HMDB: a knowledgebase for the human metabolome.

Abstract

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

Full text

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Published online 25 October 2008Nucleic Acids Research, 2009, Vol. 37, Database issueD603–D610
doi:10.1093/nar/gkn810
HMDB: a knowledgebase for the human metabolome
David S. Wishart1,2,3,*, Craig Knox1, An Chi Guo1, Roman Eisner1, Nelson Young1,
Bijaya Gautam1, David D. Hau1, Nick Psychogios1, Edison Dong1,
Souhaila Bouatra1, Rupasri Mandal1, Igor Sinelnikov1, Jianguo Xia2, Leslie Jia1,
Joseph A. Cruz1, Emilia Lim1, Constance A. Sobsey1, Savita Shrivastava1,
Paul Huang4, Philip Liu1, Lydia Fang1, Jun Peng4, Ryan Fradette4, Dean Cheng1,
Dan Tzur1, Melisa Clements4, Avalyn Lewis4, Andrea De Souza4, Azaret Zuniga4,
Margot Dawe4, Yeping Xiong4, Derrick Clive4, Russ Greiner1, Alsu Nazyrova5,
Rustem Shaykhutdinov5, Liang Li4, Hans J. Vogel5and Ian Forsythe1
1Department of Computing Science,2Department of Biological Sciences, University of Alberta, Edmonton, AB,
Canada T6G 2E8,3National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G
2M9,4Department of Chemistry, University of Alberta, Edmonton, AB, Canada T6G 2G2 and5Department of
Biological Sciences, University of Calgary, Calgary, AB, Canada T2N 1N4
Received September 15, 2008; Revised October 9, 2008; Accepted October 10, 2008
ABSTRACT
The Human Metabolome Database (HMDB, http://
www.hmdb.ca)isa richly
that is designed to address the broad needs of bio-
chemists, clinical chemists, physicians, medical
geneticists, nutritionists and members of the meta-
bolomics community. Since its first release in 2007,
the HMDB has been used to facilitate the research
for nearly 100 published studies in metabolomics,
clinical biochemistry and systems biology. The
most recent release of HMDB (version 2.0) has
been significantly expanded and enhanced over
the previous release (version 1.0). In particular, the
number of fully annotated metabolite entries has
grown from 2180 to more than 6800 (a 300%
increase), while the number of metabolites with bio-
fluid or tissue concentration data has grown by a
factor of five (from 883 to 4413). Similarly, the
number of purified compounds with reference to
NMR, LC-MS and GC-MS spectra has more than
doubled (from 380 to more than 790 compounds).
In addition to this significant expansion in database
size, many new database searching tools and new
data content has been added or enhanced. These
include better algorithms for spectral searching
and matching, more powerful chemical substructure
searches, faster text searching software, as well
asdedicated pathway
annotated resource
searchingtools and
customized, clickable metabolic maps. Changes to
the user-interface have also been implemented to
accommodate future expansion and to make data-
base navigation much easier. These improvements
should make the HMDB much more useful to a
much wider community of users.
INTRODUCTION
Over the past 3 years, metabolomics has evolved from a
little-known branch of analytical chemistry to a main-
stream enterprise being practiced by hundreds of labora-
tories around the world. Thanks to technical advances in
NMR spectroscopy, mass spectrometry and compound
separation, it is now possible to identify and quantify
hundreds of metabolites (i.e. the metabolome) from
many different types of biological samples in relatively
short order. This information can be used in a variety of
applications including biomarker identification, drug dis-
covery or development, clinical toxicology, nutritional stu-
dies and quantitative phenotyping of plants or microbes
(1, 2). When combined with genomic, transcriptomic and/
or proteomic studies, metabolomics can also help in the
interpretation and understanding of many complex biolo-
gical processes. Indeed, metabolomics is now widely recog-
nized as being a cornerstone to all of systems biology (3).
