Nucleic Acids Research, 2008, Vol. 36, Database issuePublished online 25 October 2007
CEBS—Chemical Effects in Biological Systems:
a public data repository integrating study design and
toxicity data with microarray and proteomics data
Michael Waters1, Stanley Stasiewicz1, B. Alex Merrick1, Kenneth Tomer1,
Pierre Bushel1, Richard Paules1, Nancy Stegman1, Gerald Nehls1, Kenneth J. Yost2,
C. Harris Johnson2, Scott F. Gustafson2, Sandhya Xirasagar2, Nianqing Xiao2,
Cheng-Cheng Huang2, Paul Boyer2, Denny D. Chan2, Qinyan Pan2, Hui Gong2,
John Taylor3, Danielle Choi4,5, Asif Rashid4, Ayazaddin Ahmed6,
Reese Howle6, James Selkirk1, Raymond Tennant1and Jennifer Fostel4,*
1NIEHS, National Center for Toxicogenomics, PO Box 12233, Research Triangle Park, NC 27709,
2Science Applications International Corporation, 1710 SAIC Drive, McLean, VA 22101,3Large Scale Biology
Corporation, 3333 Vaca Valley Parkway, Vacaville, CA 95688,4Lockheed Martin Information Technologies,
PO Box 12233, Research Triangle Park, North Carolina 27709,5Research Triangle Institute, PO Box 12194,
Research Triangle Park, NC 27709 and6Alpha Gamma Technologies, Inc., 4700 Falls of Neuse Road, Suite 350,
Raleigh, NC, 27609, USA
Received June 28, 2007; Revised August 30, 2007; Accepted September 11, 2007
CEBS (Chemical Effects in Biological Systems) is an
integrated public repository for toxicogenomics
data, including the study design and timeline,
clinical chemistry and histopathology findings and
microarray and proteomics data. CEBS contains
data derived from studies of chemicals and of
genetic alterations, and is compatible with clinical
and environmental studies. CEBS is designed to
permit the user to query the data using the study
conditions, the subject responses and then, having
identified an appropriate set of subjects, to move to
the microarray module of CEBS to carry out gene
signature and pathway analysis. Scope of CEBS:
CEBS currently holds 22 studies of rats, four studies
of mice and one study of Caenorhabditis elegans.
CEBS can also accommodate data from studies of
human subjects. Toxicogenomics studies currently
in CEBS comprise over 4000 microarray hybridiza-
tions, and 75 2D gel images annotated with protein
identification performed by MALDI and MS/MS.
CEBS contains raw microarray data collected in
accordance with MIAME guidelines and provides
tools for data selection, pre-processing and analysis
resulting in annotated lists of genes of interest.
Additionally, clinical chemistry and histopathology
findings from over 1500 animals are included in
CEBS. CEBS/BID: The BID (Biomedical Investigation
Database) is another component of the CEBS
system. BID is a relational database used to load
and curate study data prior to export to CEBS, in
addition to capturing and displaying novel data
types such as PCR data, or additional fields of
interest, including those defined by the HESI
Toxicogenomics Committee (in preparation). BID
has been shared with Health Canada and the US
Environmental Protection Agency. CEBS is available
at http://cebs.niehs.nih.gov. BID can be accessed
via the user interface from https://dir-apps.niehs.
nih.gov/arc/. Requests for a copy of BID and for
depositing data into CEBS or BID are available at
CEBS (Chemical Effects in Biological Systems) is a public
repository for toxicogenomics data developed by the
National Center for Toxicogenomics (NCT) within the
National Institute of Environmental Health Science
(NIEHS). Development of CEBS began in 2002 (1) and
focused first on capture of microarray and proteomics
data. The CEBS SysBio Object Model (2), based on
MIAME (3) and MIAPE Standard (4), was used for this
*To whom correspondence should be addressed. Tel: +1 919 541 5055; Email: firstname.lastname@example.org
? 2007 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/
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portion of the development of CEBS. CEBS1 was released
in August 2003, followed by the start of development of
CEBS2. The aim of the second stage of CEBS develop-
ment was to integrate study design and toxicological assay
data with the’omics data captured in CEBS. Thus, the
CEBS SysTox Object Model (5) and the CEBS Data
Dictionary (CEBS-DD) (6) were developed to permit
accurate management of study data. CEBS2 was released
in November 2006.
