ArticleScientific reports2020
Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- The Source of Firefly Chemical Defenses: Endogenous Biosynthesis of Lucibufagins from Cholesterol.ACS omega · 2025Article
- The iPhylo suite: an interactive platform for building and annotating biological and chemical taxonomic trees.Briefings in bioinformatics · 2024Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Article
- Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case ofJournal of pharmaceutical analysis · 2024Article
- A Relook into the Flavonoid Chemical Space ofJournal of analytical methods in chemistry · 2023Article
- Thermostable chaperone-based polypeptide biosynthesis: Enfuvirtide model product quality and protocol-related impurities.PloS one · 2023Article
- Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview.Metabolites · 2021Review
- New software tools, databases, and resources in metabolomics: updates from 2020.Metabolomics : Official journal of the Metabolomic Society · 2021Review
- Article
- Chemically informed analyses of metabolomics mass spectrometry data with Qemistree.Nature chemical biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolite identification is the greatest challenge when analysing metabolomics data, as only a small proportion of metabolite reference standards exist. Clustering MS/MS spectra is a common method to identify similar compounds, however interrogation of underlying signature fragmentation patterns within clusters can be problematic. Previously published high-resolution LC-MS/MS data from the bioluminescent beetle (Photinus pyralis) provided an opportunity to mine new specialized metabolites in the lucibufagin class, compounds important for defense against predation. We aimed to 1) provide a workflow for hierarchically clustering MS/MS spectra for metabolomics data enabling users to cluster, visualise and easily interrogate the identification of underlying cluster ion profiles, and 2) use the workflow to identify key fragmentation patterns for lucibufagins in the hemolymph of P. pyralis. Features were aligned to their respective MS/MS spectra, then product ions were dynamically binned and resulting spectra were hierarchically clustered and grouped based on a cutoff distance threshold. Using the simplified visualization and the interrogation of cluster ion tables the number of lucibufagins was expanded from 17 to a total of 29.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.