ArrayTrack -- Supporting Toxicogenomic Research at the FDA's National
Center for Toxicological Research (NCTR)

# 

# Weida Tong^1\*^, Xiaoxi Cao^2^, Stephen Harris^2^, Hongmei Sun^2^, Hong Fang^2^, James Fuscoe^3^, Huixiao Hong^2^, Qian Xie^2^, Roger Perkins^2^, Leming Shi^1^, Dan Casciano^4^ 

# 

# 

^1^Center for Toxicoinformatics, Division of Biometry and Risk
Assessment, National

Center for Toxicological Research (NCTR), FDA, Jefferson, AR 72079

^2^Northrop Grumman Information Technology, Jefferson, AR 72079

^3^Center for Functional Genomics, Division of Reproductive and Genetic
Toxicology,

NCTR, FDA, Jefferson, AR 72079

^4^Office of Director, NCTR, FDA, Jefferson, AR 72079

Address correspondence to Dr. Weida Tong, Center for Toxicoinformatics,
Division of Biometry and Risk Assessment, NCTR, 3900 NCTR Road, MC 910,
Jefferson, AR 72079. Telephone: (870) 543-7142. Fax: (870) 543-7662.
E-mail: <Wtong@nctr.fda.gov>

**Running Title:** ArrayTrack -- Supporting Toxicogenomic Research at
the FDA's

National Center for Toxicological Research (NCTR)

**Key words:** Database; Bioinformatics; Toxicogenomics; Systems
Toxicology;

Microarray; Genomics; Infrastructure; Data analysis; MIAME; Toxicology

**Acknowledgement:** The authors gratefully acknowledge Drs. Angela
Harris and Varsha

Desai at NCTR, who provided encouragement and insightful suggestions for
ArrayTrack.

**Abbreviations:**

NCTR --National Center for Toxicological Research

FDA -- Food and Drug Administration

TIS -- Toxicoinformatics Integrated System

MIAME -- Minimum Information About a Microarray Experiment

TRC -- Toxicogenomics Research Consortium

NCT --National Center for Toxicogenomics

CEBS -- Chemical Effects in Biological Systems

TSM -- Tivoli Storage Management

SAN --Storage Area Network

ILSI -- International Life Sciences Institute

EBL -- European Bioinformatics Institute

GO -- Gene Ontology

KEGG --Kyoto Encyclopedia of Genes and Genomes

EDKB -- Endocrine Disruptor Knowledge Base

# CPDB -- Carcinogenicity Potency Database

#  

# Outline of section headers:

-   Abstract

-   Introduction

-   Methods

-   Results

-   MicroarrayDB

-   LB

-   TOOL

-   Discussion

# Abstract

The mapping of the human genome and the determination of corresponding
gene functions, pathways, and biological mechanisms are driving the
emergence of the new research fields of toxicogenomics and systems
toxicology. Many technological advances such as microarrays are enabling
this paradigm shift that indicates an unprecedented advancement in the
methods of understanding the expression of toxicity at the molecular
level. At the National Center for Toxicological Research (NCTR) of the
US Food Drug Administration, core facilities for genomic, proteomic and
metabonomic technologies have been established that utilize standardized
experimental procedures to support center-wide toxicogenomic research.
Collectively, these facilities will continuously produce an
unprecedented volume of data. NCTR plans to develop a Toxicoinformatics
Integrated System (TIS) for the purpose of fully integrating genomic,
proteomic and metabonomic data with the data in public repositories, as
well as conventional *in vitro* and *in vivo* toxicology data.
Importantly, the TIS will enable data curation in accordance with
standard ontology, and provide or interface to a rich collection of
tools for data analysis and knowledge mining. In this paper, the design,
practical issues and functions of the TIS are discussed through
presenting its prototype version, ArrayTrack, for the management and
analysis of DNA microarray data. ArrayTrack is logically constructed of
three linked components: 1) a library (LIB) that mirrors critical data
in public databases; 2) a database (MicroarrayDB) that stores microarray
experiment information that is MIAME (Minimal Information About
Microarray Experiments) compliant; and 3) tools (TOOL) that operate on
experimental and public data for knowledge discovery. Using ArrayTrack,
we can select an analysis method from the TOOL, apply the method to
selected microarray data stored in the MicroarrayDB, and the analysis
results can be directly linked to gene information in the LIB.

**Introduction**

While modern toxicology has focused on understanding, at the molecular
level, biological mechanisms involved in the expression of toxicity, a
technological revolution has occurred enabling researchers to perform
experiments on a scale of unprecedented proportions (Marshall and
Hodgson 1998; Ramsay 1998). High-throughput experimentation is producing
large amounts of data impossible to analyze without informatics-related
support (Bellenson 1999; Spengler 2000). We see a paradigm shift in
toxicology research where hypothesis-driven research is complemented by
data-driven experimentation that is designed to be hypothesis-generating
(Afshari et al. 1999). While toxicogenomics, the study of toxicology
using high-throughput omics technologies (Aardema and MacGregor 2002;
Hamadeh et al. 2002; Nuwaysir et al. 1999; Schmidt 2002; Ulrich and
Friend 2002), and systems toxicology, the study of toxicology through
data integration (Waters et al. 2003), have advanced rapidly and are
likely to continue to advance, development of software infrastructures
to manage, analyze and integrate the diverse data have lagged behind.
Recently, Waters et al. (Waters et al. 2003) proposed a conceptual
framework of Chemical Effects in Biological Systems (CEBS) to meet the
expanding toxicogenomic research needs at the National Center for
Toxicogenomics (NCT) (Tennant 2002), including both NCT intramural
research and the research within the Toxicogenomics Research Consortium
(TRC) (Medlin 2002).

Implementing toxicogenomic technologies is a high priority initiative at
the FDA's National Center for Toxicological Research (NCTR). A
microarray core facility utilizing validated and standardized protocols
has been established. Similar facilities for proteomics and metabonomics
are at an advanced stage of development and are preparing for validation
of protocols. A Toxicoinformatics Integrated System (TIS) is
concurrently being developed to meet the data management and analysis
challenges associated with these efforts. The TIS is designed to
aggregate data from toxicogenomic research with traditional
toxicological endpoints and chemical data, along with sequence, gene
function and pathway data in public repositories. Through integration of
different data types with analysis capabilities, the TIS will enable
extraction of a tailored dataset for data interpretation and hypothesis
generation and testing.

In this paper, the prototype of TIS, ArrayTrack, is presented in the
context of meeting the following bioinformatics challenges associated
with DNA microarray experiments in toxicology:

1.  How to manage the massive information associated with a microarray
    experiment and what relevant toxicology-specific experimental
    information, or ontology, needs to be acquired for the database?

2.  What visualization and analysis capabilities are required to
    efficiently extract knowledge from the microarray data?

3.  How should the microarray experimental data be linked with data from
    public databases to make the germane information on gene annotation,
    protein function and pathways readily available for data
    interpretation?

# Methods

ArrayTrack contains three integrated components, MicroarrayDB, LIB and
TOOL. MicroarryDB and LIB are used to store in-house experimental
results and public data, respectively, whereas TOOL provides various
algorithms for data visualization and analysis. At the time of this
writing, ArrayTrack is not open source software but can be accessed
through the web (ArrayTrack 2003). Prospective users also can acquire a
free distribution of the software by conducting the authors.

Both MicroarrayDB and LIB were developed based on the ORACLE relational
database management system. The database structure of MicroarrayDB and
LIB were designed to accommodate the essential data associated with a
microarray experiment as well as the data from the public repositories
on genes, proteins and pathways (The database schema is available upon
request). The robust design allows that data entities (tables of the
identical type of data) and their relationships in the databases can be
conveniently added and modified to accommodate needs of ever evolving
microarray technology and public databases. The diverse data in
MicroarrayDB and LIB are stored in an IBM Storage Area Network (SAN),
and backed up daily using TSM (Tivoli Storage Management) system.

## User interface components providing query analysis and visualization capabilities are programmed in the JAVA language, ensuring portability to most computer operating systems, as well as enabling easy web-deployment. Interfaces have been built for several data exchange formats, including flat text files and Excel spreadsheets. The "data drilling" capabilities were developed to allow the user to lock-down and re-query the database across other data within the realm of the previous query. 

## Controlling access to experimental data is a sensitive issue for many organizations and researchers. ArrayTrack allows only the owner of the data and members of groups approved by the owner to access the data for either read or write. 

**Results**

Figure 1 depicts the ArrayTrack comprising three integrated components:
(1) MicroarrayDB that stores essential data associated with a microarray
experiment, including information on slide, samples, treatment and
experimental results; (2) TOOL that provides analysis capabilities for
data visualization, normalization, significance analysis, clustering,
and classification; and, (3) LIB that contains information (e.g, gene
annotation, protein function and pathways) from public repositories.
Through a user-friendly interface, the user can select an analysis
method from the TOOL, apply the method to selected microarray data
stored in the MicroarrayDB, and the analysis results can be directly
linked to gene information in the LIB. Additionally, ArrayTrack also
allows data to be directly linked with other public databases.

**[MicroarrayDB]{.underline}**

Microarray experimentation has become one of the fastest-growing methods
used in genomic research, and has led to a broad diversity of microarray
databases in both the public domain and commercial domains
(Gardiner-Garden and Littlejohn 2001). Unfortunately, most if not all of
them fail to accommodate the toxicology-related information needed for
toxicogenomic studies. Recently, a joint effort between International
Life Sciences Institute's Health and Environmental Sciences Institutes
(ILSI HESI), NIEHS-NCT, and EMBL European Bioinformatics Institute
(EMBL-EBI) resulted in a draft defining the required information for
toxicogenomic experiments. Augmenting the original MIAME (Minimum
Information About a Microarray Experiment) proposal (Brazma et al.
2001), the MIAME/Tox document outlines the minimum information for a
toxicogenomic experiment to ensure that the results are interpretable
and the experiment is replicable (MIAME/Tox 2003).

Our effort is to develop a validated microarray database that can be a
rich resource for cross-experiment and platform comparison to derive
toxicity-specific signatures. By validated, we mean that data is stored
if and only if it meets prescribed standards for completeness and
accuracy as well as conformance to the applicable ontology. Currently,
MicroarrayDB was designed to support toxicogenomic studies adhering to
the MIAME guidelines. Currently, a number of journals, including Nature,
the Nature group of journals, Cell, The Lancet, EMBO and Toxicology
Pathology, requires an accession number from the public microarray
databases developed based on the MIAME guidelines to be supplied at or
before acceptance of publication (2003; Ball et al. 2003). The following
two practical issues had been specifically discussed to implement the
MIAME guidelines among software developers, bioinformaticians and
toxicologists who closely work together to understand both the structure
of the database and the structure of the data to be stored in the
database:

-   MIAME vs database -- MIAME specifies the content of the information
    to be available, whereas database addresses how the content should
    be managed, and most importantly, queried. In other words, there is
    a distinction between the way the database handles all available
    information and a subset that is searchable. Technically, both
    available and searchable information can be treated in the same way.
    However, practically, such an approach usually imposes an inevitable
    burden on the end user to enter all information into the database in
    a tedious way, which might hinder their participation. Therefore, it
    is critical to define a balance point that can be accepted by both
    experimentalists and bioinformaticians.

-   Local vs global repository -- The MIAME guidelines broadly specifies
    required data with the goal of a truly global repository for public
    data deposition and data exchange that would evolve as needs
    changes. However, most databases like ArrayTrack are intended
    primarily, at least initially, for local usage within an
    institution. For local institutional usage, the extensive MIAME
    format can be simplified while still retaining the most essential
    information for toxicogenomics experiment interpretation and
    replication.

Thus, the ArrayTrack is MIAME-compliant with inclusion of additional
parameters related to toxicogenomics using controlled vocabularies.
Figure 2 gives the data submission requirements for the essential
information from both the microarray and toxicology perspectives.
Currently, MicroarrayDB contains over 650 array data. We are closely
following the current development of MAGE-ML standards (Spellman et al.
2002) that represents microarray data using markup language. We will
develop a mean using MAGE-ML to allow data in MicroarrayDB to be
communicated with other microarray data repositories, such as
ArrayExpress (Brazma et al. 2003) and GEO (Edgar et al. 2002).

**[LIB]{.underline}**

The public domain has a rich and diverse collection of biological
databases that greatly facilitates microarray experiment interpretation
and associated knowledge discovery (Baxevanis 2003). A comprehensive
summary of the major databases can be found in a special issue (volume
31, 2003) of Nucleic Acids Research. The databases we find most valuable
for microarray research are GenBank (Benson et al. 2003), SWISS-PROT
(Boeckmann et al. 2003), LocusLink (Kanehisa 2002), KEGG (Kanehisa
2002), and Gene Ontology (GO) (Ashburner et al. 2000), each of which
emphasizes a different type or aspect of biological information.
ArrayTrack's LIB is a compilation of the essential public data to
facilitate annotation and interpretation of microarray expression data.

We downloaded a number of public databases to create local mirrored
databases that are automatically updated using scripts every 2 weeks.
The LIB was the selected aggregation of the information in the mirrored
databases that are relevant for interpretation of microarray results.
Currently, the LIB comprises three sub-libraries, GeneLib, ProteinLib
and PathwayLib, that as the names suggest, concentrate public data on
genes, proteins and pathways, respectively. Each contains only the most
relevant selected information from UniGene, LocusLink, SWISS-PROT, KEGG
and GO.

The three libraries (GeneLib, ProteinLib and PathwayLib) have the same
design and functional interface. A screenshot of the GeneLib is
displayed in Figure 3. The gene information is displayed in an
Excel-like spreadsheet. Each row is associated with a gene while each
column is a particular functional annotation, such as chromosomal
location, pathways, or functional assignments (molecular function,
biological process, and cellular component) defined by GO (Ashburner et
al. 2000). The spreadsheet can be customized by including/excluding a
specific functional annotation. The common functions, such as sorting,
ranking and querying, are available for comparing across the entire gene
list. The genes can be also categorized based on their common pathways
(Figure 4). In addition, detailed information on each gene is also
available, including synonym, sequence, chromosomal map, reference, and
so forth. Information for each gene not contained in the GeneLIb is
readily available by hot link to a wide range of public data
repositories.

**[TOOL]{.underline}**

The TOOL was designed to provide a spectrum of algorithmic tools for
microarray data visualization, quality control, normalization,
significant gene identification, pattern discovery, and class
prediction.

A QA/QC tool was developed to assist quality control of slide array
results (Figure 5). The tool summarizes most relevant information into
one interface to facilitate the process of quality control. The user can
determine the quality of individual microarray results through
visualizing data, applying statistical measures and viewing experimental
annotation. Statistical measures are provided to assess the quality of a
hybridization result based on the raw expression data, including
signal-to-noise ratio, the percentage of non-hybridized spots, and etc.
The experimental annotations associated with the processes of
hybridization, RNA extraction and labeling are also available to the
end-user. Additionally, a scatter plot of Cy3 vs Cy5 together with the
original image is available for visual inspection for quality control
purposes.

Currently, two data visualization methods, ScatterPlot Viewer and
VirtualImage Viewer, are provided. The ScatterPlot Viewer plots gene
expression profiles of one sample versus another sample as shown in
Figure 6, while the VirtualImage Viewer displays expression pattern in
an array image format as shown in Figure 7. Both functions permit visual
identification of significant genes and hyperlink directly from the
graph to additional detailed library information on any particular gene.

**Discussion**

The GeneLib, ProteinLib and PathwayLib components of ArrayTrack contain
general, but essential information for functional genomics research.
These libraries also provide a basis for linking and integrating various
"omics" data. For example, the lists of genes, proteins and metabolites
derived from various omics platform could be cross-linked based on their
common IDs through these three libraries. Currently, an additional
library, ToxicantLib, is being developed for ArrayTrack that will
similarly provide linkage between toxicological data and the different
types of omics data. The ToxicantLib contains chemical name and
structure together with toxicological endpoints. Through the similarity
comparison of the chemical structure of a toxicant with the structures
of the metabolites in the PathwayLib, we might be able to examine the
toxicity effect of a particular toxicant at the molecular level. The
first toxicological data in ToxicantLib is data from our Endocrine
Disruptor Knowledge Base (EDKB) (Tong et al. 2002) and the
Carcinogenecity Potency Database (CPDB) (Gold and Zeiger 1997). Other
specific toxicology libraries will be added in the near future,
including LiverLib (gene/protein associated with liver toxicity) and
SNPsLib (containing information on single nucleotide polymorphism).

Development of commercial software for visualizing and analyzing
microarray data is currently an area of vigorous effort by
bioinformatics-oriented companies. Representative software providers for
microarray data analysis include Spotfire, Silicon Genetics,
BioDiscovery, and Partek. Similarly, a diversity of software is
available in the public domain, some of which can be accessed through a
web link at Stanford University (2003). Collectively, commercial and
public software provide many redundant capabilities, though each
software may have unique features or other attributes or familiarity
that appeal to end-users. Consequently, we have developed and will
develop more interfaces (as part of TOOL) to provide interoperability
between ArrayTrack and other analysis software. ArrayTrack includes some
tools common to other bioinformatics software, but future development of
tools will focus on development of novel analysis approaches and on
tools for toxicology-specific problems. For example, we developed a
novel class prediction method, Heterogeneous Decision Forest (Tong et
al. 2003), that could be useful for omics data analysis, generally, and
for development of predictive models, particularly.

ArrayTrack has been developed and programmed in a modular manner, and
utilizes a JAVA library such that the code is readily extensible for
other omics data, such proteomics and metabonomics, as well as
conventional toxicology data. The extended system, supporting the broad
diversity of data types is the TIS that will be under further
development and evolution for several more years. The ultimate goal is
for TIS to serve as a general, broad repository for diverse data sources
(e.g. omics, toxicology and chemical structure data), supporting broad
data mining and meta-analysis activities, as well as the development of
robust and validated predictive systems. The TIS will facilitate
scientific discovery and productivity via effective management of
diverse data and knowledge, and integration of toxicological information
at different levels of biological complexity. Through cross-linking
gene, protein, and pathway information available in public databases,
and experimental data from multiple experiments, protocols and labs,
systems toxicology will allow full understanding of toxicological
mechanism.

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**Figure legends:**

Figure 1. Overall system architecture of ArrayTrack. The software
consists of three integrated components: (1) MicroarrayDB captures
toxicogenomic data associated with a microarray experiment; (2) TOOL
provides data visualization and analysis capabilities; and (3) LIB
contains annotated information on genes, proteins and pathways.

Figure 2. Data Submission Form. The form (A) consists of two sequential
components: (1) Experiment Design and (2) Hybridization and Data. The
description of an experiment and its associated experimental protocols
are input in the "Experiment Design" section, where the owner of the
experiment can define "read and/or write" privilege (B) to share the
experiment with collaborators. The content and output of the experiment
are input in the "Hybridization and Data" section. The experiment
details on the sample preparation can be input through this section (C).

Figure 3. GeneLib. The main part of the GeneLib is an Excel-like
spreadsheet, where each row is associated with a gene while each column
presents a particular functional annotation, such as chromosomal
location, pathways, and functional assignments (molecular function,
biological process, and cellular component) defined by GO. On the top
and left side of the spreadsheet, several functions are available to
assist further exploration of the contents in the GeneLib.

Figure 4. The genes were ordered based on their common pathways (A).
Each pathway was directly linked to the detailed pathway map (B) in
KEGG, where the genes involved in the pathway (A) were highlighted as
yellow in the map (B) to indicate their roles in the pathway.

Figure 5. Quality Control interface. It summarizes most relevant
information into one interface to facilitate the process of quality
control of a microarray experiment. The tool allows investigator(s) to
make a QC/QA decision (F) for a slide using the following information:
(A) the Cy3 versus Cy5 plot; (B) the rank-order intensity distribution
of Cy3 and Cy5; (C) the slide image; (D) the summary of various
statistics; and (E) the experimental annotation.

Figure 6. ScatterPlot Viewer. The function plots gene expression
profiles of one sample versus another sample (A). The information on any
gene circled in the plot (A) will be displayed in the GeneLib (B).

Figure 7. VirtulArray Viewer. The function shows a reconstruction of the
original array image from the expression data that was derived from the
array (A). This virtual array image provides a visual representation of
data in the format of the original image. There are several functions on
the top of the image that allow browsing the contents of the array,
identifying significant spots and information about their corresponding
genes. For examples, there are two sliding controls for filtering out
unwanted spots. The upper sliding control is used to eliminate spots
whose expression fold change is less than the predefined criteria. The
other sliding control is used to eliminate spots for which the intensity
of both Cy3 and Cy5 channels falls below the selected threshold. The
resulting image (B) contains only those genes that meet both ratio and
intensity criteria. Those genes can be directly linked to the GeneLib

# Figure 1

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height="0.8708661417322835in"}![](media/image9.png){width="5.078740157480315e-2in"
height="0.8708661417322835in"}![](media/image9.png){width="5.078740157480315e-2in"
height="0.8708661417322835in"}![](media/image9.png){width="6.047244094488189in"
height="0.17795275590551182in"}![](media/image9.png){width="5.078740157480315e-2in"
height="0.8708661417322835in"}![](media/image12.png){width="1.752755905511811in"
height="1.5838582677165354in"}

Figure 6.

# ![](media/image13.png){width="10.0375in" height="3.8784722222222223in"}  {#section-4 .unnumbered}

# Figure 7

![](media/image14.png){width="10.0in" height="6.0in"}