Abstract
Summary One of the major challenges in LC-MS data (metabolome, lipidome, and exposome) is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed, containing lots of information about compounds and pathways. However, there is still no tool developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment analysis. massDatabase is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Materials.
Availability and implementation https://massdatabase.tidymass.org/.
Contact shenxt{at}stanford.edu and mpsnyder{at}stanford.edu
Supplementary information Supplementary data are available at Bioinformatics online.
1 Introduction
Liquid chromatography coupled to mass spectrometry (LC−MS) is a comprehensive, unbiased technology to research small compounds, which has become increasingly popular in dietary, environmental, and biomedical studies (Wishart, 2016). One of the major challenges in LC-MS data (metabolome, lipidome, and exposome) is the post-processing of a large number of metabolic feature entries to achieve clear biological evidence, such as the compound annotation and pathway enrichment. Therefore, the databases for compounds and pathways are essential for these analyses. Multiple public databases for compounds and pathways are available online, which benefits the community (Go, 2010). However, the existence of an automated, multiple compound/pathway query processing package in R is still a demand. So far, although several R packages have been developed to extract online databases, most of them only support one or limited databases and have different design concepts and output formats. In addition, they can not be combined with other existing tools for a straightforward subsequent analysis, which limits their further applications.
Here, we presented the massDatabase package to overcome the challenges mentioned above while accessing the online databases, particularly to (1) support most of the commonly used online public databases (11 databases, Table S1), (2) operate (extracting, downloading, reading, and converting) the online public databases, and (3) combine the online public databases with existing tools for subsequent compound annotation, and pathway enrichment analysis (Fig. 1).
The overview of (a) the online databases that massDatabase support, (b) the functions used to process databases, and (c) the combination with other tools in the tidyMass project.
2 Features and methods
Using massDatabase, users can extract compound/pathway information from the online databases (11 databases, Table S1) and download them. In addition, massDatabase can also be combined with other tools for metabolite annotation and pathway enrichment analysis. The massDatabase can be installed on Mac OS, Windows, and Linux.
2.1 Online databases operation
The functions in massDatabase could be grouped into four classes. (1) Request specific information of one item (compound, pathway, reaction, etc.) online using the web crawler, (2) download the corresponding database, (3) read the downloaded databases (csv, mgf formats, etc.) as R object (list or data frame), and (4) convert the databases to other formats that could be used for other tools (Fig. 1).
2.2 Combination with other tools
The users can download the online databases and then convert them to the formats supported by the packages in the tidyMass project using massDatabase. Currently, two packages from tidyMass projects could combine with massDatabase. Users can download the compound databases (MS1 or MS2 spectra databases), convert them to the database format in the metID package, and then use them for compound annotation by metID. Furthermore, users can also download the pathway databases, convert them to the pathway database format in the metPath package, and then use them for pathway enrichment analysis by metPath.
3 Case study
We applied massDatabase to a published study from our lab (Liang et al., 2020) as a case study for exemplifying the value of massDatabase in biological function mining by integrating with the online public databases. The MS2 spectra databases from HMDB, MassBank, and MoNA were first downloaded and converted to databases format in metID. And the pathway database from KEGG is downloaded and converted to pathway database format in metPath. Then the metabolic feature table was annotated by metID which is based on the public databases from massDatabase and our in-house library. Then all the annotated metabolites were used for pathway enrichment analysis using metPath. The top enriched pathways include Steroid hormone biosynthesis, Phenylalanine metabolism, Caffeine metabolism, Linoleic acid metabolism, Primary bile acid biosynthesis, etc., which are most consistent with the original analysis (Fig. S1) (Liang et al., 2020). These results indicate that massDatabase is a powerful tool for utilizing online public compound and pathway databases for automated and reproducible analysis of LC-MS-based metabolomics data (Supplementary Material).
4 Conclusion
massDatabase is developed to operate public databases in untargeted LC-MS-based data (metabolome, lipidome, and exosome). It allows users to extract, download, read databases, and convert database formats to different formats required by other tools. To our best knowledge, it is the first R package allowing users to operate most of the commonly used online public databases for subsequent biological function mining.
Funding
This work received no external funding.
Conflict of Interest
M.S. is a co-founder and member of the scientific advisory boards of the following: Personalis, SensOmics, Filtricine, Qbio, January, Mirvie, and Oralome.
Footnotes
