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

Abstract

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.
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.
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.

Full text

Page 1

SOFTWAREOpen Access
Consolidating metabolite identifiers to enable
contextual and multi-platform metabolomics
data analysis
Henning Redestig*, Miyako Kusano, Atsushi Fukushima, Fumio Matsuda, Kazuki Saito, Masanori Arita
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.
Background
Efficient analysis of data from high-throughput experi-
ments requires sufficient access to information about
the measured biomolecules. Such data are often referred
to as meta-data and provide a biological and chemical
context in the form of parameters such as function,
localization, and structure. Numerous applications of
genomics and transcriptomics data analysis depend on
the availability of meta-data, including pathway projec-
tions [1,2] and gene set enrichment analysis [3-5].
Metabolomics data analysis is no exception, and apart
from the aspects of biological interpretation, meta-data
are also needed for data integration. In particular, wide
coverage detection of metabolites can only be achieved
by combining multiple metabolomics platforms [6] such
as GC-MS (gas chromatography-mass spectroscopy),
CE-MS (capillary electrophoresis - MS) [7], LC-MS
(liquid chromatography - MS) [8] and1H-NMR [9].
However, in order to summarize multi-platform data in
a consensus data set, it is crucial that the meta-data
define to which metabolite each feature corresponds in
a consistent and non-redundant manner.
Data on biomolecules can be found in online data-
bases, which must be cross-referenced to allow for col-
lation of meta-data packages for individual data sets.
Creating mappings between local identifiers used in
experimental data and public identifiers is a laborious
process since missing, ambiguous or redundant entries
are common. Identifiers are also subject to frequent
changes and it is therefore clear that cross-referencing
must be an automated process to enable efficient and
* Correspondence: henning@psc.riken.jp
Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-
ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
© 2010 Redestig et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

Page 2

reproducible research. For sequence-based data, several
tools have been developed that can be used to automati-
cally cross-reference public databases. The AnnBuilder
R package [10] assembles and consolidates genomic
information from resources such as LocusLink and the
Gene Ontology. PICR [11] and PAnnBuilder [12] per-
form similar tasks for proteins.
Several public databases are focused on gathering
information about metabolites and integrating this with
data on genes and proteins. Notable examples include
the Chemical Entities of Biological Interest database
(ChEBI) [13], the Kyoto Encyclopedia of Genes and
Genomes (KEGG) [14], the Madison Metabolomics
Consortium Database (MMCD) [15] and the Human
Metabolome Database (HMDB) [16]. All these databases
use more than one public identifier and can therefore
also be used for identifier conversion. Middleware solu-
tions such as BioSpider [17], BioMart [18] and BridgeDb
[19] are useful tools for querying these in an efficient
manner. However, metabolomics data are often anno-
tated with compound names (synonyms) of varying con-
sistency, or in the best case, references to in-house
libraries. These local identifiers can often be associated
with several different molecular structures causing ambi-
guities and redundancies that make them very difficult
to cross-reference with public identifiers. A tool that
aim to solve this task has, to the best of our knowledge,
not yet been reported.
There are several aspects of metabolite identifiers that
make them difficult to cross-reference in an automated
fashion. A major obstacle is the lack of a widespread
standardized identifier. There is a multitude of different
schemes for referencing chemical compounds because
the best way of doing so largely depends on the purpose
of the identifier. Metabolites are in general best referred
to by their absolute chemical structure using e.g. InChI
(IUPAC International Chemical Identifier). However, in
certain circumstances it is necessary that the identifier is
human readable warranting the use of chemical syno-
nyms; on other occasions, we need to refer to a specific
resource and therefore use database keys. Currently,
chemical databases solve this problem by using multiple
types of identifiers in parallel.
Unfortunately, with different databases relying on dif-
ferent identifiers, consolidation becomes very difficult
[20], especially since one frequently must rely on multi-
ple intermediate resources.
Another serious problem for data integration is that
most referencing schemes are redundant in the sense
that the same compound has multiple valid identifiers.
Therefore, even if everyone used, e.g., PubChem IDs as
suggested by Kind et al. [20], cross-referencing for the
purpose of data integration may still be difficult as dif-
ferent identifiers do not imply different metabolites.
A related problem stems from the fact that chemical
databases are geared toward annotating single, specific
compounds, which is not entirely compatible with real
life metabolomics data. Metabolites are often deriva-
tized prior to separation due to analytical requirements
or inexactly determined because of the limited resolu-
tion of high-throughput metabolomic platforms.
Hence, the measured analyte does not necessarily cor-
respond to the same chemical structure as the original
metabolite and is therefore associated with a different
identifier. These identifiers must be mapped back to
their plain structure prior to biological interpretation
and integration with data from other platforms. This
problem becomes especially vexing since different plat-
forms may use different analytes for the same metabo-
lite. The abstraction between analytes and metabolites
is typically only defined in platform-specific in-house
libraries (e.g., [21]). Taken together, identifiers used in
metabolomics are connected in a many-to-many kind
of relationship, which current chemical databases do
not fully support.
One approach to solve the problem of gathering meta-
data for metabolomics would be to build a new database
of chemical compounds including all known analytes,
how they relate to parent metabolites as well as links to
all relevant biological resources. Such a project, how-
ever, would be extremely resource intensive, and since
different metabolomics researchers use different refer-
ence libraries and have different ambitions, it would still
not solve the problem completely.
Instead, we opted for a more pragmatic strategy and
designed a program that can import both in-house
reference libraries and online resources and organize the
identifiers by how they are interconnected. By this
method, groups of compounds are formed containing
both the analytes and the metabolites they refer to as
well as links to the selected biological databases. Because
all available identifiers are used in parallel, there is no
need for any master identifier, and databases can be
consolidated as long as they can be linked using any of
the imported resources. The result is a metabolite-cen-
tric database, which can be used to obtain tailored meta-
bolite meta-data in a flexible and straightforward
manner.
Here, we present and discuss our strategy for reconcil-
ing metabolite identifiers across in-house libraries and
public databases. Examples are given both for how to
create and query a custom database as well as the types
of data analysis that this technology enables. Using the
provided software, MetMask (the metabolite masking
tool), tailored mappings between different metabolite
identifiers are easy to construct, thereby providing meta-
data accessibility similar to that known from gene
expression data analysis.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 2 of 11

