Toxicology, the study of poisons, focuses on substances and treatments that
cause adverse effects in living things. A critical part of this study is the
characterization of the adverse effects at the level of the organism, the tissue,
the cell, and the molecular makeup of the cell. Thus, studies in toxicology
measure effects on body weight and food consumption of an organism, on individual
organ weights, on microscopic histopathology of tissues, and on cell viability,
necrosis, and apoptosis. Recently added to the arsenal of end points that such
toxicological studies can use is the measurement of levels of the thousands
of proteins and mRNAs present in the cell. The former measurement was made
possible with the advent of two-dimensional gel electrophoresis and forms the
basis of the field of proteomics. The latter measurement was made possible
with the advent of whole genomic sequencing and the subsequent development
of microarrays capable of measuring thousands of transcripts at once and is
best described as transcript profiling, although it has often been referred
to as genomics or transcriptomics. The application of these technologies to
toxicology is based on the assumption that the sequelae of events leading to
adverse events at the cellular and organism levels will include critical changes
in certain mRNAs and proteins. Consequently, these changes may give insight
into the molecular mechanisms of toxicity and/or may be diagnostic for a given
mode of toxicity. Thus the number of toxicology studies incorporating either
proteomics or transcript profiling has been exponentially increasing for several
years.
Although both proteomics and transcript profiling measure molecular events
at a global and cellular levels, the two are dramatically different in both
technology and readout. Proteomics relies on the physical separation of all
the proteins of a sample, usually by means of two separate characteristics
such as charge and molecular weight, followed by detection of the protein with
a dye, and finally, identification by means of mass spectrometry. Transcript
profiling with microarrays makes use of hundreds to thousands of defined probes,
each of which is intended to detect a single mRNA molecule. The mRNA sample
is labeled and hybridized to the microarray such that the signal at a given
probe is related to the amount of that particular mRNA in the sample. This
readout characteristic makes microarray-based transcript profiling particularly
appealing because the identities of the signals are predetermined. In this
sense, data generated in transcript profiling experiments are rather straightforward.
However, because of the relatively poor annotation of expressed genes and sequence
tags, particularly in the dog and rat, the interpretation of transcript profiling
experiments is challenging.
The field of toxicogenomics integrates the data-rich science of transcript
profiling with traditional toxicological end point evaluation. If successfully
implemented, this integration has the potential to serve as a powerful synergistic
tool for understanding the relationship between gross toxicology and genome-level
effects. From its inception the field of transcript profiling using microarrays
has, through the sheer volume of data involved, required incorporation of resources
for bioinformatics, data management, and statistical analysis (Bassett et al.
1999; Eisen et al. 1998; Ermolaeva et al. 1998). The addition of toxicology
information to these data poses additional and unique informatics challenges.
A typical toxicogenomics study might involve an animal study with three dose
groups (one vehicle group, one low-dose group, and one high-dose group), two
to three sacrifice times, and four to five animals per group. Even if only
one tissue is examined per animal, this represents 36-45 arrays per study,
not including replicates. In addition, each animal will have associated data
on total body and organ weight measurements, clinical chemistry measurements
(often up to 25 parameters), and microscopic histopathology for several tissues.
The challenges and opportunities for a rigorous toxicogenomics database are
the capture, storage, and integration of a large volume of diverse data. Several
commercial ventures, including GeneLogic (Gaithersburg, MD; www.genelogic.com)
(Castle et al. 2002), Curagen (New Haven, CT; http://www.curagen.com/)
(Rininger et al. 2000), and Iconix Pharmaceuticals (Mountain View, CA; http://www.iconixpharm.com/)
have developed proprietary databases of this type.
In this article we focus on the development of public toxicogenomics databases
and the application of international database standards in that process. The
authors acknowledge that the review does not include all public databases of
microarray toxicogenomics experiments.
Role of Public Toxicogenomics Databases
Although several reports have described software for managing genomic/transcript
profiling data at the local or laboratory level (Bumm et al. 2002; Bushel et
al. 2001; Ermolaeva et al. 1998; Liao et al. 2000; Stoeckert et al. 2001),
there are compelling reasons for the establishment of public databases that
house not only such transcript profiling data but also the associated toxicological
end points. First and foremost is that such a public warehouse would provide
a means for the scientific community to publish and share the data from such
experiments to advance understanding of biological systems. These repositories
would also serve as a resource for data mining and discovery of expression
patterns common to certain experimental conditions. In addition, a public repository
would offer the regulatory community a resource for comparison with toxicogenomics
data submitted as part of the compound registration process (Petricoin et al.
2002). Deposition of data into public databases has already been proposed as
a requirement for journal publication of standard genomics experiments (Anonymous
2002; Ball et al. 2002), and public databases for microarray data have been
established (Anonymous 2002; Brazma et al. 2003; Edgar et al. 2002).
Another important function of some public repositories is the promotion of
international standards in data organization and nomenclature (Anonymous 2002;
Bassett et al. 1999; Brazma et al. 2001; Stoeckert et al. 2002). Particularly
in the case of biological data, the establishment of standard ontologies allows
uniform analysis of diverse data (Ashburner et al. 2000). Finally, public toxicogenomics
databases would also offer the larger toxicology community common resources
for comparing analytical tools and discussing experimental approaches. Thus
a database that organizes results from diverse laboratories and platforms would
allow the identification of experimental practices that introduce undesirable
variability into toxicogenomics data. Although there are challenges for developing
public databases that combine genomic and toxicological data, the example of
an international infrastructure for nucleotide sequence data such as the GenBank/EMBL/DDBJ
(European Molecular Biology Laboratory/DNA Data Bank of Japan) collaboration
points to the vast benefit that the larger scientific community would reap
from them.
Challenges
Despite the obvious scientific benefits of public toxicogenomics resources,
as described below, many technical and logistical issues challenge their implementation.
These problems may be broadly classified as a) approaches addressing
accuracy and specificity, b) standardization of data inputs, c)
methods assuring data quality and comparability, and d) development
and design of standardization experiments.
Accuracy and Specificity
The use of advanced data referencing and analysis tools is valuable only
to the extent that the data employed by these tools have a high degree of internal
accuracy. However, the inherent dynamics of hybridization coupled with the
incomplete nature of genomic sequence information create the potential for
imprecision and/or error in a transcript profiling experiment even before the
assay is run.
