ArticleTrends in analytical chemistry : TRAC2015
Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics.
Article in Trends in analytical chemistry : TRAC, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed.
- Fusion Partner and ER-Retention Signal Are Associated with Distinct Host Proteomic and Metabolic Responses During Interleukin-15 Production inPlants (Basel, Switzerland) · 2026Article
- Gas Chromatography Chemometrics of Volatile Analytes in Mexican Mezcals: Fingerprinting Ancestral and Artisanal Manufacturing Processes, Geographical Origins, and Agave Species.Foods (Basel, Switzerland) · 2026Article
- Phytochemical Composition of Clonally PropagatedMolecules (Basel, Switzerland) · 2026Article
- Detecting and Grouping In-Source Fragments with Low-Energy Stepped HCD, Together with MSMetabolites · 2026Article
- Article
- Characterization of black oat root exudates in the presence of interspecific weed species and intraspecific neighbors, and their effects on root traits.Frontiers in plant science · 2026Article
- Article
- MSNature methods · 2025Article
- An ensemble model of machine learning regression techniques and color spaces integrated with a color sensor: application to color-changing biochemical assays.RSC advances · 2025Article
- An Ensemble Spectral Prediction (ESP) model for metabolite annotation.Bioinformatics (Oxford, England) · 2024Article
- High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.Environmental science & technology · 2024Review
- Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies.Analytical chemistry · 2024Article
- Heterogeneous Transformation of Ginsenoside Rb1 with Ethanol Using Heteropolyacid-Loaded Mesoporous Silica and Identification by HPLC-MS.ACS omega · 2023Article
- Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification.Metabolites · 2023Article
- Optimization Strategies for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Small Polar Molecules in Human Plasma.Metabolites · 2023Article
- An end-to-end deep learning framework for translating mass spectra to de-novo molecules.Communications chemistry · 2023Article
- Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.Analytical chemistry · 2023Article
- CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.Analytical chemistry · 2022Article
- MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS.Environmental science & technology · 2022Article
- Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.
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.