Page 3

Implementation
The goal of this project was to construct a method to
efficiently cross-reference different types of metabolite
identifiers in order to facilitate downstream data analysis
(Figure 1). Specifically, we wish to obtain a local data-
base that associates every relevant metabolite with a
group of identifiers comprising all known references to
that metabolite. Because we deal with applied data ana-
lysis, two metabolites that can not be distinguished by
the metabolomics platform at hand are considered the
same metabolite. Every identifier group should also con-
tain references to the platform-specific analytical deriva-
tives, or analytes, of the associated metabolite.
These ambitions imply that the constructed groups
may contain references to more than one distinct che-
mical structure. The goal of the constructed database is
therefore clearly different from that of public chemical
databases such as HMDB and PubChem [22], which
gather information about specific chemical structures.
The resources that are available for constructing the
desired database are chemical reference libraries and
external public databases such as KEGG [14], ChEBI
[13] and the *Cyc databases (e.g. EcoCyc [23], Human-
Cyc [24] and PlantCyc [25]). The information taken
from external databases is the primary identifiers and
their links to other public databases. The reference
libraries are files that list the analytes that are recog-
nized by the corresponding metabolomics platform and
associate these with their parent metabolite. The listing
typically uses local identifiers for the analytes and at
least one publicly used identifier of the metabolite, e.g.,
a CAS (Chemical Abstracts Service) registry number or
commonly used synonyms.
Unfortunately, there are several limitations that make
these resources non-trivial to integrate with each other.
• The primary identifier that we seek, the identifier
group, is not used by any resource.
- All resources may list multiple entries for the
same metabolite, e.g., separate entries for alanine
and L-alanine.
• There is no identifier type that is used by all
entries in all resources.
• Comparing different resources, there may be errors
in the sense that the same identifier can be used to
refer to different metabolites.
The input can be thought of as a large network of
identifiers where primary identifiers are linked to other
identifiers. A straightforward way of obtaining the
groups of identifiers that we seek is to combine all iden-
tifiers that are interconnected. However, the last obser-
vation above implies that this could also erroneously
group strictly different metabolites. To solve this pro-
blem we designed a heuristic, which is described in the
following section.
A strategy for cross-referencing metabolites
We reason that the primary key, the identifier group,
should correspond to a group of strongly interconnected
identifiers. We grow these groups incrementally as new
data are imported to our database and merge groups if
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.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 3 of 11