Hybridization specificity. At the design stage, each element
of a microarray, be it a cDNA clone or oligonucleotide sequence, must be selected
from the thousands of entries in sequence databases on the basis of several
characteristics (Lockhart et al. 1996; Schena et al. 1995). The first of these
is specificity: for example, the mRNA for cytochrome P450 (Cyp) 3A4 (GenBank
accession no. NM_017460; http://www.ncbi.nih.gov/GenBank/) is 92% identical
to the mRNA for Cyp3A7 (GenBank accession no. NM_000765), and thus a microarray
element consisting of a cDNA sequence
for Cyp3A7 would be expected to detect Cyp3A4 as well. Similarly, a microarray
element may lack specificity because it corresponds to a sequence (e.g., a
3´ untranslated region) common to several alternatively spliced transcripts,
for example, the UDP-glucuronyltransferase 1A family, where seven transcripts
(UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A7, UGT1A8, and UGT1A9) all share the
same 3´ sequences (Burchell et al. 1991). Commercial chip manufacturers
have gradually recognized some of these problems and have refined their probe
sets accordingly.
Accuracy of gene sequences in public databases. Many early
sequence entries deposited in public sequence databases were the product of
less advanced and less accurate sequencing techniques than are currently available.
Thus, when multiple sequence entries for a single gene are available, they
should be cross-checked against each other to determine the best consensus
sequence to use. The formerly common practice of relating a given clone sequence
to only one of several possible GenBank accession numbers must be avoided.
Finally, sequences must be examined for hybridization characteristics, as abnormally
high or low G + C content may skew the signal for that target relative to other
targets.
Accuracy of annotation on a microarray platform. Commercial
array manufacturers and custom array designers use GenBank, EMBL Nucleotide
Sequence Database (http://www.ebi.ac.uk/embl/index.html), or DDBJ (http://www.ddbj.nig.ac.jp/)
gene sequence accession numbers of either cDNA expressed sequence tags (ESTs)
or mRNAs, IMAGE clone or RefSeq identifiers (IDs), UniGene cluster numbers,
or proprietary/internal accessioning to identify gene features. As such, annotation
of the elements present on any given microarray can be a potential confounder
for microarray use in toxicology. This problem can arise because entries to
sequence databases often predate the standardized gene names and descriptions
found in curated resources such as LocusLink (http://www.ncbi.nih.gov/LocusLink/;
Pruitt and Maglott 2001) or may not be represented in such resources. Such
inconsistency in annotation hampers toxicogenomics in two ways. First,
it complicates mechanistic interpretation of transcript changes, as nonstandard
annotation of a sequence element (i.e., gene) may limit the effectiveness of
a literature search. Second, differences in annotation both within and between
different microarray platforms hamper the ability to compare results obtained
with their use. One approach would be to adopt an automated client server-based
system that incorporates annotation from several sources and allows those sources
to contribute equally in real time to annotation content (Dowell et al. 2001).
Another approach (described in this volume) cross-references the GenBank accession
number for a given array sequence with UniGene (http://www.ncbi.nih.gov/UniGene)
and LocusLink (Mattes 2004). This process creates a single identifier number
and annotation, making the assumption that LocusLink information
(if available) represents the best (i.e., curated) annotation, with UniGene
information as an alternative. The single identifier then allows intra- and
interplatform comparisons, with the caveat that different probe sequences annotating
to the same gene may still give different results based on the hybridization
specificity noted above.
Standardization of Data Inputs
Critical to the utility of a database is the breadth, depth, and uniformity
of the information it contains. To address the last issue, the same standard
nomenclature and numerical units must be used for different data sets so data
may be compared across experiments. To address the first two issues, guidelines
must be developed detailing what information must be included in a data set.
Minimum Information About a Microarray Experiment (MIAME) guidelines (Brazma
et al. 2001) allow sufficient and structured information to be recorded to
correctly interpret and replicate the experiments or retrieve and analyze the
data. Accordingly, guidelines for journal publication of microarray experiments
have been proposed (Ball et al. 2002), along with submission of the data to
either of the two existing public repositories: ArrayExpress (http://www.ebi.ac.uk/arrayexpress/;
Brazma et al. 2003) or Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/)
(Edgar et al. 2002). Several journals now require an accession number (indicating
that a data set has been submitted successfully to one of
these two public repositories) to be supplied at or before acceptance of publication.
Although current MIAME guidelines address the information content for a variety
of microarray experiments, a need for comparable guidelines for the toxicology
component of some microarray experiments was identified. To address the additional
information content in toxicology studies, the National Institute of Environmental
Health Sciences National Center for Toxicogenomics (NIEHS NCT; Research Triangle
Park, NC) has partnered with EMBL-European Bioinformatics Institute (EBI)
(Toxicogenomics at EBI; http://www.ebi.ac.uk/microarray/Projects/ilsi/index.html;
Hinxton, U.K.); the International Life Sciences Institute (ILSI) Health and
Environmental Sciences Institute
(HESI) Technical Committee on the Application of Genomics to Mechanism-Based
Risk Assessment; (http://hesi.ilsi.org/ and http://hesi.ilsi.org/index.cfm?pubentityid=120;
Washington, DC), and more recently, the National Center for Toxicological Research
(NCTR), Center for Toxicoinformatics, U.S. Food and
Drug Administration (U.S. FDA) (Jefferson, AR) to initiate the development
of guidelines for describing toxicogenomics experiments--MIAME/Tox [see Microarray
Gene Expression Data (MGED); http://www.mged.org/]. MIAME/Tox extends MIAME
to provide a structured annotation and framework for capturing information
associated with the toxicology component
of toxicogenomic experiments. MIAME/Tox includes some free-text fields along
with controlled vocabularies or external ontologies, specifically regarding
species taxonomy, cell types, anatomy terms, histopathology, clinical chemistry,
toxicology, and chemical compound nomenclature. An additional objective of
MIAME/Tox is to guide the development of toxicogenomics databases and data
management software.
One challenge that has arisen as part of the ongoing formulation of the MIAME/
Tox structure is the harmonization of diverse ontologies. Pathology observations,
both macro- and microscopic, are critical components of toxicological data.
To this end pathologists have developed (and are continuing to develop) controlled
vocabularies for both human clinical pathology [e.g., systemized nomenclature
of medicine (SNOMED); http://www.snomed.org] and veterinary pathology (e.g.,
Society of Toxicologic Pathology (STP)/ILSI; http://www.toxpath.org/); National
Toxicology Program Pathology Code Table (NTP PCT; http://hazel.niehs.nih.gov/user_spt/pct_terms.htm).
