Dark Matter
Map structural relationships between named and unnamed features across your samples
Built for the samples that are hardest to identify
Plant extracts are dense with structurally related compounds — exactly where spectral networking earns its keep, propagating a single confirmed identity across a whole family of related features.
Read the methodology
Connect the dots in your data.
A single identified compound rarely travels alone. Spectral networking links features that share structural motifs, adduct variants, or common fragmentation pathways — turning isolated annotations into a connected molecular map of your sample.
When one node in the network earns an MSI Level 1a identification, its neighbours inherit structural context. Unknown features become “dark matter” with a family name, narrowing the search space for subsequent annotation.
From spectra to molecular families.
Spectral networking builds connections that survive polarity, adduct form, and instrument variation — because they are grounded in physical measurement.
Cosine Similarity
The MS² spectrum of each feature is compared pairwise across the dataset. A cosine score ≥ 0.85 between two spectra indicates they share a common substructure. VeroMass computes similarity in both ESI+ and ESI− independently, capturing complementary views of the same molecule.
Molecular Families
Connected components in the similarity graph form molecular families: related glycosides, acylated derivatives, or oxidation products that differ by a known neutral loss. A single Level 1a assignment propagates structural knowledge to every member of the family.
Neutral Loss Propagation
Network edges are annotated with the mass difference between connected nodes. Neutral losses of 162.053 Da (hexose), 146.058 Da (deoxyhexose), and 176.032 Da (glucuronide) are automatically flagged — turning a collision cell into a structural classifier.
Annotating the unknown.
In any untargeted metabolomics experiment, the majority of detected features have no match in any reference library. Spectral networking gives these “dark matter” features a context that in-silico tools cannot provide.
When an unknown feature shares high cosine similarity with a Level 1a node and exhibits a consistent mass shift, it inherits the structural class of its neighbour. The annotation may not reach Level 1a, but it moves from “unknown” to “likely flavonol diglycoside” or “probable hydroxycinnamate ester” — a hypothesis that can be tested.
Propagation rules
Cosine ≥ 0.85 + consistent mass shift ≤ 5 ppm + RT correlation → structural family assignment.
Confidence scoring
Each propagated annotation carries a confidence score derived from the distance to the anchor node and the number of independent edges supporting the connection.
Interactive network graphs.
The network is not a static diagram. Every node, edge, and annotation layer is inspectable, filterable, and connected to the underlying spectral evidence.
Force-Directed Layout
Nodes repel and edges attract — the graph automatically organises by structural similarity. Move, pin, and zoom any node. Layout parameters are adjustable for dense or sparse datasets.
Multi-Layer Views
Toggle between ESI+ and ESI− networks, overlay retention time gradients, colour by compound class, and filter by cosine threshold — all without regenerating the graph.
Selection to Evidence
Click any node to pull up its MS¹ and MS² spectra, retention time, adduct series, and gate status. Click an edge to see the cosine alignment between the two spectra side by side.
Works with your existing tools.
Spectral networking is not a silo. The network graph, annotations, and edge tables are exportable in formats that feed directly into downstream analysis.
Export to Cytoscape / Gephi
Download the network as GraphML or CSV edge tables for further exploration in network analysis platforms.
Batch API
Submit a full sample cohort, retrieve the network as JSON, and incorporate the family assignments into your existing Python or R workflow.
GNPS Compatibility
Export networks in GNPS-compatible format for cross-platform validation and public repository deposition (MassIVE).
Turn unknowns into families.
Bring your raw LC-MS/MS runs into VeroMass and see the network form around every confirmed identification. Anonymous nodes become structural neighbours, and the dark matter in your sample starts to take shape.