Page 4

they overlap strongly with each other; see Figure 2 for a
schematic representation of the integration strategy.
All input can be broken down into groups of identi-
fiers that associate a primary identifier with a set of
associated identifiers. Upon data import, the database
that is being constructed is searched for any pre-existing
identifier group that overlaps with the incoming group.
If an overlapping group is found, it is tested for compat-
ibility with the new group. If the two groups are compa-
tible, they are merged to form a new larger group; if
they are not compatible, the overlapping identifiers in
the new group are annotated as ‘weak’ and the rest form
a separate group. Weak identifiers are defined as identi-
fiers that may be associated with more than one identi-
fier group. The compound name C is a typical example
of a weak identifier as it can refer to either cysteine or
cytosine.
Because no identifiers are deleted, all original associa-
tions found in the input are also present in the resulting
database and can be used to query for metabolites.
The constructed database may group different chemi-
cal structures, and therefore there is no direct way of
knowing which identifiers should be connected and
which should not. Hence, we resort to a rule set and
define two identifier groups as compatible if,
1. They come from the same source and share a
non-weak identifier.
2. They come from different sources but share at
least n types of non-weak identifiers, where n is
user-defined (default 2).
3. Two identifier groups are not compatible if they
do not have the same chemical sum formulas (ignor-
ing single proton differences and derivatized
compounds).
The threshold in step 2 is included to cope with errors
and minor ambiguities in the input. A high n implies
that correctness is prioritized and only groups of identi-
fiers that are very likely to refer to the same metabolite
are merged. A low n on the other hand prioritizes non-
redundancy, causing more groups to be merged. When
consolidating two databases that only can be linked with
a single type of identifier, this threshold should be set to
one. When working with very unreliable data sources, it
may instead be increased.
The resolved groups of metabolite identifiers are
stored in a local database that keeps track of the source
of every imported identifier, which identifier group it
belongs to and if that association is annotated as weak.
The identifier groups have a local primary key that
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.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 4 of 11

Page 5

allows for instantaneous conversion between different
types of identifiers.
The notion of defining certain identifiers as weak also
makes it possible to set entire identifier types as weak.
This way, annotational identifiers such as pathway infor-
mation can be imported without causing any identifier
groups to be merged.
The MetMask interface
The program interface is command line-based, making
it easy to integrate into automated data analysis pipe-
lines. Sources and binary distributions can be found at
http://metmask.sourceforge.net. The project page also
gives access to a web-interface, which can be used to
perform identifier conversion and visualization using an
example database.
The widely used statistical programming environment
R [26] together with the BioConductor project [27] pro-
vides meta-data for proteins and genes but not metabo-
lites. Therefore, we designed the metmask.db R-package
which can be used in a similar way as the packages
depending on the AnnotationDbi [28] framework. The
metmask.db package depends on a slightly modified
version of AnnotationDbi, which is available from the
MetMask project page. The package can be used either
with its accompanying database or with a customized
database by simply replacing the database file.
The main MetMask program is platform-independent,
free, open-source software implemented as a Python
package. Identifiers are stored in a local SQLite [29]
database and the package is distributed together with an
example database including 1439 identifier groups.
Import
Different parser modules depending on which source is
being imported handle imports to the database. The par-
sers read different file formats but in essence all perform
the same thing: collation of groups of identifiers, annotat-
ing and inserting them to the database. The currently dis-
tributed parsers are listed in Table 1. File format
definitions can be found in the user manual provided as
Additional file 1. The parsers modules are implemented
as plug-ins making implementation of new parsers easy.
Imports can be performed both comprehensively and
in synchronization mode in which only identifier groups
are imported that already have some overlap in the
database.
The identifier types KEGG compound ID, CAS
Number, KNApSAcK, CQ ID (from MMCD), Pub-
Chem Substance ID and Compound ID, InChI, Metlib
ID and HMDB ID are matched with regular
expressions to ensure that imported identifiers are well
formatted.
Once a database has been created, minor updates that
define new metabolites or add data to already existing
metabolite groups may be performed by re-importing
the updated source. Larger rearrangements and deletion
of identifiers is not supported in the current version of
MetMask and must therefore be done by rebuilding the
whole database.
Query and visualization
The MetMask database can be queried in a flexible
manner, making it easy to extract both information on
single entries and to do batch queries. When input is
given via standard input, each line is treated as a query
string, and the result is provided as standard output.
Full export is also supported in which the requested
identifier types are extracted for all identifier groups.
Default output is given as a comma delimited table
where one row corresponds to one identifier group and
each column corresponds to the queried identifier type.
Multiple identifiers of the same type and group are
separated by the pipe character. This type of output can
be imported into spreadsheet programs or read by inter-
preters such as Python, Perl or R.
The associations in the constructed database are
annotated with both the original source they came from
and whether they are considered useful for identifying a
specific metabolite or weak (only provide annotation).
The identifier groups can be visualized as graphs by tra-
cing how primary identifiers link to other identifiers in
the input sources (example in Figure 2). To facilitate
these visualizations, MetMask can output graphs of
identifier groups as text files with one edge per row,
with the source node in the first column and target
node in the second column. The original source is
given in the third column and the status as weak in the
fourth column. This type of text file can be visualized
using graph drawing software Cytoscape [30] or Rgraph-
viz [31].
Provided database
MetMask is distributed with a database built for our
metabolomics platform. The database is geared towards
plant primary metabolism and is not meant to suit all
researchers’ needs but mainly to serve as an example.
The database was built by importing platform specific
reference libraries and synchronization with KEGG,
PlantCyc (version 3.0) and ChEBI [13]. There are 1439
different identifier groups in the database representing
our estimation of the total number of distinct
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 5 of 11