As of July 2007, there are 27 toxicogenomics studies in
CEBS. A ‘study’ refers to an observational or perturba-
tional experiment carried out over a defined timeline to
understand a biological system, address a scientific
question and/or to generate hypotheses. Of the 27 studies,
22 are of rat, 4 are of mouse and 1 is of Caenorhabditis
elegans. Twenty-six of the studies have associated micro-
array data, one has proteomics data. Companies which
have published data in CEBS include Iconix Biosciences
(7,8), Pfizer Inc. (9) and Sankyo Co., Ltd (10). Other
data in CEBS have been submitted by researchers at the
National Cancer Institute (11,12), the University of
Tennessee (13–15), the University College of London
(16,17), the HESI Toxicogenomics Committee (18,19) and
the Toxicogenomics Research Consortium (submitted).
Additional data have been deposited in CEBS from in-
house studies carried out at the NIEHS (20) and at the
National Toxicology Program (NTP) (21,22).
CEBS can store data from studies of laboratory
animals, cultured cells or humans. Most studies in CEBS
contain observations or measurements made of the study
subjects and of specimens such as blood or tissue sections
derived from these subjects. The objective of CEBS is to
permit the user to integrate various data types and studies.
The CEBS user can select groups of subjects drawn from
different studies, based on subject responses or study
conditions. Once the subjects are selected, any associated
microarray data can be analyzed to produce lists of
annotated genes that can shed light on the biological and
toxicological processes occurring in the subjects.
MATERIALS AND METHODS
Scope and Utility of CEBS
CEBS is the first public repository designed to integrate
measures with’omics data. A number of other databases,
for instance the Gene Expression Omnibus (GEO) (23,24),
capture microarray data and information about the
sample treatment.The ArrayExpress
captures observations and measures taken on the study
subject concurrently with preparation of the tissue for
microarray analysis (26). A distinguishing feature of
CEBS is that the data captured from toxicogenomics
studies includes observations made of the subject through-
out the study timeline, potentially both before and after
a specimen was taken for toxicological, histopathological
or other biological analysis. Since the descriptions of the
protocols used in the study and associated analyses are
captured using controlled vocabularies rather than in free
and other biological
text form, these data are available for effective filtering
and query. These protocols, measures and temporal events
are useful in anchoring the transcriptomics or proteomics
profile displayed by the specimen within the time- and
dose-dependent biological responses seen in the study.
Thus CEBS supports phenotypic anchoring (27–31),
defined as the linking of microarray or proteomics data
with a pathophysiological phenotype.
CEBS includes both microarray and proteomics data.
Microarray data in CEBS includes 965 hybridizations to
Affymetrix arrays, 924 to Agilent arrays and 1810 to
custom format microarrays. Data can be combined within
a given microarray platform for cross-study analysis in
CEBS. Thus, data from rats exposed to any of a number
of different chemical agents can be selected using a CEBS
query tool and the microarray data can be compared to
identify differentially responsive gene products. The
proteomics data include downloadable 2D gel images,
and MALDI and MS/MS spectra used to identify peptide
spots from the gels. Intensity levels of both identified and
unidentified spots are also available in CEBS, and can be
browsed for association with time- and dose–response.
