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. 2010 Apr 29:11:214.
doi: 10.1186/1471-2105-11-214.

Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis

Henning Redestig  1 , Miyako Kusano, Atsushi Fukushima, Fumio Matsuda, Kazuki Saito, Masanori Arita
Affiliations

Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis

Henning Redestig et al. BMC Bioinformatics. .

Abstract

Background: Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.

Results: Here we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings.

Conclusions: Efficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at http://metmask.sourceforge.net, that implements our ideas.

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Figures

Figure 1
Figure 1
The MetMask concept. Metabolite identifier consolidation using MetMask. (A) A local database is created by importing public databases as well as platform specific reference libraries (Ref Lib) that list all relevant analytes and the parent metabolites to which they correspond. (B) The created database can be used to rapidly extract identifiers and meta-data to enable summarization and contextual analysis such as pathway projections.
Figure 2
Figure 2
Metabolite identifier integration. Strategy for integrating metabolite identifiers. (1) The reference library RefLib specifies an identifier and its links. Upon import to the MetMask database, these identifiers are assigned to Group 1. (2) The KEGG entry c00037 links to both the CAS number 56-40-6 and the synonym glycine and is therefore merged to Group 1. (3) The PlantCyc entry gmp overlaps with the identifiers in Group 1 but only in a single synonym and is therefore assigned to the new identifier group, Group 2. Group 2 retains the mapping to the conflicting synonym G but has this link annotated as weak.
Figure 3
Figure 3
The connection graph for the KEGG identifier "C00041". An excerpt of the connection graph associated with alanine. Identifiers for D-alanine and L-alanine have been merged since high-throughput metabolomics usually do not resolve optical isomers. Several of the connections are only available via intermediate steps, illustrating how complicated manual identifier conversions can be. Green edges come from the MPIMP MS library, gray edges come from ChEBI, red edges come from PlantCyc, and blue edges come from KEGG and the yellow edge come from our CE-MS library.
Figure 4
Figure 4
Obtaining a consensus metabolite feature. Examples of enabled technologies. (A) MetMask makes it easy to cross-reference identifiers used in different metabolomics platforms. Once this has been done, features representing the same metabolite can be summarized using, e.g., principal component analysis to obtain a consensus data set. Here, an example is shown where the features from CE-MS and GC-MS that represent alanine are replaced by PC1. (B) MetMask can link unified identifiers to annotation databases such as PlantCyc, thereby allowing for contextual interpretation such as metabolite set enrichment analysis. The boxplot shows that the log fold changes between red and green tomatoes are higher among the nucleotide synthesis related metabolites than the other metabolites.
See this image and copyright information in PMC

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