Page 6

metabolites in our reference libraries. Table 2 presents a
listing of the used sources, and Table 3 shows a descrip-
tion of the created database.
Results and discussion
In order to demonstrate our approach, we look at an
example data set from a CE-MS-, GC-MS-, and LC-MS-
based study measuring metabolite levels in tomato fruits
in two ripening stages. The data set was composed of
three different data matrices, each annotated with either
chemical synonyms or identifiers referring to platform-
specific reference libraries. The biological aspects of the
experiment are not within the scope of this study and
we will only consider it as generic data set coming from
three platforms were all preliminary data processing
such as peak picking, deconvolution and alignment has
been successfully performed.
In the following sections we use two reference
libraries and public resources to create a MetMask
database and then show how it can be used to cross-
reference the local identifiers with public identifiers.
Finally, we provide two short examples of data analysis
techniques that the created identifier database enables.
The results presented here are specific to how these
particular libraries were constructed, but the main con-
cept will be the same regardless of the utilized
platform.
Creating and querying an identifier database
First, we import two reference libraries consisting of
comma separated text files and a NIST MS export file
listing local identifiers and synonyms as well as partial
links to publicly used identifiers. Using MetMask, an
import into a new database called ‘mydb’ is performed
by,
> metmask –import library-one.csv –parser
simple –db mydb
> metmask –import library-two.txt –parser
riken –db mydb
This import limits the search space of the metabolites
in order to obtain a stream-lined database for our
libraries. When this import has been performed, we can
enrich the created identifier groups with data from
other sources by importing those sources in synchroni-
zation mode. Augmenting our database with data from
ChEBI and KEGG is performed by,
> metmask –import chebi –synchronize –db
mydb
> metmask –import kegg –synchronize –db
mydb
The two commands listed above may take up to 20
minutes but only necessary when building or updating
the database.
Table 1 Parsers
NameResource FormatImported identifier types
simple
sdf
mpimp
cycdb
cyc
kegg
User provided
The NIST library
MPIMP MS library
Any *Cyc database
Any *Cyc database
KEGG
Comma separated text file
SDF chemical information file
NIST MS export file
compounds.dat file
compounds dump file
compounds file (local or via FTP)
File specific
NIST number, CAS, Synonyms, Sum formula
Name, KEGG, Synonyms, CAS
Frame ID, CAS, Synonyms, SMILES, InChI, KEGG
Synonyms, CAS, KEGG, SMILES, Sum formula, Pathway
KEGG ID, Synonyms, CAS, Sum formula, ChEBI, KNAp-SAcK,
Pathway, PubChem SID
ChEBI ID, IUPAC Name, CAS, KEGG, InChI, SMILES, Sum formula,
Synonyms
BioCyc, CAS, ChEBI, Sum formula, HMDB, InChI, IUPAC, KEGG,
Metlin, Synonym, Pub-Chem SID, PubChem CID
chebiChEBIonline database (SOAP)
metabocardHMDBmetabocards.txt
The currently provided parsers for importing metabolite information. File format definitions can be found in the user manual. The imported identifier types
indicate the identifiers that are extracted from the source file.
Table 2 The sources for the provided database
Name SourceSynchronization modeParser
PRIMe chemical standards
RIKEN MS Library
MPIMP MS library
PlantCyc compounds.dat
KEGG Compounds/Pathways
ChEBI
In-house database
http://prime.psc.riken.jp
Personal communication, [21]
http://www.plantcyc.org
http://www.genome.jp
http://www.ebi.ac.uk/chebi
No
No
No
Yes
Yes
Yes
simple
riken
mpimp
cycdb
kegg
chebi
The sources used to build the provided database. Each source contains one or more different identifier types. Synchronization mode imports only additional data
to already existing metabolite groups in the database.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 6 of 11

