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Ordering molecular diversity in untargeted metabolomics via molecular community networking.
Coler, Elizabeth A; Melnik, Alexey; Lotfi, Ali; Moradi, Dana; Ahiadu, Ben; Gomes, Paulo Wender Portal; Patan, Abubaker; Dorrestein, Pieter C; Barnes, Stephen; Boginski, Vladimir; Semenov, Alexander; Aksenov, Alexander A.
Afiliação
  • Coler EA; Department of Chemistry, University of Connecticut, Storrs, CT, USA.
  • Melnik A; Department of Chemistry, University of Connecticut, Storrs, CT, USA.
  • Lotfi A; Arome Science Inc., Farmington, CT, USA.
  • Moradi D; Department of Chemistry, University of Connecticut, Storrs, CT, USA.
  • Ahiadu B; Department of Chemistry, University of Connecticut, Storrs, CT, USA.
  • Gomes PWP; BileOmix Inc, Farmington, CT, USA.
  • Patan A; Collaborative Mass Spectrometry innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, CA, USA.
  • Dorrestein PC; Collaborative Mass Spectrometry innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, CA, USA.
  • Barnes S; Collaborative Mass Spectrometry innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, CA, USA.
  • Boginski V; Department of Pharmacology and Toxicology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
  • Semenov A; Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL, USA.
  • Aksenov AA; Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA.
bioRxiv ; 2024 Aug 07.
Article em En | MEDLINE | ID: mdl-39131284
ABSTRACT
Nature's molecular diversity is not random but displays intricate organization stemming from biological necessity. Molecular networking connects metabolites with structural similarity, enabling molecular discoveries from mass spectrometry data using arbitrary similarity thresholds that can fracture natural metabolite families. We present molecular community networking (MCN), that optimizes connectivity for each metabolite, rescuing lost relationships and capturing otherwise "hidden" metabolite connections. Using MCN, we demonstrate the discovery of novel dipeptide-conjugated bile acids.

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos