingerID with ppm-max typically set to 20; (4) call make-hierarchy to construct the feature tree from predicted fingerprints, merging feature tables and outputting a rooted phylogeny, hashed feature table, and feature metadata. The tree is built by hierarchical clustering of the 2936 (or 489 if PubChem-filtered) binary molecular properties across all features, with optional filtering to remove features lacking fingerprints. Optionally integrate MS2 spectral library matches (e.g., from GNPS FBMN) to annotate nodes with known structures.

Related tools

• q2-qemistree (orchestrates the complete pipeline: compute-fragmentation-trees, rerank-molecular-formulas, predict-fingerprints, make-hierarchy, and get-classyfire-taxonomy commands) — https://github.com/biocore/q2-qemistree
• SIRIUS (de-novo identification of molecular formulas from fragmentation trees and prediction of molecular substructures via CSI:FingerID) — https://bio.informatik.uni-jena.de/sirius/
• MZmine2 (peak detection and feature extraction from LC-MS/MS raw data; outputs MGF and feature table required as q2-qemistree inputs) — http://mzmine.github.io
• GNPS FBMN (optional: generates MS2 spectral library matches for structural annotation; output can be integrated into make-hierarchy via --i-ms2-matches) — https://gnps.ucsd.edu/ProteoSAFe/index.jsp?params=%7B%22workflow%22:%22FEATURE-BASED-MOLECULAR-NETWORKING%22%7D

Examples

qiime qemistree make-hierarchy --i-csi-results fingerprints.qza --i-feature-tables feature-table.qza --o-tree qemistree.qza --o-feature-table feature-table-hashed.qza --o-feature-data feature-data.qza

Evaluation signals

• Output tree is a valid rooted Newick phylogeny with branch lengths reflecting Tanimoto distance between feature fingerprints; can be visualized in phylogenetic software.
• Filtered feature table contains only features with non-null fingerprints; row count typically 70–90% of input features depending on MS2 spectral quality and SIRIUS success rate.
• Feature metadata table has one row per feature with valid parent_mass, retention_time, and csi_smiles (and ms2_smiles if MS2 matches provided); no missing critical fields.
• Downstream alpha/beta-diversity metrics (Faith PD, Bray-Curtis) computed on the tree and filtered table are consistent with expected metabolomic variability; tree exhibits meaningful chemical groupings visually inspectable via QIIME 2 Emperor or iTol.
• ClassyFire taxonomy output shows >70% of features classified to at least 'class' level; structure_source column indicates source of SMILES (CSI vs. MS2).

Limitations

• SIRIUS predicts molecular substructures for only a subset of features (typically 70–90%), dependent on MS2 spectral quality, sample type, and user-defined tolerances (ppm-max, zodiac-threshold); features without fingerprints are filtered out before tree construction.
• De-novo molecular formula and substructure prediction are probabilistic; predicted structures may not match true chemistry, especially for novel or low-abundance compounds; integration of MS2 spectral library matches (via --i-ms2-matches) improves confidence.
• Requires SIRIUS ≥4.4.29 (older versions incompatible); CSI:FingerID uses PubChem fingerprint database downloaded on 13 August 2017, so newly reported substructures are not recognized.
• Tree construction merges input feature tables; non-unique feature identifiers across datasets may cause collisions; features are automatically hashed to prevent overlap.
• Computationally intensive: fragmentation tree generation and CSI:FingerID prediction require significant Java heap memory (>16 GB recommended) and storage for temporary files; user must provide writable tmpdir with sufficient space.

Evidence

• [readme] A tool to build a tree of mass-spectrometry (LC-MS/MS) features to perform chemically-informed comparison of untargeted metabolomic profiles.: "A tool to build a tree of mass-spectrometry (LC-MS/MS) features to perform chemically-informed comparison of untargeted metabolomic profiles."
• [readme] q2-qemistree uses SIRIUS, a software-framework developed for de-novo identification of metabolites. We use molecular substructures predicted by SIRIUS to build a hierarchy of the MS1 features in a dataset.: "We use molecular substructures predicted by SIRIUS to build a hierarchy of the MS1 features in a dataset."
• [readme] These input files can be obtained following peak detection in MZmine2. To begin this demonstration, create a separate folder to store all the inputs and outputs.: "These input files can be obtained following peak detection in MZmine2."
• [readme] A combined feature table by merging all the input feature tables; MS1 features without fingerprints are filtered out of this feature table. This is done because SIRIUS predicts molecular substructures for a subset of features (typically for 70-90% of all MS1 features) in an experiment.: "SIRIUS predicts molecular substructures for a subset of features (typically for 70-90% of all MS1 features) in an experiment (based on factors such as sample type, the quality MS2 spectra, and"
• [readme] A tree relating the MS1 features in these data based on molecular substructures predicted for MS1 features. This is of type Phylogeny[Rooted]. By default, we retain all fingerprint positions i.e. 2936 molecular properties).: "A tree relating the MS1 features in these data based on molecular substructures predicted for MS1 features. This is of type Phylogeny[Rooted]. By default, we retain all fingerprint positions i.e."
• [readme] These can be used as inputs to perform chemical phylogeny-based alpha-diversity and beta-diversity analyses.: "These can be used as inputs to perform chemical phylogeny-based alpha-diversity and beta-diversity analyses."
• [readme] Qemistree will use ms2_smiles to make chemical taxonomy assignments, when MS2 matches are available for a feature. Otherwise, csi_smiles will be used.: "Qemistree will use ms2_smiles to make chemical taxonomy assignments, when MS2 matches are available for a feature. Otherwise, csi_smiles will be used."