Page 7

Our experimental identifiers may be cross-referenced
by querying the created database. ChEBI identifiers,
KEGG IDs and synonyms for our example data set can
be extracted by calling,
> metmask local-ids.txt –goal chebi,
kegg, synonym -Q –db mydb
which yields
17497, c00160, glycolate|glycolic acid|
hydroxyacetic acid
17794|17050, c00197|c00597, 3-phospho-
dl-glycerate|3-phosphoglycerate|<cont..>
Other more complex queries such as “all CAS num-
bers and ChEBI identifiers for the entries associated
with KEGG pathway 00500 (starch and sucrose metabo-
lism)” are also straightforward, e.g.,
> metmask –query 00500 –table pathway
–goal cas, chebi –db mydb
Comparison with using single resources
The main, still relatively easy alternative to using
MetMask for cross-referencing identifiers is to write a
custom script to query a public database. In order to
compare our performance with this approach, we cre-
ated MetMask databases using only the example refer-
ence libraries library-one.csv, library-two.txt and the
input file local-ids.txt (Additional files 2, 3 and 4) and
one of the resources ChEBI, KEGG, PlantCyc, the
MPIMP MS (Max-Planck Institute for Molecular Plant
Physiology) library [21] and a manually curated list of
chemicals which we refer to as the PRIMe (Platform for
RIKEN Metabolomics) chemical standards. We then
tried to convert the identifiers and compound names
used in the experimental data to the public identifiers
CAS, KEGG ID or InChI. These identifier types are
used by all resources except the MPIMP MS library and
KEGG which do not use the InChI identifier.
Table 4 lists the number of successfully converted
identifiers. The result shows that although the public
databases contain the sought identifiers, they cannot
always be reached with the search strings found in the
experimental data. However, when all databases are
used together as in the MetMask strategy, identifiers are
accumulated and cross-referencing becomes possible.
This result shows that the MetMask approach of using
multiple resources improves cross-referencing, thereby
reducing the risk that metabolites get lost during identi-
fier conversion. Of the 251 search strings, 13 were com-
pletely absent from the MetMask database. Manual
examination revealed that they were either typos or rare
compound names. An advantage with MetMask is that a
list of such problematic identifiers can be used to create
a new input file, which associates them with better
known identifiers. After this input has been imported to
the database, the old identifiers are directly linked to all
other identifiers as well.
Enhanced performance in identifier conversion is,
however, not the only advantage when using MetMask
compared to querying single public resources. MetMask
makes it possible to incorporate local identifiers to cre-
ate a tailored database, something that is not supported
by any online resource.
MetMask also gives a single interface for queries that
facilitates and speeds up batch queries. Performing the
251 queries for the example data set takes ca. 5 sec-
onds on a standard PC. In comparison, the identifier
conversion tool BioSpider [17] use several online
resources making it continuously up-to-date, but also
fairly slow with a single query typically taking several
minutes.
Visualization of a group of identifiers
The associations in the example database provided with
MetMask are annotated both with the original source
they came from and whether they are considered to be
useful for identifying a specific metabolite or are weak
(only provide annotation). Identifier groups can be
visualized as networks where each source connects its
own master identifier to a set of externally used identi-
fiers. An excerpt of the connection graph for alanine is
Table 3 Statistics of the provided database
Identifier typeIdentifier nameNumber of identifiers
Groups
PRIMe chemical standards
RIKEN MS Library [33]
Synonym
Sum-formula
CAS
KEGG Compounds [34]
KEGG Pathway [34]
PubChem Compound [22]
PubChem Substance [35]
IUPAC Names
SMILES
InChI
KNApSAcK [36]
KaPPA-View [1]
LipidBank [37]
Lipid maps [38]
ChEBI [13]
Chemspider
MPIMP MS library [21]
PlantCyc Frame ID [25]
_id
rlib
riken
synonym
formula
cas
kegg
pathway
cid
sid
iupac
smiles
inchi
knapsack
kappav
lipidbank
lipidmaps
chebi
chemspider
mpimp
cycdb
1439
1287
241
11180
951
2416
1297
184
1857
1077
1928
2666
1668
671
261
127
178
1177
1001
3439
495
Statistics of the provided database. The number of groups is the total number
of constructed distinct metabolite groups. Each group gathers one or more
identifiers of the following listed identifier types.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 7 of 11

Page 8

shown in Figure 3. The MPIMP MS library connects
alanine, -DL (2TMS) with a KEGG entry and two syno-
nyms. As further sources were imported, more identi-
fiers were added to the same group, making it easy to
map our in-house identifier to the external resources
even though those associations would require intermedi-
ate identifier types.
Note that multiple entries from the public databases
ChEBI and KEGG have been merged to the same entry
since the analyte alanine, DL- (2TMS) can be inter-
preted as any of the entries L-alanine (KEGG:C00041,
ChEBI:16977), D-alanine (KEGG:C00133, ChEBI:15570)
or alanine (KEGG:C01401, ChEBI:16449). This merging
is an important feature because the resolution of the
annotations must match that of the experimental plat-
form and be as non-redundant as possible to avoid sta-
tistical bias toward multiply represented metabolites.
Gathering all equivalent identifiers also helps to avoid
inconsistencies, which may arise when the same meta-
bolite is annotated with slightly different compound
names.
Performing this kind of identifier grouping is very dif-
ficult without combining the result of queries to multi-
ple databases.
To avoid creating connections between different meta-
bolites we resorted to a heuristic rule-based approach.
Our capability to detect erroneous input is currently
limited to ensuring good overlap and matching sum for-
mulas between identifier groups before merging them.
Therefore, our accuracy depends largely on correct
input. After importing new resources to the database it
is recommendable to inspect the output manually to
confirm the result. If mistakes are discovered, the graph
visualization capability of MetMask provides a way to
Table 4 Comparison of cross-referencing performance on
the example data set
Databases CAS Registry
number
KEGG
ID
InChI Any
identifier
Only reference
libraries
PlantCyc
GMD
ChEBI
KEGG
PRIMe chemical
standards
All (MetMask)
124 580 124
125
199
124
131
168
8274
0
54
0
125
202
124
131
168
146
58
111
158166
235 222231238
Comparison of cross-referencing performance for the 251 identifiers and
synonyms found in the example data set. Local reference libraries were
combined with the sources listed in column Databases via MetMask and used
to convert local identifiers to CAS, KEGG and InChI identifiers. The table lists
the number of successfully converted identifiers. Conversion to “Any identifier”
indicates the number of local identifiers that could be converted to any other
type of identifier (e.g., SMILES, synonym, IUPAC name, etc.). Using all resources
together, as performed in the MetMask approach, we obtain a better identifier
conversion performance.
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.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 8 of 11