As with any ‘omics’ discipline, metabolomics is highly
dependent on the availability and quality of electronic
databases. Furthermore, because metabolomics combines
molecular biology with chemistry and physiology, there is
*To whom correspondence should be addressed. Tel: +1 780 492 0383; Fax: +1 780 492 1071; Email: david.wishart@ualberta.ca
? 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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a need for not just one type of database, but a wide variety
of electronic resources. Currently, there are at least five
types of databases used in metabolomics research. These
include: (i) metabolic pathway databases; (ii) compound-
specific databases; (iii) spectral databases; (iv) disease/
physiology databases; and (v) comprehensive, organism-
specific metabolomic databases. KEGG database (4), the
‘Cyc’ databases (5) and the Reactome database (6) are
examples of some of the more popular metabolic pathway
databases. These resources contain carefully illustrated,
hyperlinked metabolic pathways with synoptic metabolite
information for a wide range of organisms. On the other
hand, compound-specific databases such as Lipid Maps
(7), KEGG Glycan (4), DrugBank (8), ChEBI (9) and
PubChem (10) contain essentially no pathway informa-
tion. Rather, they focus on providing detailed nomencla-
ture, structural or physicochemical data on restricted
classes of compounds, such as lipids, carbohydrates,
drugs, toxins or other chemicals of biological interest.
These somewhat specialized databases often contain meta-
bolites or xenobiotics not found in most metabolic path-
way databases. Spectral databases for metabolomics
include the BMRB (11), MMCD (12), MassBank (13),
the Golm Metabolome database (14) and Metlin (15).
These very valuable resources contain reference NMR,
GC-MS and/or LC-MS spectra for a wide variety of
small molecules along with software to identify these com-
pounds via spectral matching. Disease and physiology
databases (or encyclopedias) commonly used in metabo-
lomics include OMIM (16), METAGENE (17) and
Scriver’s OMMBID (18). These contain descriptions of
the causes, clinical symptoms, diagnostic indicators or
genetic mutations associated with many metabolic disor-
ders. Finally, organism-specific, comprehensive metabolo-
mic databases—or knowledgebases—attempt to combine
all of the information from most of the four kinds of
databases into a single resource. Examples of these include
BiGG (19), SYSTONOMAS (20) and the Human
Metabolome Database or HMDB (21).
First described in 2007, the HMDB is currently the
largest and most comprehensive, organism-specific meta-
bolomics database assembled to date. It contains spectro-
scopic,quantitative, analytic
information about human metabolites, their associated
enzymes or transporters, their abundance and disease-
related properties. Since its initial release, the HMDB
has been used in a wide range of metabolomics applica-
tions including the characterization and rationalization of
biomarkers for multiple sclerosis (22), the identification of
metabolites with anticancer properties (23) and the net-
work modeling of liver cancer (24). Feedback from users
has led to many excellent suggestions on how to expand
and enhance HMDB’s offerings. Likewise, continued
advances in the field of metabolomics along with ongoing
data collection and curation by the Human Metabolome
Project (HMP) team has led to a substantial expansion of
the HMDB’s content. Here, we wish to report on these
developments as well as many additions and improve-
ments appearing in the latest version of the HMDB
(release 2.0).
and molecular-scale
DATABASE ENHANCEMENTS
Details regarding the HMDB’s overall design, data pre-
sentation format, data sources, curation protocols, data
management system, quality assurance and metabolite
selection criteria have been described previously (21).
These have largely remained the same between releases
1.0 and 2.0. Here, we shall focus primarily on describing
the changes and improvements made to the HMDB. More
specifically, we will describe the: (i) enhancements to the
HMDB’s content, completeness and coverage; (ii) imp-
rovements to the HMDB’s interface; (iii) enhancements
to its spectral databases and searching; and (iv) improve-
ments to the HMDB’s data querying and data viewing.
Expanded databasecontent, completeness and coverage
A detailed content comparison between the HMDB
(release 1.0) versus the HMDB (release 2.0) is provided
in Table 1. As seen here, the latest release of the HMDB
now has detailed information on 6826 experimentally con-
firmed metabolites, representing an expansion of nearly
300% over the previous database. This increase is primar-
ily due to the addition of more than 3800 lipids that have
recently been experimentally detected and/or quantified in
human tissues and biofluids. The addition of so many
lipids reflects the fact that lipid detection and identifica-
tion technologies are rapidly improving, leading to a
greater number of lipid species being reported in the lit-
erature or being accessible via commercial lipidomic
assays (25). While these technological improvements are
impressive, it is still important to remember that upwards
of 20 000 lipids could theoretically exist in the human
body. Therefore it appears that only ?20% of all possible
lipids are detectable with today’s technology.
