ArticleGigaScience2019
PhenoMeNal: processing and analysis of metabolomics data in the cloud.
Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
What it found
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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
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A priori sample size determination and power analysis in metabolic phenotyping and integrative metabolomics: an application framework based on a systematic review of literature.Metabolomics : Official journal of the Metabolomic Society · 2026Pooled it
- S'Wipe: user-friendly stool collection for high-throughput gut metabolomics and multi-omics.mSystems · 2026Article
- Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.bioRxiv : the preprint server for biology · 2025Article
- Review
- Review
- Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study.Metabolites · 2024Article
- A multi-omics data analysis workflow packaged as a FAIR Digital Object.GigaScience · 2024Article
- Statistical methods and resources for biomarker discovery using metabolomics.BMC bioinformatics · 2023Review
- Metabolomics meets systems immunology.EMBO reports · 2023Review
- MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry.Journal of cheminformatics · 2023Article
- Ontologies for increasing the FAIRness of plant research data.Frontiers in plant science · 2023Review
- Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae.Scientific data · 2022Article
- From biomedical cloud platforms to microservices: next steps in FAIR data and analysis.Scientific data · 2022Article
- MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data.BMC bioinformatics · 2022Article
- Development of RIKEN Plant Metabolome MetaDatabase.Plant & cell physiology · 2022Article
- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
- Metabolomics of neurological disorders in India.Analytical science advances · 2021Review
- MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.Biomolecules · 2021Article
- Article
- 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
56 authors.
Funding
Abstract
backgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution.
findingsPhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm.
conclusionsPhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.
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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.