PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

Abstract

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: The PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which 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 infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and 'omics research domains.

Accessible and reproducible mass spectrometry imaging data analysis in Galaxy

“…With the integration of the MSI data analysis toolset, we have incorporated an accessible and reproducible data analysis platform for MSI data in the Galaxy framework. Our MSI tools complement the multitude of already available Galaxy tools for proteomics and metabolomics that are maintained by Galaxy-M, Galaxy-P, Phenomal and Workflow4Metabolomics [34][35][36][37][38].…”

“…have led to a multitude of metabolomics and proteomics analysis tools available in Galaxy [34][35][36][37][38]. These tools allow analyzing additional tandem mass spectrometry data that is often acquired to aid identification of MSI m/z features.…”

“…The possibility to seamlessly link tools of different origins has outstanding potential for MSI studies that often rely on different software platforms to analyze MSI data, additional MS/MS data (from liquid chromatography coupled tandem mass spectrometry), and (multimodal) imaging data. More than hundred tools for proteomic and metabolomics data analysis are readily available in Galaxy due to community driven efforts [34][35][36][37][38]. Increasing integration of MSI with other omics approaches such as genomics and transcriptomics is anticipated and the Galaxy framework offers a powerful and future-proof platform to tackle complex, interconnected data-driven experiments.…”

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

“…We have implemented an approach to exploit encapsulation, packaging and container orchestration for Galaxy workflows, that simplifies cloud portability. This has been used extensively in the context of the PhenoMeNal H2020 Project (Peters et al, 2018), in production at https://public.phenomenal-h2020.eu/ for the past three years (handling tens of thousands of jobs in the public instance). However, the set-up and methods are generic and can be easily applied to other omics, as done recently for instance on Single Cell RNA-Seq.…”

Tools and resources for metabolomics research community: A 2017–2018 update

“…is an e-infrastructure that provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform that integrates diverse open source tools, tested and packaged as Docker containers, which are deployed based on a kubernetes orchestration framework. [81]. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm (Fig.…”