Abstract
MBROLE (Metabolites Biological Role) facilitates the biological interpretation of metabolomics experiments. It performs enrichment analysis of a set of chemical compounds through statistical analysis of annotations from several databases. The original MBROLE server was released in 2011 and, since then, different groups worldwide have used it to analyze metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE3, accessible at http://csbg.cnb.csic.es/mbrole3. This new version contains updated annotations from previously included databases as well as a wide variety of new functional annotations, such as additional pathway databases and Gene Ontology terms. Of special relevance is the inclusion of a new category of annotations, ‘indirect annotations’, extracted from the scientific literature and from curated chemical-protein associations. The latter allows to analyze enriched annotations of the proteins known to interact with the set of chemical compounds of interest. Results are provided in the form of interactive tables, formatted data to download, and graphical plots.
Graphical Abstract
Graphical Abstract.
INTRODUCTION
The last step in the analysis of ‘omics’ experiments is usually the functional interpretation of the results. For that purpose, annotation enrichment analysis (over-representation of functional annotations) was originally used for the interpretation of transcriptomics experiments. A wide array of online tools was soon developed to analyze large sets of genes/proteins with this methodology (1). These tools obtain the functional annotations of a set of genes or proteins of interest and compute their statistical enrichment by comparing the input set to a background set.
Chemical compounds perform a variety of functions in cells and organisms. Metabolomics, the study and identification of chemical compounds in a biological sample, has moved in recent years from the search for biomarkers to the comprehensive discovery of metabolites and general biological processes (beyond metabolic pathways) that are associated with particular phenotypes (2). To achieve this goal, there is also a need to interpret, in biological and chemical terms, the results obtained by metabolomics experiments especially when these comprise large sets of metabolites.
Known functional information on chemical compounds is spread across a number of data resources. These functional annotations can be used to interpret metabolomics results. Two main approaches can be followed to functionally interpret results: mapping and visualization and statistical analysis (3). MBROLE follows the second approach, as it performs over-representation analysis of functional annotations. This type of analysis can also be performed with other tools. Some of these are provided by individual databases, e.g. KEGG pathways (4), BioCyc pathways (5), ChEBI (6). The advantage of these tools is that they analyze the most updated version of the data. The drawback is that, in order to analyze a given dataset with more than one of these sources, researchers need to use each tool independently and integrate their diverse and heterogeneous results. Other tools offer additional analysis features, but contain a rather limited range of functional annotations and/or are focused on particular organisms. This is the case of MetaboAnalyst (7), a general analysis platform that allow users to analyze metabolomics experiments from start to end. IMPALA (8), Paintomics (9) and 3Omics (10) allow combined analysis of gene/proteins, and metabolites but they are limited to pathway annotations. ConsensusPathDB (11) allows integrated analysis on several pathway databases, but does not contain other types of functional annotations and is limited to certain organisms (human, yeast, mouse). Finally, some pioneering tools for metabolite enrichment analysis are no longer available, like MPEA (12), a problem that affects bioinformatics servers in general (13).
The MBROLE server was first released in 2011 (14), and has been maintained and further developed during these years. A second major release was launched in 2016 (15), expanding the annotations and server functionality and improving the user experience. MBROLE has been used by many researchers worldwide to interpret metabolomics studies of several organisms, e.g. (16–27). In this work, we present the latest release of the server, MBROLE3, which provides a wider and richer set of functional annotations for analysis including a new category of indirect annotations, as well as improvements on the interface and the presentation of the results.
RESULTS
As in previous releases, MBROLE3 requires users to provide a list of chemical compound identifiers, select the annotations they want to analyze, and the background option for statistical analysis. The server provides an interactive table with enriched annotations together with statistical parameters (P-values and corresponding corrected values for multiple testing) as well as hyperlinks to the source databases. Users can also retrieve the functional annotations from a set of chemical compounds and convert a set of chemical compound identifiers from different databases.