Other classes of compounds that have seen substantial
increases in numbers over the past 2 years include glucur-
onides, carnitines, bile acids and coenzyme A derivatives.
In many cases, these additions do not represent the dis-
covery of new compounds, but simply reflect improve-
ments in the HMDB curation team’s ability to identify
(with the assistance of text mining tools) and archive
metabolites previously reported in the literature. Currently
?60% of the metabolites in the HMDB have been identi-
fied or confirmed by the HMDB’s team of analytical che-
mists using NMR, LC-MS or GC-MS methods applied to
a variety of human biofluids. Likewise, ?45% (2900/6475)
of the metabolites in the HMDB have been identified and
archived through literature surveys or electronic data
mining. It is also worth noting that many of the most
commonly used metabolite databases (KEGG, Human-
Cyc, BiGG or Lipid Maps) only list about one-fifth the
number of metabolites found in the HMDB. We believe
this statistic underscores the uniqueness and comprehen-
siveness of the HMDB in describing human metabolism.
In addition to substantially increasing the number of
metabolite entries, we have also increased the complete-
ness of the HMDB’s annotations for hundreds of meta-
bolitesby addingmany
descriptions, including more synonyms (60% increase),
doubling the number of compounds with NMR and MS
spectra, increasing the number of compounds with
more detailedcompound
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biofluid concentration data by a factor of five and increas-
ing the number of compounds with synthesis records by a
factor of eight. Beyond these changes, a substantial effort
was also made to manually classify all compounds in the
HMDB into chemicals ‘kingdoms’, ‘classes’ and ‘families’.
The chemical class information is particularly useful for
metabolite comparison and classification. Table 2 pro-
vides a list of the 52 metabolite classes used by the
HMDB and the number of compounds found in each
class. In choosing these chemical class names, the
HMDB curation team assessed a number of previously
published chemical classification schemes (used in plant
and microbial metabolomics) and attempted to select
those class names that were most commonly used or
most chemically informative. Of course, no classifica-
tion scheme is perfect and the current ontology simply
represents a compromise of many competing needs,
ideas and preferences. Nevertheless, we believe this
kind of chemical ontology should help to provide a
common language for large-scale mammalian metabolome
comparisons.
Thanks to the feedback provided by HMDB’s user
community, a number of new data fields have been
added to each MetaboCard in order to facilitate certain
types of queries or comparisons. These include chemical
source information (endogenous versus exogenous), phy-
siological charge, experimental and predicted logP,
HMDB pathway images, general metabolite references
and macromolecular interacting partners (such as trans-
porters or proteins that use the metabolites as co-factors).
New data fields have also been added for the BiGG data-
base, Wikipedia and METLIN (for metabolites) while
extra data fields for GeneCard IDs, GeneAtlas IDs and
HGNC IDs have been added for each of the correspond-
ing enzymes. In addition to these changes, new data fields
for NMR assignment files (both1H and13C) in the BMRB
NMR?exchange format (11) have been inserted as well as
data fields for experimental1H-13C HSQC spectra, simpli-
fied TOCSY spectra and BMRB TOCSY spectra.
Over and above these changes, the normal and abnormal
biofluid concentration data fields have also been consoli-
dated (from 10 to 2) and reformatted for improved
viewing.
