ReviewMetabolites2019
The metaRbolomics Toolbox in Bioconductor and beyond.
Review in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled 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.
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
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software.Metabolomics : Official journal of the Metabolomic Society · 2023Pooled it
- Metabonaut: an open educational resource for learning reproducible, script-based untargeted LC-MS/MS metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- Enhancing Chimeric Fragmentation Spectra Deconvolution Using Direct Infusion-Tandem Mass Spectrometry Across High-Resolution Mass Spectrometric Platforms.Rapid communications in mass spectrometry : RCM · 2026Article
- Article
- Leveraging open cheminformatics tools for non-targeted metabolomics analysis of C. elegans: a workflow comparison and application to strains related to xenobiotic metabolism and neurodegeneration.Analytical and bioanalytical chemistry · 2025Article
- Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data.Nature communications · 2025Review
- QC4Metabolomics: Real-Time and Retrospective Quality Control of Metabolomics Data.Analytical chemistry · 2025Article
- Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies.Analytical chemistry · 2025Review
- Data Treatment for LC-MS Untargeted Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies.bioRxiv : the preprint server for biology · 2024Article
- Phytochemical drivers of insect herbivory: a functional toolbox to support agroecological diversification.Royal Society open science · 2024Article
- MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.Nature communications · 2024Article
- Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER.iScience · 2023Article
- Combination of Structure Databases, In Silico Fragmentation, and MS/MS Libraries for Untargeted Screening of Non-Volatile Migrants from Recycled High-Density Polyethylene Milk Bottles.Analytical chemistry · 2023Article
- The untargeted urine volatilome for biomedical applications: methodology and volatilome database.Biological procedures online · 2022Review
- Quick tips for re-using metabolomics data.Nature cell biology · 2022Article
- A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.Metabolites · 2022Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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.