Page 9

track down the source of the errors. False associations
can then be dropped from the database to avoid future
errors. The main alternative to MetMask, writing cus-
tom scripts, is not less error-prone, particularly since
chemical databases tend to be sparsely connected
requiring the use of intermediate identifiers.
MetMask facilitates multi-platform metabolomics and
contextual data analysis
Identifier unification plays an important role if one com-
bines multiple analytical platforms to obtain better cov-
erage of the metabolome. Different platforms may use
different reference libraries, which results in data sets
with mixed types of identifiers. In order to obtain a con-
sensus, non-redundant data set, it is crucial that meta-
bolite identifiers are used in a consistent manner.
Efficient identifier management is therefore a key tech-
nology for multi-platform metabolomics. Current mid-
dleware solutions exemplified by BioMart [18] and
BioSpider [17] provide efficient access to online
resources but do not resolve any ambiguities or redun-
dancies that they imply.
After the identifiers in the example data set are unified
using MetMask, all analytes that correspond to the same
metabolite can be extracted and summarized by, e.g.,
replacing them with their first principal component (PC).
In Figure 4A, the alanine features from CE-MS and GC-
MS are replaced by their first principal component (PC).
This procedure can then be repeated for all duplicated
metabolites until all features are unique. Without proper
identifier integration, this task would require manual
intervention - an unfeasible process when working with
wide coverage metabolomics data. The obtained
consensus data set has the advantage over the original
data that it is not biased towards the number of features
that represent each metabolite. Following data analysis
thereby become easier to interpret and false positives due
to multiply represented metabolites can be avoided.
MetMask can also link to databases with biological
annotation and thereby facilitate biological interpretation.
In Figure 4B, the fold-changes between red and green
ripening stages were sorted in to their metabolite classes
as suggested by PlantCyc. In a manner analogous to the
gene set enrichment analysis (GSEA) [3] where sets of
genes are tested for association with a particular response
variable, we can perform metabolite set enrichment analy-
sis (MSEA). Using the Kolmogorov-Smirnov test (KS), we
test each class of metabolites to examine if the distribution
of fold-changes within the class differs from that of the
metabolites outside the class. Here, we found that metabo-
lites related to nucleotide/nucleoside synthesis, e.g., ribose,
uridine, guanine and adenosine, have been up-regulated
when comparing green tomatoes to red tomatoes (KS test
P = 0.0002, false discovery rate = 0.006). This observation
is supported by Carrari et al.’s (2006) finding that tran-
scripts for nucleotide conversion enzymes are strongly
affected during tomato development [32].
Conclusion
Cross-referencing metabolite identifiers and gathering
meta-data are essential technologies for metabolomics
data analysis. There are several public databases that
contain information on metabolites, but linking these
data with the local identifiers and compound names
often used in experimental data sets has been very diffi-
cult. Here we presented a novel strategy for creating a
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.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 9 of 11