Table 1. Content comparison of HMDB 1.0 with HMDB 2.0
Database feature or content statusHMDB (v 1.0) HMDB (v 2.0)
Number of metabolites
Number of unique
metabolite synonyms
Number of compounds
with disease links
Number of compounds
with biofluid or tissue
concentration data
Number of compounds
with chemical synthesis
references
Number of compounds
with urine concentration data
Number of compounds with
serum concentration data
Number of compounds with
cerebrospinal fluid
concentration data
Number of compounds with
experimental reference
13C NMR spectra
Number of compounds with
experimental reference
1H NMR spectra
Number of compounds with
predicted NMR spectra
Number of compounds with
reference MS-MS spectra
Number of compounds with
GC-MS reference data
Number of human-specific
pathway maps
Number of compounds in
Human Metabolome
Library (HML)
Number of HMDB data fields
Pathway search/browse
Disease search/browse
Chemical class search/browse
Chemical substructure search
Biofluid search/sort tools
Advanced (multipeak or
multicompound) NMR search
Advanced (multipeak or
multicompound) MS-MS search
Advanced (retention index or
MS peak) GC-MS search
2180
27700
6826
43882
862 1002
8834413
2201647
231472
174 3976
47 360
380784
385 792
19003044
390799
0 279
26 58
607 920
91
No
No
No
No
No
No
102
Yes
Yes
Yes
Yes
Yes
Yes
No Yes
No Yes
Table 2. Chemical classes in the HMDB (v 2.0)
Compound class NumberCompound classNumber
Minerals and elements
Fatty acids
Alcohols and polyols
Keto acids
Carbohydrates
Purines and purine
derivatives
Catecholamines and
derivatives
Acyl phosphates
Phospholipids
Amino ketones
Glycerolipids
Retinoids
Pterins
58
126
103
31
195
32
Polyphenols
Dicarboxylic acids
Alkanes and alkenes
Glycolipids
Hydroxy acids
Prostanoids
54
70
26
138
97
54
34Peptides 69
37 Nucleotides
Cyclic amines
Nucleosides
Aromatic acids
Amino alcohols
Steroids and steroid
derivatives
Leukotrienes
Indoles and indole
derivatives
Sugar phosphates
Glucuronides
Sugar phosphates
Miscellaneous
Bile acids
Amino acid
phosphates
Quinones and
derivatives
Pyridoxals and
derivatives
Acyl glycines
Lipoamides and
derivatives
Polyamines
106
55
52
71
27
323
2630
45
1163
26
47
Carnitines
Amino acids
48
234
79
32
Porphyrins
Coenzyme A derivatives
Ketones
Inorganic ions and gases
Sphingolipids
Alcohol phosphates
54
117
23
34
19
22
66
74
66
118
84
10
Aldehydes2116
Pyrimidines and
pyrimidine derivatives
Tricarboxylic acids
Cobalamin derivatives
139
9
9
37
10
Biotin and derivatives65
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We believe that one of the more important improve-
ments to the HMDB concerns the addition of nearly 60
hand-drawn, zoomable and fully hyperlinked human
metabolic pathway maps (Fig. 1). While the HMDB still
maintains full linkage to nearly 100 KEGG pathways, the
addition of these ‘custom’ maps to the HMDB arose from
requests by users who were dissatisfied with being unable
to visualize the chemical structures on metabolic maps or
unable to get detailed information about human metabolic
enzymes. Unlike, most online metabolic maps, these
HMDB pathway maps are quite specific to human meta-
bolism and explicitly show the subcellular compartments
where specific reactions are known to take place. All che-
mical structures in these pathway maps are hyperlinked to
HMDB MetaboCards and all enzymes are hyperlinked to
UniProt data cards for human enzymes. They are also
searchable (via PathSearch) in a manner that is more con-
ducive to typical metabolomics queries (see below).
In addition to these changes, a substantial effort has
also been put into identifying and correcting a number
of structural, image format, naming, annotation and spec-
tral assignment errors in the HMDB. While a number of
internal checking and editing procedures are used by the
HMDB curation team [see (21) for details], we are parti-
cularly grateful to external users who identified more
subtle errors or offered suggestions to improve the data
quality. Interestingly, a number of errors were found to be
‘propagation’ errors arising from the transfer of erroneous
data from one well-regarded database to another. In addi-
tion to these error corrections, a substantial update to the
HMDB’s metabolite–enzyme associations has also been
completed. Indeed, all enzyme–metabolite associations
that were automatically ‘text-mined’ have now been
manually verified by multiple HMDB annotators. While
it is difficult to formally quantify these changes or correc-
tions, we can say that the quality of the data in release 2.0
is generally much better than the previous release.