Chemical compound annotations
In MBROLE3, we have updated the annotations with respect to the previous version for those databases that are still public, updated, and maintained (e.g. KEGG, BioCyc, HMDB (28), ECMDB (29), ChEBI (30)). We have not included annotations from databases that are no longer publicly available, e.g. UniPathways (31) and MATADOR (32).
We have also included new annotations that were not available in previous versions: disease classification from KEGG, drug indications from the Therapeutic Target Database (TTD) (33), pathways from PathBank (34), Reactome (35) and PharmGKB (36), and lipid classification from the LIPID MAPS® Structure Database (LMSD) (37).
Functional information on chemical compounds beyond metabolic pathways and chemical classifications is still scarce compared with that available on gene products. However, several efforts have been devoted during these years to fill this gap. The Comparative Toxicogomics Database (CTD) (38), which is focused on environmental exposures and their effects on human health, manually compiles Gene Ontology (39) annotations for chemical compounds from the scientific literature. We have included these annotations in MBROLE3.
In contrast to these curated sets, some efforts using automatic methods have focused on extracting reliable annotations from the literature by means of statistical approaches, like Metab2MeSH (40). We have used an automatic literature (PubMed) analysis (41) to extract significant associations between chemical compounds and a subset of Medical Subjects Headings (MeSH) terms (42) in relevant categories (see Supplementary Table 1). As these MeSH term associations are automatically inferred from statistically significant co-citations in the literature, we provide them in a separate category of ‘indirect annotations’. These indirect annotations of MeSH terms were generated for the chemical compounds in the Human Metabolite Database (HMDB) and the chemical compounds annotated in MeSH itself. For HMDB, we use the name and synonyms of the chemical compounds annotated in that resource for the literature searches and the subsequent co-citation analysis. For that, we excluded some generic terms that HMDB annotates as ‘synonyms’ as those present in more than one chemical compound. For MeSH, we identified the terms in this resource describing specific chemical compounds by their categories (see Supplementary table 1), with the additional requirement that they are associated to at least one ID in other databases.
An additional source of information on chemical compounds that have not yet been exploited in functional enrichment tools is that derived from known chemical-protein interactions. I.e. transfer to a chemical compound the annotations of the proteins it interacts with. Indeed, curated interactions between chemical compounds and proteins are available from a number of databases (e.g. BioCyc, HMDB, etc.). Specifically, MBROLE3 allows to analyze two types of indirect annotations from known chemical-protein interactions: UniProt keywords (43) and PANTHER protein functional classification (44). Due to the large number of chemical-protein associations available, only a subset of model organisms have been included in MBROLE3 for this type of indirect annotation (see Supplementary Table 2).
With the addition of these types of indirect annotations, MBROLE3 now enables the analysis of a wider variety of functional annotations that describe different aspects of the chemistry and biology of chemical compounds (see Table 1). Direct annotations include pathways, modules, and pathway classes; Gene Ontology terms; interactions with proteins; physiological locations; chemical classifications and taxonomies; and diseases, biological roles, uses, and applications. Indirect annotations include interacting protein keywords, protein functional classification, and selected MeSH terms from the literature.
Table 1.
