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PhenoMeNal: processing and analysis of metabolomics data in the cloud
- Source :
- GigaScience 2 (8), 12 p.. (2019), GigaScience, GigaScience, BioMed Central, 2019, 8 (2), pp.giy149. ⟨10.1093/gigascience/giy149⟩, GigaScience, 2019, 8 (2), pp.giy149. ⟨10.1093/gigascience/giy149⟩, GigaScience, vol. 8, no. 2, pp. giy149, GigaScience, 8(2), 1-12
- Publication Year :
- 2019
-
Abstract
- International audience; Background Metabolomics 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. Findings PhenoMeNal (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. Conclusions PhenoMeNal 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.
- Subjects :
- Life Sciences & Biomedicine - Other Topics
Computer science
Interoperability
data analysis
Cloud computing
METABOLITES
ANNOTATION
Field (computer science)
Workflow
NMR
cloud computing
computational workflows
e-infrastructures
mass spectrometry
metabolomics
standardization
Technical Note
TOOL
Orchestration (computing)
0303 health sciences
030302 biochemistry & molecular biology
Small molecule
Multidisciplinary Sciences
Open data
Statistical analysis
statistics
[SDE]Environmental Sciences
Science & Technology - Other Topics
User interface
Life Sciences & Biomedicine
INTEGRATION
STANDARDS
REPOSITORY
Process (engineering)
Phenome
03 medical and health sciences
Metabolomics
[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
Metabolome
[SDV.BV]Life Sciences [q-bio]/Vegetal Biology
SPECTRA
Humans
Adaptation (computer science)
signal processing
Biology
030304 developmental biology
Reproducibility
Science & Technology
business.industry
PLATFORM
Metabolism
MASS-SPECTROMETRY
Omics
Data science
Spectrum analysis
galaxy
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
business
Cloud Computing
Metabolomics/methods
Software
SYSTEM
Subjects
Details
- Language :
- English
- ISSN :
- 2047217X
- Database :
- OpenAIRE
- Journal :
- GigaScience 2 (8), 12 p.. (2019), GigaScience, GigaScience, BioMed Central, 2019, 8 (2), pp.giy149. ⟨10.1093/gigascience/giy149⟩, GigaScience, 2019, 8 (2), pp.giy149. ⟨10.1093/gigascience/giy149⟩, GigaScience, vol. 8, no. 2, pp. giy149, GigaScience, 8(2), 1-12
- Accession number :
- edsair.doi.dedup.....5dcb798f9363e6117b443b71b5d911d3