SIMILE enables alignment of tandem mass spectra with statistical significance.
Daniel G C TreenMingxun WangShipei XingKatherine B LouieTao HuanPieter C DorresteinTrent R NorthenBenjamin P BowenPublished in: Nature communications (2022)
Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking.
Keyphrases
- tandem mass spectrometry
- liquid chromatography
- ultra high performance liquid chromatography
- mass spectrometry
- high performance liquid chromatography
- machine learning
- ms ms
- optical coherence tomography
- density functional theory
- simultaneous determination
- gas chromatography
- deep learning
- high resolution mass spectrometry
- quantum dots
- solid phase extraction
- high resolution
- liquid chromatography tandem mass spectrometry
- single molecule
- resting state
- magnetic resonance
- functional connectivity
- dual energy
- molecular dynamics