User interface improvements
Both the front-end and selected components of the back-
end of the HMDB have been substantially redesigned to
accelerate searches, improve data visualization and allow
greater flexibility in the number of query tools and links
that can be provided by the database. The HMDB’s navi-
gation bar (located at the top of each page) has been
simplified to just six pull-down menu tabs (‘Home’,
‘Browse’, ‘Search’, ‘About’, ‘Download’ and ‘Contact
Us’). The ‘Browse’ tab allows users to select from six
browsing options (HMDB Browse, Biofluid Browse,
HML Browse, ClassBrowse, PathBrowse and Disease
Browse) of which the last four are new. The HML
Browse allows users to browse or search through the
HML. The HML is a library of ?1000 reference metabo-
lites stored in ?808C freezers. Small amounts of these
compounds are freely available to designated HMDB col-
laborators. They are also available on a cost-recovery
basis to other laboratories on an as-needed basis. The
second of the new browsing tools, ClassBrowse, allows
users to view compounds according to their chemical
class designation. Each displayed compound name is
hyperlinked to the HMDB MetaboCard. Users may
search for compounds (via a text box) or select to view
certain compound classes using a pull-down menu located
that the top of the ClassBrowse page. The third browsing
tool, PathBrowse, allows users to browse through the
custom-drawn HMDB pathway images. Each pathway is
named and each image is zoomable and extensively hyper-
linked. Users may also search PathBrowse using lists of
compounds (obtained from a metabolomic experiment)
and view hyperlinked tables that display all of the path-
ways that are potentially affected. The last browsing
tool, Disease Browse, allows users to scroll and search
through tables of diseases, which are co-listed with hyper-
linked metabolite and enzyme/protein names. As with
PathBrowse users may submit multiple lists of compounds
and then view hyperlinked tables of diseases or conditions
that may be associated with the observed metabolic
changes.
The HMDB’s ‘Search’ menu offers eight different
querying toolsincluding
SequenceSearch, DataExtractor, MS search, MS-MS
search, GC-MS search and NMR search. While only the
GC-MS and MS search features are new, significant
improvements in terms of speed, accuracy and robustness
have been made to many of the other query tools. These
enhancements are described in detail in later sections of
this article. Adjacent to the ‘Search’ menu, the ‘About’
pull-down menu contains information on the HMDB
database, release notes, recent news or updates, database
statistics, data source tables, data field explanations and
links to other useful metabolomic databases. Finally, the
‘Download’ menu contains downloadable data for all
HMDB compounds (in SDF format), all NMR spectra
(in BMRB?format and as PNG images), all GC-MS spec-
tra (in NIST format), all MS-MS spectra (as PNG
images), all enzyme/protein sequences as well as complete
flat file data sets of current and past HMDB releases.
Over and above these enhancements to the menu struc-
ture and database navigation scheme, improvements have
also been made to the formatting and display of all of
HMDB’s MetaboCards. For instance, certain data fields
have been reordered to bring logically similar data sets
(such as structure files or pathway diagrams) closer
together in each MetaboCard. Other data fields (such as
the NMR and MS spectral data fields) have had extra
information added to the data cell, such as collection con-
ditions and FID data. In other cases, data fields have
reformatted to provide more information in a more struc-
tured manner. For example, the information in normal
and abnormal biofluid concentrations, data cell has been
reformatted to display much more data in a more readable
tabular format. A similar change has been made to the
associated disorders field. Likewise all PubMed IDs and
abbreviated chemical synthesis references have been
replaced with full reference information (authors, title,
journal, volume, page, year). In a similar manner, the
SNP (single nucleotide polymorphism) data field (found
in HMDB’s Enzyme section) has also been modified so
that SNPs aredisplayed
mary tables containing information on their type (synon-
ymous, nonsynonymous), location, validation status and
ChemQuery,TextQuery,
inhyperlinkedsum-
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Figure 1. A screenshot of the HMDB pathway image for glycolysis/gluconeogenesis as found in humans. All metabolite structures and enzyme IDs
are hyperlinked to the HMDB and UniProt, respectively.
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population distributions. This change to the SNP data
field has also made the browsing of MetaboCards much
faster and less taxing on our servers.
Enhancements tospectral databases and spectral searching
In genomics and proteomics, most genes and proteins are
identified via sequence comparisons against libraries on
known sequences. In metabolomics, most compounds
are identified via spectral comparisons against libraries
of known compound spectra. Consequently, there is a
critical need by many metabolomics researchers for com-
prehensive, publicly accessible libraries of reference com-
pound spectra. There is also an equally strong need for
robust search algorithms to perform spectral matching
and compound identification. Over the past 18 months,
the HMDB’s analytical chemistry team has been actively
collecting, assigning and verifying reference NMR, GC-
MS and MS-MS spectra for all compounds in the HML.
As seen in Table 1, the number of compounds with experi-
mentally acquired NMR and MS-MS spectra has more
than doubled. Likewise, a completely new set of 279
experimentally acquired GC-MS spectra (with retention
index data) has just been added. In another 6 months,
the number of compounds with GC-MS spectra should
nearly equal the number of compounds with NMR or
MS-MS data.
In keeping with our open access mandate, all experi-
mentally acquired NMR spectra in the HMDB are avail-
able in BMRB?format and as fully labeled PNG images.
Likewise, all GC-MS spectra are available in NIST-
AMDIS format, while all MS-MS spectra available as
PNG images. What is particularly unique about the
HMDB’s NMR data is that all compounds are fully
assigned (both1H and13C shifts) under standardized aqu-
eous conditions. While reference spectral collection and
deposition is continuing, it is expected that data for
fewer than 100 compounds will be added over the
coming year. This slowdown simply reflects the fact that
pure standards of many metabolites are neither commer-
cially available nor are they easily synthesized.
Thanks to suggestions from the user community, a
number of enhancements to the MS-MS, MS and NMR
search routines have been made. The HMDB’s MS-MS
search now allows users to search for compounds (with
experimental MS-MS data) by name, synonym, molecular
formula or parent ion mass. The complete, scrollable list
of compounds with experimental MS-MS data is also
viewable. The MS-MS peak search has also been
improved by the addition of more search options and
more detailed descriptions on how to use the query
engine. The results from the MS-MS peak search query
nowreturndataonthespectralfitqualityalongwithhyper-
links to the MetaboCards of the matching compounds.
Also included is the corresponding MS-MS spectrum, the
data collection protocol and the MS-MS peak list.
For the MS search, users can search for compounds by
parent ion mass in three different modes (positive ion,
negative ion and neutral) against four different data-
basesincluding theHMDB,
(a food additive and phytochemical database containing
DrugBank,FooDB
?2000 compounds) or all four databases together.
Adducts (Na+, K+, NH4+, etc.) for all entries in each
of the databases have been precalculated allowing users to
identify potential adduct matches to the observed parent
ion masses.
As with the MS-MS search, the NMR search supports
queries for compounds (with experimental or predicted
shifts) by name, synonym, molecular formula or molecu-
lar weight. Users may search against different types of
NMR data including 1D
2D
for a pure compound or for a mixture of several dozen
compounds (from a biofluid or tissue extract). Users may
also select what kind of biofluid/extract they are analyzing
(urine, CSF, plasma, cell extracts or undefined). The
results from an NMR peak list query will return the
name of the compound(s), the spectral matching score
along with hyperlinks to each matching compound’s spec-
tral peak list and the category of spectrum matched (pre-
dicted or experimental). The algorithm used in the
HMDB’s NMR search combines peak matching with
peak uniqueness and pairwise peak distance measures
along with specific knowledge of specific biofluid compo-
sitions to identify compounds. The performance of the
algorithm, when assessed with real and synthetic biofluid
mixtures of up to 30 compounds (corresponding to several
hundred peaks), was found to achieve >80% identifica-
tion success using either TOCSY or1H-13C HSQC data.
This was 2-3X better than other NMR spectral matching
algorithms. Additional details about the algorithm, the
comparative performance and its limitations are given
elsewhere (26).
1H, 1D
13C, 2D TOCSY and
1H-13C HSQC spectra. The input peak list may be
Improvements in dataquerying and viewing
As mentioned earlier, improvements to the performance
and speed for a number of HMDB query functions have
been implemented with release 2.0. For both the general
text search and the more specialized TextQuery functions,
the HMDB now uses KinoSearch (27). This particular text
query system is approximately five times faster than the
previous system and supports text match rankings, mis-
spellings (offering suggestions for incorrectly spelled
words) and highlights text where the word is found.
Consequently, general text queries now rapidly produce
a table of hits that provides the HMDB ID, a Metabo
Card link, the common name, the formula, the molecular
weight and the text or sentence(s) where the query word is
most frequently found. HMDB’s TextQuery function not
only uses the same KinoSearch engine, but also supports
more sophisticated text querying functions (Boolean logic,
multiword matching and parenthetical groupings) as well
as data-field-specific queries (such as finding the query
word only in the ‘Compound Source’ field). Additional
details and examples are provided on the HMDB’s
TextQuery page. The Data Extractor has also been com-
pletely rewritten and the algorithm has been substantially
sped up. This tool supports much more specialized queries
and now provides users with the ability to output their
data in HTML, HTML-printable and comma separated
value (Excel compatible) formats.
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The ChemQuery function has also been revamped,
replacing the old, multistep conversion and query process
with ChemAxon’s single-step structure query tool. With
this new and improved structure query system, users may
draw a structure (using a chemical drawing applet) or
paste a SMILES string directly into the structure drawing
palette to query the HMDB structure database. Users can
also select the type of search (exact or Tanimoto score) to
be performed. We have found that the new structure
querying tool is able to provide much more consistent
structure matches than our ‘home-built’ structure match-
ing tool used in release 1.0. The same ChemAxon structure
querying applet is also used with the ‘Find Similar
Structures’ button located at the top of every Metabo
Card. Overall, we believe the improvements to many of
the text and structure querying tools in this release of the
HMDB should make data searching and data extraction
much easier, more robust and significantly faster.
CONCLUSION
The HMDB is designed to be a comprehensive, web-
accessible metabolomics database that brings together
quantitative chemical, physical, clinical and biological
data about all experimentally ‘proven’ or experimentally
detected human metabolites. Over the past 2 years, a sig-
nificant expansion to the content as well as a significant
enhancement to the database’s capabilities has taken
place. Many of these content additions and content cor-
rections are the result of continued experimental and lit-
erature mining efforts by the HMDB curatorial and
analytical chemistry staff. Likewise, many of the graphical
interface and query function improvements, which arose
primarily from external user suggestions, are the result of
significant programing efforts by the HMDB software
development team. Overall, we believe these improve-
ments to the query functions and enhancements to the
database content should make the HMDB much more
useful to a much wider collection of metabolomics
researchers.
Unlike the human genome, the human metabolome is
not a finite or easily defined entity (2). Certainly, as tech-
nology improves and detection limits decrease, it is likely
that many more metabolites will be identified (by ourselves
and others) or reported in the literature. What this parti-
cularreleaseoftheHMDB providesisarelatively complete
picture of what is detectable in the human metabolome as
of 1 January 2009. No doubt the size of the human meta-
bolome will continue to grow (although, not as quickly as
the past 2 years), as will the collection of reference com-
pound spectra and our knowledge of metabolite concentra-
tions, pathways, enzyme and disease associations. In an
effort to keep the HMDB as current as possible, we
intend to release database updates every 6 months (1 July
and 1 January) for at least the next 2 years.
FUNDING
Alberta Advanced Education and Technology (AAET);
Canadian Institutes ofHealth Research (CIHR);
Alberta
(AICML); Alberta Ingenuity Fund (AIF); Genome
Alberta, a division of Genome Canada.
IngenuityCentre for MachineLearning
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