These efforts obviously do not include gene expression terms or genomic and
postgenomic information and may
be contrasted with those efforts in the bioinformatics community to develop
ontologies (MGED; http://www.mged.org/), Gene Ontology (GO; http://www.geneontology.org),
Human Genome Organisation/Proteomics Standards Initiative (HUGO/PSI; http://psidev.sourceforge.net/)
where the clinical annotation of the samples (anatomy, pathology, clinical
pathology) is pending. It is unlikely that a single terminology
will cover all domains, and recent effort has been placed on the semantic mapping
and the interoperability among terminologies [e.g., Standards and Ontologies
for Functional Genomics (SOFG) mouse anatomy effort; http://www.sofg.org/].
However, semantic mapping/interoperability can only be achieved among true
ontologies. Ontology is a key step to integration--a system of coding knowledge.
An ontology is a formal and declarative representation that includes the vocabulary
(or names) for referring to the terms in that subject area and the logical
statements that describe what the terms are, how they are related to each other,
and how they can or cannot be related to each other. An ontology has a definition
for each term it includes and is built in a tool that allows export in a standard
and machine-readable format. Developing pathology-controlled vocabularies as
an ontology will facilitate data exchange with databases that use a different
ontology, subject to a semantic mapping but will require close collaboration
between the pathology and bioinformatics community.
Data Quality and Comparability
The old adage "garbage in, garbage out" is constantly reiterated in the world
of database development regarding population of data and information. Toxicogenomics
databases are not immune to the pitfalls of a poorly guarded data storage system
and may contain data of subpar quality and/or insufficient or incorrect biological
information to describe or annotate experiments. Data quality metrics for microarray
data have been extensively investigated in recent years without any clear consensus
in the toxicological community as to which universal standard to adopt (Finkelstein
et al. 2002; Gollub et al. 2003; Hessner et al. 2003; Model et al. 2002; Tseng
et al. 2001). The difficulty in reaching consensus is due in part to the diversity
of existing (and pending) array platforms, data acquisition methods, and normalization
procedures. This systematic complexity makes the distillation of consensus
data quality standards a significant challenge.
Some of this complexity can be circumvented by storing pixel intensity images
(rawest form of the microarray data) from the array scanners in a data repository.
Image processing software could be archived and made available on request to
permit reanalysis using a common data acquisition method. In addition, several
microarray data analysis tools use the mean, median, or other measure of central
tendency of acquired data. As such, the unnormalized or unadjusted data could
also be captured in a database. By starting with unadjusted data, the same
background subtraction procedure and common normalization or transformation
method could be applied to the data to make assessments of gene expression
changes across different microarray platforms more comparable.
Although they do not represent consensus standards, the statistical measures
and data quality metrics associated with an individual technical platform can
be useful tools. This information may be used to compare data quality between
two samples run on the same type of technical platform. Alternatively, these
measures can be used as qualitative tools for comparison across different technical
platforms.
A major impediment to comparing gene expression data and subsequent data
quality across platforms is resolving the annotation of the gene features arrayed
on the chips. As discussed above (see "Accuracy of Annotation on a Microarray
Platform"), when comparing gene expression of features on separate arrays,
the problem is ascertaining whether the probes for the genes with equivalent
accessioning are actually derived from the same sequence region (start and
end base position for oligos or cDNA fragment in the case of ESTs) of the gene.
At worst the features with the same gene ID actually may be probing different
alternative splice variants of the same gene (Murphy 2002; Wolfinger et al.
2001). Data integrity and usability within toxicogenomic databases can thus
be improved by a) maintaining the DNA sequence of gene features on the
arrays, b) regularly updating gene annotation and description by BLAST
sequence analysis, and c) clustering similar gene sequences to reduce
or identify redundancy.
In addition to the challenges associated with microarray data, the challenge
in development of a toxicogenomics database is also to effectively capture
clinical chemistry parameters and histopathological observations in a manner
not only practical for relational or object-oriented database structuring but
also intuitive for extracting informative association rules. Customary laboratory
quality control measures and routine calibration parameters for clinical chemistry
profiles must be stored in a toxicogenomics database to effectively assess
the quality of the data when modeling gene expression data in conjunction with
clinical pathology evaluations. For instance, checking the linearity of a response
variable and collecting a control standard curve measurement are extremely
useful in assessing the quality of clinical chemistry data and will prove to
be imperative for correlating biochemical changes in biosamples with gene expression-level
alterations in cell populations.
Histopathology data result largely from discrete observations drawn from
standard nomenclatures or tables of gross and micropathology observations and
thus lend themselves well to constructing indicator (class) variables in statistical
modeling and data mining algorithms. Pathologists use these descriptors and
conventions to describe the essential components of a specific target organ
response to a toxicant. Historically, there has not been much agreement with,
or standardization of, a common system for pathologists to describe conventional
pathological interpretations. Therefore, compatibility among histopathology
observations coded by pathologists is a challenge to resolve in a toxicogenomics
database and makes the integration of the microarray, clinical chemistry, and
histopathology data domains difficult to stage. The use of a sophisticated
indexing system to reconcile differing pathological evaluations stored in the
toxicogenomics database will theoretically improve the possibility of merging
good-quality microarray data with equally precise toxicological information.
Standardization Experiments
In mid-1999 the membership of the HESI formed a project committee to develop
a collaborative scientific program to address issues, challenges, and opportunities
afforded by the emerging field of toxicogenomics. This committee, comprising
corporate members from the pharmaceutical, agrochemical, chemical, and consumer
products industries as well as advisors from academia and government, conducted
a program in which common pools of RNA were analyzed in more than 30 different
laboratories on both similar and different technical platforms. As reported
in this volume, the considerable data set generated by the HESI Genomics Committee
has been useful in increasing the understanding of sources of biological and
technical variability, the alignment of toxicant-induced transcription changes
with the accepted mechanism of action of these agents, and the challenges in
the consistent analysis and sharing of the voluminous data sets generated by
these approaches (Pennie et al. 2004). The experimental programs have shown
that patterns of gene expression relating to biological pathways are robust
enough to allow insight into mechanisms even across different platforms and
analysis sites. Thus, toxicogenomics experiments within the broad fields of
hepatotoxicity, nephrotoxicity, and genotoxicity have determined that known
mechanisms and pathways of toxicity can be associated with characteristic gene
expression profiles. This data set, including both genomic and toxicology data,
is currently being deposited in EBI's ArrayExpress (see discussion below).
In a parallel effort the NIEHS Division of Extramural Research and Training
(DERT), under the auspices of the NIEHS NCT, initiated the Toxicogenomics Research
Consortium (TRC; http://www.niehs.nih.gov/dert/trc/intro.htm) in November 2000
to serve as the extramural research arm of the NIEHS NCT. The TRC consists
of several academic institutions,
and its primary goal is to perform investigator-initiated molecular toxicology
research using current gene expression technologies. In addition to these independent
research projects, all centers participate along with the NIEHS Microarray
Group in two types of collaborative research projects, standardization experiments
and collaborative toxicology, or Science To Achieve Results (STAR), projects.
The STAR projects involve investigators from two or more centers conducting
collaborative toxicology research using gene expression profiling. Through
a series of experiments, researchers are evaluating sources of technical variation
in gene expression experiments, with an eye toward establishing standards for
evaluating competency and quality of gene expression data across multiple technology
platforms and research centers. Findings from the first standardization experiment
indicate that individual centers can identify differentially expressed genes
in standard RNA samples with moderate to high correlation across a variety
of microarray platforms. Conversely, the greatest variation is observed when
gene expression experiments are conducted across multiple centers using one
or more microarray platforms (Unpublished data). Gene expression data generated
by the TRC will support the field of toxicogenomics as a whole as well as assist
the NIEHS NCT in developing the Chemical Effects in Biological Systems (CEBS)
knowledge base described below.
Path Forward
The many informatics challenges encountered during the course of toxicogenomics
projects (as described above) are all surmountable but are made far more tractable
if the data required for and generated by the project flow seamlessly in and
out of a well-designed database. Thus, array design information such as clone
or sequence ID, if properly stored, can be verified, updated, and linked with
current annotation automatically. Similarly, such information is more readily
indexed across platforms if stored in a single database. Analysis of data from
both a single experiment and across experiments is simplified if the data already
have a structure that can be readily accessed with standard query tools and
statistical routines. Integration and correlation of microarray data with biological
data are made easier and are often possible only if both are housed within
the same database. Certainly the utility of such a database for toxicogenomics
is greatly enhanced if data from microarray and other relevant global technologies
and biological or toxicological phases of the experiment are captured electronically
and/or automatically loaded into the database.
Existing and Emerging Databases
As noted above, several public databases for microarray data have been established
(Anonymous 2002; Brazma et al. 2003; Edgar et al. 2002; Thomas et al. 2002).
The extension of efforts like these to incorporate toxicological and biological
end points critically defines and distinguishes toxicogenomic databases, and
several key initiatives will be discussed here. It should be noted that creation
of an internationally compatible informatics platform for toxicogenomics data
will enhance the impact of the individual data sets and provide the scientific
community with easy access to integrated data in a structured standard format
that will facilitate data comparison and data analysis. Coordination of database
structure development and acceptance of common guidelines will result in robust
databases valuable to many scientific communities.
One effort to build a public toxicogenomics database focuses on structuring
the existing ArrayExpress database to include toxicogenomics data and is under
way at the EMBL-EBI, in collaboration with the HESI Technical Committee on
the Application of Genomics to Mechanism-Based Risk Assessment. A parallel
effort is the CEBS knowledge base (Waters et al. 2003) under development by
the NIEHS NCT (http://www.niehs.nih.gov/nct/cebs.htm).
Both the EBI ArrayExpress and NIEHS NCT CEBS database models are based on the
international standards
developed by the MGED Society (http://www.mged.org/),
including common minimal descriptors, standard data storage and exchange format,
and harmonized nomenclature.
These similarities position both CEBS and ArrayExpress as highly collaborative
public repositories for scientists internationally. Other efforts include the
public Comparative Toxicogenomics Database (CTD; http://www.niehs.nih.gov/oc/factsheets/ctd.htm)
at the Mount Desert Island Biological Laboratory, (Mount Desert Island, Salsbury
Cove, ME), the dbZach System (http://dbzach.fst.msu.edu/)
at the Molecular and Genomic Toxicology Laboratory at Michigan State University
(East Lansing, MI) and the Toxicoinformatics Integrated System (TIS; http://www.fda.gov/nctr/science/centers/toxicoinformatics/)
at the NCTR.
European Bioinformatics Institute: ArrayExpress and Tox-MIAMExpress
The ArrayExpress infrastructure for microarray-based data (ArrayExpress;
http://www.ebi.ac.uk/arrayexpress) (Brazma et al. 2003) has been accepting
submissions since February 2002 and sees a rapidly growing volume of data deposited.
The goals of the ArrayExpress infrastructure are to a) provide the community
with easy access to high-quality data in a well-structured standard format; b)
serve as a repository for gene expression data and any biological metadata
correlated with the experiments (e.g., toxicological or pharmacological end
points) that support publications; c) allow data mining, data comparison,
and data analysis across different technology platforms, associating gene expression
patterns with the biological metadata; and d) facilitate the sharing
and reuse of array designs and experimental protocols. The meaningful exchange
of information is supported by the use of standard contextual information,
MIAME (Brazma et al. 2001) (MIAME1.1;http://www.mged.org/Workgroups/MIAME/miame_1.1.html) and MIAME/Tox (http://www.mged.org/),
and a common data exchange format, MAGE Markup Language (MAGE-ML) (Spellman
et al. 2002), developed by MGED Society (http://www.mged.org/). MAGE-ML
is an extensible markup language (XML)-based data exchange format adopted as
a standard by the Object Management Group (OMG; http://www.omg.org/technology/documents/formal/gene_expression.htm).
The ability to compare data obtained across different platforms is facilitated
by a set of procedures for updating
the array annotation and formatting the design into a standard referencing
system. A high level of data annotation is ensured by a team of curators assisting
the data producers in providing the appropriate information. An MAGE-ML pipeline
for direct data submission has been established or is under testing and construction
with a number of companies and many academic and governmental laboratories,
including Affymetrix, Agilent, Sanger Institute, Stanford University, The Institute
for Genomics Research, NIEHS NCT, NCTR, and National Environmental Research
Council-United Kingdom. Currently, others (Xybion, Rosetta Biosoftware, Silicon
Genetics, National Cancer Institute, Lund University) are adopting MAGE-ML
format, including recently developed analysis tools [J-Express (http://www.ii.uib.no/~bjarted/jexpress/),
Imagene (http://www.biodiscovery.com/imagene.asp), BioConductor (http://www.bioconductor.org/)].
New collaborations have been established to populate ArrayExpress with high-quality
reference data sets, such as human and mouse expression atlases (e.g., in collaboration
with Human Genome Mapping Project-Medical Research Council, Cambridge, U.K.),
gene expression time courses of basic biological processes in model organisms,
and expression profiles of toxic substances (HESI and NIEHS NCT). In the long
term, ArrayExpress aims to build a gene expression atlas characterizing gene
expression in different tissue and cell types by systematic added value annotation
from the team of curators. As of 17 December 2003, the database contains more
than 4,000 hybridizations (excluding the approximately 1,000 hybridizations
from the HESI genotoxicity, hepatotoxicity, and nephrotoxicity studies in curation
phase).
The ArrayExpress infrastructure consists of two data submission routes, a
core repository, an online query interface, a query-optimized data warehouse
(under development), and an online analysis tool, Expression Profiler (http://ep.ebi.ac.uk/EP/).
The first data submission route (via an ftp site) allows batch submission in
MAGE-ML format. The creation of MIAME-compliant MAGE-ML files is a demanding
but necessary exercise for high-throughput data transfer. For smaller data
sets, users can take advantage of a simpler submission process via MIAMExpress.
MIAMExpress (http://www.ebi.ac.uk/miamexpress/) is an online annotation and
submission tool presented in the form of a MIAME-based questionnaire, where
MGED Ontology is used to structure
inputs and provide controlled vocabularies for entry.
Figure 1. EBI ArrayExpress toxicogenomics infrastructure. |
As part of a collaborative undertaking with the HESI Committee on Genomics
(http://hesi.ilsi.org/ and http://hesi.ilsi.org/index.cfm?pubentityid=120),
MIAMExpress has undergone further development. These modifications allow the
incorporation of standard microarray data in
conjunction with conventional toxicological end points (e.g., clinical observations,
histopathology evaluation, and clinical pathology) (Figure 1). The new annotation
and submission tool Tox-MIAMExpress (http://www.ebi.ac.uk/tox-miamexpress/)
is tailored to accommodate input of the results from the HESI Committee on
Genomics toxicogenomics experimental program. Tox-MIAMExpress
allows three type of submission: experiment, protocol, and array design. The
experiment (a set of related hybridizations) contains the information related
to samples, descriptions, treatments, and toxicological assessments data. The
integration of toxicology and microarray end points in this database will serve
as a prototype for toxicogenomic database design and execution and will allow
for a more powerful analysis of the experimental data than is possible in the
absence of such a resource.
To ensure harmonization of the toxicological end points, allowing successful
data mining, data evaluation, and data comparison, Tox-MIAMExpress is designed
according to proposed MIAME/Tox standards. Tox-MIAMExpress uses MGED Ontology
concepts that point to established controlled vocabularies for toxicological
end points: the International Union of Pure and Applied Chemistry (http://www.iupac.org/dhtml_home.html)
for clinical pathology and the NTP PCT (http://hazel.niehs.nih.gov/user_spt/pct_terms.htm)
for clinical observations and pathological and
histopathological evaluations.
Tox-MIAMExpress is an open-source project, consisting of a perl-CGI interface,
MySQL database, and MAGE-ML export component implemented using MAGE Software
ToolKit. The system can be also installed locally and used as an electronic
notebook for toxicogenomics experiments, potentially allowing one-click submissions
to ArrayExpress or to any other toxicogenomics database or tool that accepts
MAGE-ML-formatted data. The first beta version of the Tox-MIAMExpress
was launched in January 2003, and the public online version has been accepting
submissions since September 2003.
The ArrayExpress core repository itself accepts experiments, protocols, and
array designs submissions. An accession number is assigned to each completed
and curated submission. Upon submission of array designs, a set of procedures
is provided to the users to format the array into a standard referencing system.
This format will unambiguously locate each element on the array and provide
a consistent biological annotation for data mining, data evaluation, and data
comparison across different arrays and technology platforms. Another set of
tools allows the user to access the latest gene annotation, to reannotate or
update their array, by the link provided to another EBI database, EnsMart (http://www.ensembl.org/EnsMart/).
Although the array annotation is created on the basis of the sequence information
available at the time of its release, drafts
of the genomes are continuously updated and subsequent array annotation can
be always improved and harmonized. EnsMart is built on the data in EnsEMBL,
the genome database at EBI containing consistent species-specific and interspecies
annotation (including Homo sapiens, Mus musculus, and Rattus norvegicus).
EnsMart is the recipient of the latest updated drafts of the genomes, with
cross-references between identifiers from a wide variety of the public sequence
repositories and internal EnsEMBL identifiers (LocusLink, RefSeq, Swiss-Prot,
Interpro, GO, VEGA). The output of the query to the EnsMart system can be downloaded
in various formats also for direct submission to Tox-MIAMExpress.
The ArrayExpress online query interface allows simple queries and data retrieval.
The query parameters can be either general experiment properties (e.g., accession
number, author name, condition tested) or sample properties (e.g., species
used). The query results are provided as lists of experiments, array designs,
and protocols with associated gene expression data and toxicological end points.
Users can also receive the data as a MAGE-ML download for easy export into
any MAGE-standard supportive tool (MGED; http://www.mged.org/).
The data warehouse, under development, will allow gene- and datacentric queries
where queries can be expressed in terms of gene properties (e.g., GO categories,
accession numbers) and expression value restrictions. The query results can
span multiple experiments, combining MAGE patterns with toxicological end points,
and provide cross-array platform analysis facilities. To facilitate gene-based
queries, a gene index will be established. This index will link standard gene
identifiers, where they exist, to the elements on the array. The gene index
will be developed in collaboration with other groups at the EBI and the established
model organism databases, forming the basis of database integration.
The gene expression data from ArrayExpress can also be exported into Expression
Profiler (http://ep.ebi.ac.uk/EP/), a set of online tools for the analysis
and interpretation of gene expression and other functional genomics data. It
incorporates data subselection
and transformation components as well as hierarchical and K-means clustering,
principal component analysis (PCA), between group analysis (BGA), and several
others, together with facilities for the visualization of the data and the
analysis results. The data cross-linking module augments the analysis by linking
to other tools and databases, for example, metabolic pathway databases. Expression
Profiler allows the user to benefit from the latest annotations of the genomes
via EnsMart. Third-party components and algorithms, both those installed locally
and remote web services, can also be integrated into the workflow mechanism,
enabling the platform to expand and develop with the needs of the microarray
data analysis community.
The ArrayExpress database will be also fully integrated with other relevant
databases at EBI, also as part of Integr8, a new data integration project coordinated
by the EBI and funded by the European Union as part of the TEMBLOR (The European
Molecular Biology Linked Original Resources) project (http://www.ebi.ac.uk/msd/Temblor/Temblor1.html).
It aims to provide a new integrated layer for the exploitation of genomic and
proteomic data by drawing on databases maintained at major bioinformatics
centers throughout Europe.
The ArrayExpress infrastructure for toxicogenomics provides the community
with easy access to highly curated, quality integrated data in a structured
standard format, supporting publications, guiding the harmonization process,
and facilitating data comparison and analysis.
The Chemical Effects in Biological Systems Knowledge Base
The CEBS knowledge base (http://www.niehs.nih.gov/nct/cebs.htm)
is under development (Waters et al. 2003) by the NIEHS NCT as a public toxicogenomics
information
resource combining data sets from
transcriptomics, proteomics, metabonomics, and conventional toxicology with
pathway and network information relevant to environmental toxicology and
human disease. The overall goal of CEBS is to support hypothesis-driven and
discovery research in environmental toxicology and the research needs of
risk assessment. Specific objectives are a) to compare toxicogenomic
effects of chemicals/stressors across species--yielding signatures of altered
molecular expression; b) to phenotypically anchor these changes with
conventional toxicology data--classifying biological effects as well as disease
phenotypes; and c) to delineate global changes as adaptive, pharmacologic,
or toxic outcomes--defining early biomarkers, the sequence of key events,
and mechanisms of toxicant action. CEBS is designed to meet the information
needs of systems toxicology (Waters et al. 2003), and involves study of chemical
or stressor perturbations, monitoring changes in molecular expression, and
iteratively integrating biological response data to describe the functioning
organism (Ideker et al. 2001). CEBS is a dynamic concept for integrating
large volumes of transcriptomic, proteomic, metabonomic, and toxicological
knowledge in a framework that serves as a continually changing heuristic
engine. The international data-capture guideline, MIAME, and draft MIAME/Tox
for toxicogenomics experiments, MAGE-ML data exchange format, is used to
assemble and exchange high-quality data sets with the goal of creating a
system of predictive toxicology. Toxicogenomics experiments performed using
validated NIEHS NCT and NTP methodologies will be captured in their entirety
via a unique microarray, proteomics, metabonomics object model that has been
extended from MAGE Object Model (MAGE-OM) OMG.
To phase the development of CEBS as well as test the design and implementation
of the knowledge base components and system information technology architecture,
a prototypic database system has been constructed at the NIEHS to explore the
management, integration, mining, and analysis of microarray, histopathology,
and clinical chemistry data (Figure 2). Microarray data assessed for distinct
gene expression signatures (Bushel et al. 2002) are formatted in an abridged
version of MAGE-ML and loaded into a custom-designed Oracle database table
with a version of the EBI ArrayExpress database installed. Clinical chemistry
and histopathology data on chemically exposed biological samples are obtained
from the NTP Clinical Pathology and Toxicology Database Management System (TDMS).
Data from several domains can be extracted by means of structured queries and
formatted for SAS MicroArray Solution software (SAS Institute Inc., Cary, NC),
which can be used to conduct several analyses, including a mixed linear model
gene selection method (Wolfinger et al. 2001) to identify genes that are significantly
differentially expressed after chemical exposure. Data can also be explored
using SAS JMP client software (SAS Institute Inc.) to assess the quality of
the MAGE data as well as visualize patterns of gene expression associated with
toxicity phenotypes. CEBS itself will provide "scripted analysis tool workflows" whereby
a series of statistical approaches will be linked to a detailed description
explaining in clear terms how each approach works and in what situation(s)
each should be applied. The CEBS online analysis tool suite will enable researchers
to save an analysis workflow and the corresponding parameters to CEBS. Colleagues
will then be able to apply the same statistical analysis routine by searching
for the workflow by name, specifying the data set, and submitting the request.
CEBS has leveraged the bioinformatics infrastructure and annotation engine
cancer Bioinformatics Infrastructure Objects (caBIO), developed by the National
Cancer Institute Center for Bioinformatics (NCICB, Bethdesda, MD), to provide
automated full-array annotation (Dowell et al. 2001) for CEBS. A standards-based
set of genomic components, caBIO objects, simulate genomic components--genes,
chromosomes, sequences, libraries, clones, ontologies, etc. caBIO provides
access to a variety of genomic data sources including GenBank (http://www.ncbi.nlm.nih.gov/Genbank/),
Unigene (http://www.ncbi.nlm.nih.gov/UniGene/), LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink/),
Homologene (http://www.ncbi.nlm.nih.gov/HomoloGene/), Ensembl (http://www.ensembl.org/),
GoldenPath (http://genome.ucsc.edu/cgi-bin/hgGateway?org=human), BioCarta (http://www.biocarta.com/),
dbSNP (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=snp),
and the NCICB
Cancer Genome Anatomy Project data repositories. caBIO is open
source and provides access to genomic
information using a standardized tool set. CEBS will feature automated pathway
projection of expressed genes onto BioCarta (Figure 3) and Kyoto Encyclopedia
of Genes and Genomes (KEGG; http://www.genome.ad.jp/kegg/) pathways. Pathway
visualization is linked to gene annotation, enabling point-and-click annotation
of genes on these pathways. It will be possible
to navigate the dose-time relationships within a toxicogenomics experiment
and to retrieve clinical chemistry profiles and histopathology images at will
to phenotypically anchor molecular expression profiles. Links to the supporting
literature also will be provided.
The NIEHS NCT has released the CEBS Systems Biology object model (CEBS SysBio-OM)
to capture MAGE, proteomics, and metabolomics domain information (http://cebs.niehs.nih.gov/protein/).
The model is comprehensive and leverages other open-source efforts, namely,
the MAGE-OM and the PEDRo (Proteomics Experiment Data Repository) object model.
The NIEHS NCT contractor, Science Applications International Corporation (SAIC;
San Diego, CA), in consultation with the NIEHS NCT and The Scripps Research
Institute (La Jolla, CA), has designed the CEBS SysBio-OM by extending MAGE-OM
to represent protein expression data elements (including those from PEDRo),
protein-protein interaction data, and metabonomic data. CEBS SysBio-OM
promotes the standardization of data representation as well as the standardization
of the data quality by facilitating the capture of the minimum annotation required
for an experiment so that the resulting data can be interpreted accurately.
The CEBS SysBio-OM is open source and can be implemented on varied computing
platforms.
As a multigenome knowledge base, CEBS allows characterization of the effects
of chemicals or stressors across species as a function of dose, time, and phenotype
severity. This permits classifying toxicological effects and disease phenotypes,
as well as ultimately delineating biomarkers, sequences of key molecular events
responsible for biological response, and mechanisms of action of a chemical
or stressor on a biological system. By analogy to GenBank, CEBS will support
global sequence-based query using, for example, probe sequence of differentially
expressed genes or analytically determined proteins. This will be possible
because all probe sets and analytically determined proteins represented in
the knowledge base will be sequence-aligned to gene models for all genes known
to the knowledge base. As a consequence of this design, reverse query of phenotypic
severity attributes (e.g., specific histopathology) can provide entry into
molecular expression profiles and associated sequelae. Molecular expression
profiles that match a query data set of nucleic acid or amino acid sequence
for an experimentally determined gene or protein expression profile can be
presented in rank order by quality of match for all significant matches, together
with all contextually associated (e.g., dose, time, phenotypic severity) experimental
data. In situations involving proprietary chemicals or drugs, a sequence-based
(DNA, RNA, or amino acid sequence) query can be performed without divulging
the name or chemical structure. CEBS also will support conventional simple
and complex query for compound/structure/class, toxic/pathologic effects, gene
annotation, gene groups, pathways and phenotypes, etc. Because CEBS will contain
data from multiple species of organisms, as the understanding of genetic and
biochemical pathways builds toward congruence over time, the sequence-based
system facilitates more precise definition of biological pathways and networks
as well as genetic variability and susceptibility to, for example, environmental,
chemical, or biological insult among species.
CEBS will leverage the NTP Clinical Pathology and TDMS Oracle databases in
experimental design and interpretation of phenotypes (Figure 3). In addition,
the NIEHS NCT CEBS is taking steps to extend MAGE-ML to load toxicology and
pathology data sets compatible with NTP and Xybion toxicology databases (Cedar
Knolls, NJ; http://www.xybion.com/). This will facilitate pipelining of toxicogenomics
data sets from several NTP contract laboratories that use NTP databases and
from pharmaceutical laboratories
that use the Xybion toxicology database. The NIEHS NCT CEBS is currently in
the process of mapping the NTP and Xybion toxicology databases to the MIAME/Tox
minimal toxicogenomics data guidelines. The availability of NTP toxicogenomics
data sets in CEBS will be announced in Environmental Health Perspectives.
Because CEBS will contain data on global gene expression, protein expression,
metabolite profiles, and associated chemical/stressor-induced effects in multiple
species (e.g., from yeast to humans), it will be possible to derive functional
pathway and network information based on cross-species homology and pathway
conservation. CEBS will ultimately become a knowledge base for both discovery
and hypothesis-driven research. CEBS version 1.0 (microarray) was available
for internal evaluation in August 2003 and will be available for public use
by the end of 2004. Completion of the knowledge base is scheduled for 2012.
Comparative Toxicogenomics Database
The NIEHS DERT supports an international public database devoted primarily
to comparative toxicogenomics in aquatic and mammalian species, the CTD (http://www.mdibl.org/).
The Mount Desert Island Biological Laboratory is developing CTD as a community-supported
genomic resource devoted to genes of human toxicological
significance. CTD will be the first publicly available database to a)
provide annotated associations between genes, references, and toxic agents; b)
include nucleotide and protein sequences from diverse species with a focus
on aquatic and mammalian organisms; c) offer a range of analytical tools
for customized comparative studies; and d) provide information to investigators
on available molecular reagents. This combination of features will facilitate
cross-species comparisons of toxicologically significant genes and proteins,
providing unique insights into the significance of conserved sequences and
polymorphisms, the genetic basis of variable sensitivity, molecular evolution,
and adaptation. The CTD was developed through a collaboration of five NIEHS-funded
marine and freshwater biomedical sciences centers. These centers include Mount
Desert Island Biological Laboratory, Oregon State University (Corvallis, OR),
University of Wisconsin-Milwaukee (Milwaukee, WI), University of Miami
(Miami, FL), and The Jackson Laboratory (Bar Harbor, ME). The goal of the CTD
is to develop a comparative database that links sequence information for genes
relevant to toxicology to information about gene expression, toxicology, and
biological processes. The primary focus of the CTD is on marine and aquatic
organisms as model systems for human diseases. The initial focus is also on
genes that have been identified through the NIEHS Environmental Genome Project
as important for toxicology in model systems. However, the database will eventually
merge all gene sequence information generated on all vertebrates and invertebrates,
including aquatic organisms, worms, flies, rodents, and people. The CTD provides
information about genes and annotation (gene synonyms, sets, and functions)
and links between gene sequence and toxicity data published in the scientific
literature. These aspects of the database represent an important advancement
for comparative toxicogenomics. Such information will include all the synonyms
by which a gene is known in different organisms, the toxicant responses identified
for specific genes in different species, and a platform that promotes comparisons
of gene sequences and toxicant activity among diverse organisms. This data
structure will provide comprehensive information about the mechanism of action
of toxicants. Understanding these mechanisms will allow more informed assessment
of human risk by extrapolating toxicity data from animal models to people and
will provide a mechanism by which members of the research community can share
their data and promote fruitful avenues for future toxicological research.
dbZach
The Molecular and Genomic Toxicology Laboratory, Michigan State University
(East Lansing, MI) has developed the dbZach System (http://dbzach.fst.msu.edu/),
a multifaceted toxicogenomics bioinformatics infrastructure. The goal of the
dbZach System is to provide a) facilities for the modeling of toxicogenomics
data; b) a centralized source of biological knowledge to facilitate
data mining and allow full knowledge-based understanding of the toxicological
mechanisms; and c) an environment for bioinformatic algorithmics and
analysis tools development. dbZach, designed in a modular structure to handle
multispecies array-based toxicogenomics information, is the core database implemented
in Oracle. This includes several subsystems:
- Clone Subsystem, containing information concerning the cDNA/EST
clones
- Microarray Subsystem, containing information concerning custom
array designs and the microarray data files
- Gene Function Subsystem, cataloging all the genes represented on
the arrays and their annotations
- Sample Annotation Subsystem, collecting MIAME-compliant information
about the samples and their treatments
- Toxicology Subsystem, indexing end-point toxicity measures to facilitate
correlation with gene expression analyses. The Toxicology Subsystem stores
clinical chemistry parameters and manages histopathological data, allowing
the pathologist to annotate the sample using the controlled vocabulary
from the Pathology Ontology developed by Pathbase database (http://eulep.anat.cam.ac.uk/).
dbZach also stores the actual histopathological images in the database, allowing
users to mine large numbers of images, such as a tissue microarray, with ease.