Page 10

database that gathers and organizes information about
metabolites from both in-house reference libraries and
external resources. Our approach uses multiple identifier
types in parallel when consolidating databases, thereby
avoiding the problem of lacking a widely used identifier
scheme. Issues with redundant and missing entries are
addressed by importing multiple resources to create a
unified identifier database.
The MetMask tool provides an implementation of our
ideas and can be used to create tailored metabolite map-
pings with minimum user effort. Efficient handling of
identifiers enables data summarization and biological
interpretation via contextual analysis such as pathway
projections.
Availability and requirements
Project name: metmask
Project home page: http://metmask.sourceforge.net
Operating systems: Platform independent (tested
on Windows XP and Ubuntu Linux)
Programming language: Python
Other requirements: None
License: GNU GPL
Any restrictions to use by non-academics: None
Additional file 1: User manual for MetMask. Instructions for
installation and usage. Also available by the project webpage.
Additional file 2: Example reference library - CSV. The comma
separated text file based reference library used in the demonstration
section.
Additional file 3: Example reference library - NIST. The NIST MS data
export based reference library used in the demonstration section.
Additional file 4: Example input. The file with local identifiers used in
the demonstration section.
Acknowledgements
The authors thank Kouji Takano, Akiko Takahashi, Akane Suzuki and Dr
Takayuki Tohge for their efforts with curating our chemical reference library.
Dr Alisdair Fernie is thanked for providing access to the MPIMP MS library.
Dr Shin Watanabe, Dr Kyoko Tanase, Dr Tadayoshi Hirai, Dr Hiroshi Ezura for
providing tomato fruit material.
Authors’ contributions
HR initiated the project, designed and implemented the program and wrote
the manuscript. MK wrote the manuscript, provided GC-MS data and
evaluated the program. AF evaluated and tested the program and wrote the
manuscript. FM provided LC-MS data, curated our library of reference
compounds and contributed to the graph based visualization approach. KS
and MA supervised the project.
All authors read and approved the final version.
Received: 26 November 2009 Accepted: 29 April 2010
Published: 29 April 2010
References
1.Tokimatsu T, Sakurai N, Suzuki H, Ohta H, Nishitani K, Koyama T,
Umezawa T, Misawa N, Saito K, Shibata D: KaPPA-view: a web-based
analysis tool for integration of transcript and metabolite data on plant
metabolic pathway maps. Plant Physiol 2005, 138(3):1289-1300.
2. Usadel B, Nagel A, Thimm O, Redestig H, Bläsing OE, Palacios-Rojas N,
Selbig J, Hannemann J, Piques MC, Steinhauser D, Scheible WR, Gibon Y,
Morcuende R, Weicht D, Meyer S, Stitt M: Extension of the visualization
tool MapMan to allow statistical analysis of arrays, display of
corresponding genes, and comparison with known responses. Plant
Physiol 2005, 138(3):1195-1204.
Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J,
Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ,
Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES,
Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpha-responsive genes
involved in oxidative phosphorylation are coordinately downregulated
in human diabetes. Nat Genet 2003, 34(3):267-73.
Redestig H, Repsilber D, Sohler F, Selbig J: Integrating functional
knowledge during sample clustering for microarray data using
unsupervised decision trees. Biom J 2007, 49(2):214-229.
Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools:
paths toward the comprehensive functional analysis of large gene lists.
Nucleic Acids Res 2009, 37:1-13.
Lenz EM, Wilson ID: Analytical strategies in metabonomics. J Proteome Res
2007, 6(2):443-458.
Urano K, Maruyama K, Ogata Y, Morishita Y, Takeda M, Sakurai N, Suzuki H,
Saito K, Shibata D, Kobayashi M, Yamaguchi-Shinozaki K, Shinozaki K:
Characterization of the ABA-regulated global responses to dehydration
in Arabidopsis by metabolomics. Plant J 2008, 57:1065-1078.
Werf van der MJ, Overkamp KM, Muilwijk B, Coulier L, Hankemeier T:
Microbial metabolomics: toward a platform with full metabolome
coverage. Anal Biochem 2007, 370:17-25.
Williams R, Lenz EM, Wilson AJ, Granger J, Wilson ID, Major H, Stumpf C,
Plumb R: A multi-analytical platform approach to the metabonomic
analysis of plasma from normal and Zucker (fa/fa) obese rats. Mol Biosyst
2006, 2(3-4):174-183.
Zhang J, Carey V, Gentleman R: An extensible application for assembling
annotation for genomic data. Bioinformatics 2003, 19:155-156.
Côté RG, Jones P, Martens L, Kerrien S, Reisinger F, Lin Q, Leinonen R,
Apweiler R, Hermjakob H: The Protein Identifier Cross-Referencing (PICR)
service: reconciling protein identifiers across multiple source databases.
BMC Bioinformatics 2007, 8:401.
Li H, Ding G, Xie L, Li Y: PAnnBuilder: an R package for assembling
proteomic annotation data. Bioinformatics 2009, 25(8):1094-1095.
Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A,
Alcántara R, Darsow M, Guedj M, Ashburner M: ChEBI: a database and
ontology for chemical entities of biological interest. Nucleic Acids Res
2008, 36 Database: D344-D350.
KEGG Database: Kyoto Encyclopedia of Gene and Genomes. [http://www.
genome.jp/kegg], [Compounds Database].
Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, Westler WM,
Eghbalnia HR, Sussman MR, Markley JL: Metabolite identification via the
Madison Metabolomics Consortium Database. Nat Biotechnol 2008,
26(2):162-164.
Wishart DS: Human Metabolome Database: completing the ‘human parts
list’. Pharmacogenomics 2007, 8(7):683-686.
Knox C, Shrivastava S, Stothard P, Eisner R, Wishart DS: BioSpider: a web
server for automating metabolome annotations. Pac Symp Biocomput
2007, 145-156.
Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G,
Kasprzyk A: BioMart-biological queries made easy. BMC Genomics 2009,
10:22.
van Iersel MP, Pico AR, Kelder T, Gao J, Ho I, Hanspers K, Conklin BR,
Evelo CT: The BridgeDb framework: standardized access to gene, protein
and metabolite identifier mapping services. BMC Bioinformatics 2010, 11:5.
Kind T, Scholz M, Fiehn O: How large is the metabolome? A critical
analysis of data exchange practices in chemistry. PLoS ONE 2009, 4(5):
e5440.
Schauer N, Steinhauser D, Strelkov S, Schomburg D, Allison G, Moritz T,
Lundgren K, Roessner-Tunali U, Forbes MG, Willmitzer L, Fernie AR, Kopka J:
GC-MS libraries for the rapid identification of metabolites in complex
biological samples. FEBS Lett 2005, 579(6):1332-1337.
PubChem: Database of chemical compounds. 2009 [http://www.ncbi.nlm.
nih.gov/sites/entrez?db=pccompound], [Compounds].
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 10 of 11

