Evidence map›Paper›PMID 39304763›Full record

ArticleNature protocols2025

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

Abzer K Pakkir Shah, Axel Walter, Filip Ottosson, Francesco Russo, Marcelo Navarro-Diaz, Judith Boldt, Jarmo-Charles J Kalinski, Eftychia Eva Kontou, James Elofson, Alexandros Polyzois and 47 more

Abstract read
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In one paragraph

Article in Nature protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed.

  1. Article
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  3. Molecules (Basel, Switzerland) · 2026
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  7. Article
  8. A frameshift mutation drives divergent biosynthesis of metallophores inbioRxiv : the preprint server for biology · 2026
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  20. Cooperative siderophore use stabilizes a protective leaf microbiome.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

57 authors.

Abzer K Pakkir ShahVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Axel WalterVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Filip OttossonSection for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark.
Francesco RussoSection for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark.
Marcelo Navarro-DiazUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Judith BoldtVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.ORCID 0000-0002-4598-634X
Jarmo-Charles J KalinskiVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Eftychia Eva KontouVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
James ElofsonDepartment of Chemistry and Biochemistry, University of Denver, Denver, CO, USA.
Alexandros PolyzoisVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Carolina González-MarínVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Shane FarrellBigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA.
Marie R AggerbeckVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Thapanee PruksatrakulVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Nathan ChanDepartment of Computer Science, University of California Riverside, Riverside, CA, USA.
Yunshu WangDepartment of Computer Science, University of California Riverside, Riverside, CA, USA.
Magdalena PöchhackerVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.ORCID 0009-0007-4594-088X
Corinna BrungsInstitute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
Beatriz CámaraLaboratorio de Microbiología Molecular y Biotecnología Ambiental, Centro de Biotecnología DAL, Universidad Técnica Federico Santa María, Valparaíso, Chile.
Andrés Mauricio Caraballo-RodríguezSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
Andres CumsilleLaboratorio de Microbiología Molecular y Biotecnología Ambiental, Centro de Biotecnología DAL, Universidad Técnica Federico Santa María, Valparaíso, Chile.ORCID 0000-0003-2087-7435
Fernanda de OliveiraSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
Kai DührkopDepartment of Bioinformatics, University of Jena, Jena, Germany.
Yasin El AbieadSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
Christian GeibelUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Lana G GravesDepartment of Environmental Systems Analysis, University of Tübingen, Tübingen, Germany.
Martin HansenDepartment of Environmental Science, Aarhus University, Roskilde, Denmark.ORCID 0000-0002-4663-8742
Steffen HeuckerothInstitute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.ORCID 0000-0002-4928-2526
Simon KnoblauchUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.ORCID 0009-0000-0739-9550
Anastasiia KostenkoDepartment of Chemistry and Biochemistry, University of Denver, Denver, CO, USA.
Mirte C M KuijpersDepartment of Ecology, Behavior and Evolution, University of California San Diego, San Diego, CA, USA.ORCID 0000-0002-5409-2129
Kevin MildauVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Stilianos Papadopoulos LambidisUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Paulo Wender Portal GomesSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.ORCID 0000-0001-5478-2448
Tilman SchrammUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Karoline Steuer-LoddUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Paolo StinconeUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Sibgha TayyabUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Giovanni Andrea VitaleUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Berenike C WagnerUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Shipei XingSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
Marquis T YazzieDepartment of Chemistry and Biochemistry, University of Denver, Denver, CO, USA.
Simone ZuffaSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.ORCID 0000-0001-7237-3402
Martinus de KruijffHelmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research, Saarbrücken, Germany.ORCID 0009-0007-3189-5686
Christine BeemelmannsHelmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research, Saarbrücken, Germany.ORCID 0000-0002-9747-3423
Hannes LinkUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Christoph MayerUniversity of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Justin J J van der HooftVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.ORCID 0000-0002-9340-5511
Tito DamianiInstitute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
Tomáš PluskalInstitute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.ORCID 0000-0002-6940-3006
Pieter DorresteinSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
Jan StanstrupDepartment of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg C, Denmark.
Robin SchmidVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.ORCID 0000-0003-0922-3887
Mingxun WangVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Allegra AronVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
Madeleine ErnstSection for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark. MAET@ssi.dk.
Daniel PetrasVirtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA. dpetras@ucr.edu.ORCID 0000-0002-6561-3022

Funding

Collaborative Microbial Metabolite CenterU24DK133658 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PIETER C DORRESTEIN · 2022 to 2026
$2.9M
Methods development and secondary analyses for an oral clefts association studyR03DE021437 · NIDCR · JOHNS HOPKINS UNIVERSITY · PI RUCZINSKI, INGO · 2011 to 2012
$477k
Deutsche Forschungsgemeinschaft (German Research Foundation) EXC 2124NIDCR NIH HHS R03 DE021437NIDDK NIH HHS U24 DK133658
6 · The paper itself

Abstract

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Indexed as

MetabolomicsChromatography, LiquidData Interpretation, StatisticalSoftwareTandem Mass Spectrometry

Identifiers

PMID39304763

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.