By storing the results of several toxicology assessments in the database, dbZach
facilitates comparisons of chemical mechanisms of action and supports functional
toxicogenomic and chemoinformatic investigations of structure-activity
relationships. In contrast to ArrayExpress, CEBS, and CTD, dbZach is not designed
to be a public repository for data sets contributed from diverse groups but
rather to serve as a standalone database for a laboratory or institution.
ArrayTrack
The NCTR (http://www.fda.gov/nctr/science/centers/toxicoinformatics/tools.htm)
is developing TIS to integrate genomics, proteomics, and metabonomics data
with conventional in vivo and in
vitro toxicology data (Tong et al. 2003). TIS is designed to meet the
challenge of data management, analysis, and interpretation through the integration
of toxicogenomics data, gene function, and pathways to enable hypothesis
generation. To achieve this, TIS will provide or interface with a large collection
of tools for data analysis and knowledge mining.
A first prototype of TIS has been developed. ArrayTrack is a microarray data
management and analysis system comprising three integrated components: a database
(Microarray DB) storing microarray experiment information; a library (LIB)
mirroring critical data in public databases; and a tool module (TOOL) providing
analysis capability on experimental and public data for knowledge discovery.
ArrayTrack allows users to select an analysis method from the TOOL, apply it
to the data stored in Microarray DB, and link the result to gene information
stored in the LIB module. The Microarray DB component is designed to be a rich
resource for cross-experiment and platform comparison, storing information
according to the MIAME requirements and deriving toxicity-specific signatures
from data analysis. The LIB component contains information on gene annotation,
protein function, and pathways from a diverse collection of public biological
databases (GenBank, Swiss-Prot (http://www.us.expasy.org/sprot/), LocusLink,
KEGG, GO), facilitating the annotation and the interpretation of MAGE data.
The TOOL component provides a spectrum of algorithmic
tools for microarray data visualization, quality control, normalization, pattern
discovery, and class prediction. TOOL is also designed to provide interoperability
between ArrayTrack system and other analysis software.
TIS will serve as repository for genomics, proteomics, metabonomics, and
conventional toxicology data management, supporting data mining and analysis
activities. Through cross-linking with a diverse collection of public biological
data, TIS will serve as a robust system for exploring toxicological mechanisms.
EDGE
The EDGE database (http://genome.oncology.wisc.edu/edge2/edge.php) was developed
at the McArdle Laboratory for Cancer Research, University of Wisconsin (Madison,
WI) as a resource for
toxicology-related gene expression information (Thomas et al. 2002). It is
based on experiments conducted using custom cDNA microarrays that include
unique ESTs identified as regulated under conditions of toxicity. To a large
extent the experiments were conducted in mice using a variety of agents known
to produce toxicity. The database is not designed as a public repository
of submitted data; rather, it serves as a query reference and the basis for
an algorithm predictive of toxicological potential for a chemical treatment.
The ultimate goal of the EDGE is to map transcriptional changes from chemical
exposure to predict toxicity and provide valuable insights into the basic
molecular changes responsible.
Conclusions
"Ocean in view! O! the joy." So wrote William Clark in November 1805 on sighting
the Pacific Ocean after traveling 4,142 miles in his expedition across the
western North American continent (Ambrose 1996). Yet it would be several days
before the expedition actually reached the ocean, and a year filled with many
hardships before it returned home. So it is with toxicogenomics--we can see
the goal more clearly than before, but a thorough understanding of this landscape
is yet to come. All the sources of experimental and analytical variability
are not yet known. The nature of the data demands machine handling and analysis.
The path forward must involve a rigorous meshing of biology and toxicology
with the computer science of bioinformatics and statistics. To allow the field
to develop and to allow scientists to view, assess, and mine each other's data
demands the development of public databases and standards for data storage
and transmission.
The challenges in the path forward may be seen as falling into three main
categories: information quality, information standardization, and analytical
conventionalization. Information quality challenges include not only the need
for codified, standard metrics for assessing data quality, but also encompass
the accuracy, specificity, and annotation of microarray probe elements. Ideally,
each element would have associated with it a wealth of information regarding
exactly what transcript it detects and how that transcript fits into the overall
program of a particular cell type. Furthermore, not only must this information
be easily accessible to the casual user (e.g., through a browser interface)
but it must also be in a machine-readable format suitable for data mining and
statistical analysis. Approaches to either automated or controlled data upload
can also be considered issues of information quality. Information standardization,
on the other hand, focuses on issues covered in the MIAME and MIAME/Tox efforts.
Standardization of pathology terms, which is an ambitious effort given the
spectrum of conceivable gross and microscopic observations and the diversity
of opinions as to how these observations may best be described, is critical
for toxicology. Although a few independent groups have attempted to solve this
issue, only true international harmonization of terms and ontologies, resulting
in uniform, machine-readable data, will allow the field of toxicogenomics to
realize its potential. Information standardization also encompasses the creation
of standard data sets that can be used as reference points; the studies of
the HESI Committee on the Application of Genomics to Mechanism-Based Risk Assessment
can be cited as efforts along these lines. Finally, the challenge of defining
best practice analytical approaches to toxicogenomic data may be referred to
as analytical conventionalization. Simply put, there are not enough public
examples where the same data set was analyzed by multiple approaches and the
results compared. Such comparisons would allow the field of toxicogenomics
to move forward with confidence in specific approaches. Together, the outstanding
challenges in the field of toxicogenomics provide evidence that despite its
rapid growth, toxicogenomics remains a nascent field.
Clearly the key to many of these challenges lies in creating well-structured
databases to house the results of toxicogenomic experiments. The contents of
these databases allow data quality parameters to be explored and analytical
approaches compared; in addition, the very effort of creating these databases
forces a focus in the community on information standardization. Only in such
a database can microarray annotation be kept current and at a high level of
sophistication. The collaborative efforts described in this review represent
major steps along this path and will represent an additional bonus in information
standardization and knowledge sharing. Success in these efforts will be determined
by the acceptance of and support for these databases in the toxicogenomics
community. Such acceptance will also spur input from the broad scientific community
and lead to increasingly diverse and extensive inputs to and use of these public
resources.
Toxicogenomics is an information- and informatics-intensive field. To share
experimental results successfully between labs and to address larger issues
of data handling and analysis, public databases designed to warehouse toxicogenomic
data must be developed and populated. The databases mentioned in this review
represent the promise and future of toxicogenomics.
