The metaRbolomics Toolbox in Bioconductor and beyond

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.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

“…The open access software that provide these functionalities in the interactive fashion are listed in Table 4. For the computational community, the recently assembled MetaRbolomics toolbox provides an extensive resume of R packages that can be used for data processing, metabolite annotation, and biochemical network and pathway analysis [127]. In order to map the identified metabolite changes in the biochemically relevant context, one first needs to convert the metabolite identities into the relevant metabolite identifiers (e.g., KEGG, HMDB, Recon, etc.)…”

A Network Pharmacology Approach to Estimate the Active Ingredients and Potential Targets of Cuscutae semen in the Treatment of Osteoporosis

“…Bioconductor (http://www.bioconductor.org/) provides tools for the analysis and interpretation of high-throughput genomic data. It uses the programming software R, which is an open source and open development software [20]. With the help of the R package, we successfully installed this useful analysis tool and then ran the code.…”

patRoon: open source software platform for environmental mass spectrometry based non-target screening

“…This leads to difficulties in data sharing, as exact algorithm implementations and parameter choices are hidden, while maintenance, auditing or code extension by other parties is often not possible. Many open-source or open-access tools are available to process mass spectrometry data, such as CFM - ID [ 30 , 31 ], enviMass [ 32 ], enviPick [ 33 ], nontarget [ 34 ], GenForm [ 35 ], MetFrag [ 36 ], FOR - IDENT [ 37 ], MS - DIAL [ 38 ], MS - FINDER [ 39 ], MZmine [ 40 ], OpenMS [ 41 ], ProteoWizard [ 23 ], RAMClustR [ 42 ], SIRIUS and CSI:FingerID [ 43 – 47 ], XCMS [ 48 ], CAMERA [ 49 ] and XCMS online [ 50 ] (Table 1 , further reviewed in [ 51 , 52 ]). Various open tools are easily interfaced with the R statistical environment [ 53 ] (Table 1 ).…”