Page 11

23.Karp PD, Riley M, Saier M, Paulsen IT, Collado-Vides J, Paley SM, Pellegrini-
Toole A, Bonavides C, Gama-Castro S: The EcoCyc Database. Nucleic Acids
Res 2002, 30:56-58.
Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD:
Computational prediction of human metabolic pathways from the
complete human genome. Genome Biol 2005, 6:R2.
Plant Metabolic Network: PlantCyc. 2009 [http://www.plantcyc.org], [Plant
metabolite database].
R Development Core Team: R: A language and environment for statistical
computing R Foundation for Statistical Computing, Vienna, Austria 2004.
Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B,
Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R,
Li FLC, Maechler M, Rossini A, Sawitzki G, Smith C, Smyth G, Tierney L,
Yang J, Zhang J: Bioconductor: Open software development for
computational biology and bioinformatics. Genome Biol 2004, 5:R80.
Pages H, Carlson M, Falcon S, Li N: AnnotationDbi: Annotation Database
Interface 2009, [R package version 1.6.1].
SQLite: Lightweight database. [http://www.sqlite.org], [Self-contained, zero-
configuration].
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N,
Schwikowski B, Ideker T: Cytoscape: a software environment for
integrated models of biomolecular interaction networks. Genome Res
2003, 13(11):2498-2504.
Carey VJ, Gentry J, Whalen E, Gentleman R: Network structures and
algorithms in Bioconductor. Bioinformatics 2005, 21:135-136.
Carrari F, Baxter C, Usadel B, Urbanczyk-Wochniak E, Zanor MI, Nunes-
Nesi A, Nikiforova V, Centero D, Ratzka A, Pauly M, Sweetlove LJ, Fernie AR:
Integrated analysis of metabolite and transcript levels reveals the
metabolic shifts that underlie tomato fruit development and highlight
regulatory aspects of metabolic network behavior. Plant Physiol 2006,
142(4):1380-1396.
Akiyama K, Chikayama E, Yuasa H, Shimada Y, Tohge T, Shinozaki K,
Hirai MY, Sakurai T, Kikuchi J, Saito K: PRIMe: a Web site that assembles
tools for metabolomics and transcriptomics. In Silico Biol 2008, 8(3-
4):339-345.
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T,
Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y: KEGG for linking
genomes to life and the environment. Nucleic Acids Res 2008, 36
Database: D480-D484.
PubChem: Database of chemical substances. 2009 [http://www.ncbi.nlm.
nih.gov/sites/entrez?db=pcsubstance], [Substance].
Shinbo Y, Nakamura Y, Altaf-Ul-Amin M, Asahi H, Kurokawa K, Arita M,
Saito K, Ohta D, Shibata D, Kanaya S: Plant Metabolomics, Springer 2006
chap. II.6: KNApSAcK: A Comprehensive Species-Metabolite Relationship
Database Saito K, Dixon RA, Willmitzer L 165-184.
Taguchi R, Nishijima M, Shimizu T: Basic analytical systems for lipidomics
by mass spectrometry in Japan. Methods Enzymol 2007, 432:185-211.
Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH,
Murphy RC, Raetz CRH, Russell DW, Subramaniam S: LMSD: LIPID MAPS
structure database. Nucleic Acids Res 2007, 35 Database: D527-D532.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
doi:10.1186/1471-2105-11-214
Cite this article as: Redestig et al.: Consolidating metabolite identifiers
to enable contextual and multi-platform metabolomics data analysis.
BMC Bioinformatics 2010 11:214.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
Redestig et al. BMC Bioinformatics 2010, 11:214
http://www.biomedcentral.com/1471-2105/11/214
Page 11 of 11