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<p>Searching and learning from aggregated public metabolomics data spanning thousands of studies remained largely inaccessible. Here we present StructureMASST, a web-based application enabling scalable, structure-centric searches across public metabolomics repositories using molecule names or chemical representations. It queries a precomputed knowledgebase of 2.19 billion spectral matches and 420 million metadata links, supports modification-tolerant and mass-shift searches, and maps chemical structures across taxonomy, biological context and environmental conditions to accelerate discovery.</p>

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Structure-centric searching enables global mapping of the public metabolome

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

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  • Yasin El Abiead,
  • Jeong In Seo,
  • Vincent Charron-Lamoureux,
  • Michael Strobel,
  • Wilhan Donizete Gonçalves Nunes,
  • Haoqi Nina Zhao,
  • Kine Eide Kvitne,
  • Simone Zuffa,
  • Helena Mannochio-Russo,
  • Harsha Gouda,
  • Cristina Bez,
  • Abubaker Patan,
  • Shipei Xing,
  • Jasmine Zemlin,
  • Ipsita Mohany,
  • Julius Agongo,
  • Andres Mauricio Caraballo Rodriguez,
  • Lindsey A. Burnett,
  • Victoria Deleray,
  • Abzer K. Pakkir Shah,
  • Jarmo-Charles Kalinski,
  • Daniel Petras,
  • Nikiforos Alygizakis,
  • Jeremy Carver,
  • Ozgur Yurekten,
  • Thomas Payne,
  • Eoin Fahy,
  • Shankar Subramaniam,
  • Juan Antonio Vizcaíno,
  • Mingxun Wang,
  • Pieter C. Dorrestein

摘要

Searching and learning from aggregated public metabolomics data spanning thousands of studies remained largely inaccessible. Here we present StructureMASST, a web-based application enabling scalable, structure-centric searches across public metabolomics repositories using molecule names or chemical representations. It queries a precomputed knowledgebase of 2.19 billion spectral matches and 420 million metadata links, supports modification-tolerant and mass-shift searches, and maps chemical structures across taxonomy, biological context and environmental conditions to accelerate discovery.