Functional annotations that can be analysed with MBROLE3
| Aspect | Annotations |
|---|---|
| Pathways & processes | • Pathways: from BioCyc, HMDB, KEGG, PathBank, PharmGKB and YMDB • Modules: from KEGG • Gene Ontology terms from CTD |
| Interactions | • Protein interactions: from BioCyc, HMDB and PathBank |
| Roles | • Biological Roles: from ChEBI and KEGG • Role and Physiological effect from HMDB ontology |
| Locations | • Tissue: from HMDB • Biofluid: from HMDB • Cellular: from YMDB, ECMDB and HMDB • Disposition: from HMDB ontology |
| Drugs | • Indication: from TTD • Pharmacological actions: from MeSH • Anatomical Therapeutic Chemical Classification (ATC): from KEGG |
| Diseases | • Disease Classfication: from KEGG • Diseases: from HMDB and KEGG |
| Chemical classification | • Chemical Taxonomy: from LMSD and HMDB • MeSH hierarchy • Chemical types: from BioCyc |
| Indirect Annotations | • Uniprot Keywords from chemical–protein interactions (BioCyc, HMDB and PathBank) • Panther Protein Class from chemical–protein interactions (BioCyc, HMDB and PathBank) • MeSH terms from PubMed |
ID conversion
Several compound IDs are used to refer to chemical compounds in different databases. Input compound IDs supported by MBROLE3 include those in the previous version: KEGG compounds, HMDB, YMDB and ECMDB metabolites, PubChem compounds, ChEBI accessions, and LMSD lipids; plus new ones: CAS Registry Numbers, ChemSpider, MeSH, PathBank, ParhmGKB, Reactome and TTD compound IDs.
MBROLE3 users can select two alternative automatic ID conversion mechanisms. The first one relies on direct cross-references between databases. For that, the user must select the type of compound IDs provided from a list of supported IDs (e.g. KEGG IDs). Here, only annotations from that database or those from databases that are cross-referenced (in any direction) can be analyzed.
The second is a fully automatic conversion, where MBROLE3 recognizes IDs and uses intermediate databases in case no direct cross-references are available. In this case, users can analyze any of the MBROLE3 annotations.
As in the previous versions, MBROLE3 also includes a conversion utility to convert to supported chemical compound IDs and to get compound IDs from a set of chemical names.
Graphical plots
In addition to an interactive table showing the enriched functional annotations and their statistical significance in terms of p-value and false discovery rate (FDR) corrected p-value, MBROLE3 now allows users to display results in two graphical representations: a bar plot and a dot plot (Figure 1).
Figure 1.
Example of MBROLE3 results. Two interactive tables are displayed for Direct (Panel A) and Indirect (Panel B) annotations, as well as two new graphical representations: bar plot (A) and dot plot (B). Plots contain up to a maximum of 20 annotations and with FDR <0.05.
Both of them show the most significant annotations against their FDR value. A color code highlights the enrichment ratio of each annotation (A) defined as the ratio between the percentage of compounds with annotation A in the input and the percentage of compounds with annotation A in the background. In the dot plot, additionally, the size of the dot correlates with the number of compounds from the input with that particular annotation. MBROLE3 generates separate figures for each type of annotation (e.g. KEGG pathways, GO terms, KEGG modules, etc.).
DATA AVAILABILITY
MBROLE3 is available at http://csbg.cnb.csic.es/mbrole3. MBROLE3 is free and open to all users and there is no login requirement.
Supplementary Material
ACKNOWLEDGEMENTS
We thank to database source providers, for publicly sharing their data. Special thanks to Rafael Torres (BioinfoGP service, CNB-CSIC) and David San Leon (Systems Biotechnology Group, CNB-CSIC) for their feedback on server style and functionality. The conclusions, findings and opinions expressed in this scientific paper reflect only the view of the authors and not the official position of the European Food Safety Authority.
Contributor Information
Javier Lopez-Ibañez, Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
Florencio Pazos, Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
Monica Chagoyen, Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
FUNDING
Spanish Ministry of Economy and Competitiveness with European Regional Development Fund [PID2019-108096RB-C22 to F.P. and M.C.]; Spanish State Research Agency [AEI/10.13039/501100011033] through the ‘Severo Ochoa’ Programme for Centres of Excellence in R&D [SEV-2017-0712]; European Food Safety Authority [GP/EFSA/ENCO/2020/02 to F.P.].
Conflict of interest statement. None declared
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
MBROLE3 is available at http://csbg.cnb.csic.es/mbrole3. MBROLE3 is free and open to all users and there is no login requirement.