While the transcriptomics data in CEBS are captured
using MIAME guidelines, at this time there is no widely
accepted public standard for the exchange and capture of
study design and toxicity assay data. A number of efforts
are underway to create such a standard. Recently the
result of a consensus about the minimal information to
include was reported (32). In addition, a format for data
exchange is being developed by the Standard for Exchange
of Non-clinicalData (SEND)
www.cdisc.org/standards/index.html) and an ontology
for describing a biomedical investigation, which would
include a toxicology study, is under development by the
OBI (Ontology for Biomedical Investigations) Working
HomePage). CEBS will support these standards as they
Most institutions engaged in toxicology and toxicoge-
nomics studies use in-house data repositories that are
tailored to the study designs and regimens used in the
institution [c.f. dbZach (33) and EDGE (34)]. In contrast,
because it is a public resource, CEBS must be able to
manage data from a variety of sources, reflecting a wide
range of experimental organisms and study designs.
Additionally, CEBS can manage data from experimental
animals, from in vitro cells in culture, from human studies
and from experiments with model organisms such as
C. elegans. Each depositor to CEBS to date has used
a different data experimental design, reflecting different
means to care for the subjects, different treatment
regimens, varying measurements taken and so forth.
The problems associated with managing various data
streams have been addressed by the creation of BID, the
Biomedical Investigation Database, which is based on the
CEBS data dictionary (CEBS-DD). The CEBS-DD
describes the incoming data based on alignment with
public standards and proprietary data formats. At
present, data submissions to CEBS are handled by
collaboration between the depositor and the CEBS
curation staff, because, to date, each depositor has used
Nucleic Acids Research, 2008,Vol. 36,Database issueD893
a different format. Information about data deposition is
available at the CEBS Development Forum (http://
BID is built in Oracle with a Cold Fusion interface, and,
because it is used to load study data into CEBS, it contains
essentially the same content as CEBS does. In addition,
BID can easily be modified to contain additional data
fields, as requested by users. Thus, as part of the
collaboration with the HESI Toxicogenomics Committee,
BID was extended to include PCR data and to capture
additional fields describing subject handling during the
study. Additionally, the BID interface was extended to
permit query by the users of these fields. These data will be
them. At the present time BID is a data management tool,
permitting access to data and download capabilities.
The architecture of CEBS is shown in Figure 1, which
displays the relationships between CEBS components: the
CEBS SysBio and SysTox Object Models; the Oracle
databases handling study design, assay data and metadata
for microarray and proteomics data; the caBIO annota-
tion engine developed by the National Cancer Institute’s
Center for Bioinformatics (NCICB). Microarray data files
are stored as netCDF files within CEBS. Access to the
data in CEBS is via the SysTox Browser (http://
cebs.niehs.nih.gov/). CEBS was moved to the NIEHS at
the end of 2006 from its developmental location at SAIC.
Prior to implementing CEBS at NIEHS, a series of stress
tests were run to determine whether the workloads could
be supported with the infrastructure and to identify any
potential bottlenecks. This involved simulating up to 100
concurrent users in various typical functional scenarios.
The CEBS user can follow various workflows within
CEBS. These include: Show All Studies; Search by Study
Characteristics; Search by Subject Characteristics; Browse
Proteomics Data; Analyze Microarray Data Workflow;
Annotate Gene List. The CEBS user can also combine
aspects of these different workflows to customize their
exploration and use of the data in CEBS (Figure 2). Users
can combine elements of different workflows to customize
their queries and use of CEBS as diagrammed in Figure 2.
Additionally, users can download data and annotation in
different formats at various points throughout the
‘Show All Studies Workflow’ permits the user to see
a list of all studies and investigations in CEBS (Figure 3A).
An investigation refers to a self-contained scientific
enquiry, which can be composed of several studies.
A study is, as defined earlier, an observational or
perturbational experiment carried out over a defined
timeline to understand a biological system, address
a scientific question and/or to generate hypotheses. The
data type(s) associated with each study in CEBS are shown
with icons next to the title, and the user can quickly
retrieve any data associated with the study using links on
the page. The Study Timeline, Study Details and Study
Group Grid are also accessible from this page. Study
Timeline provides a graphical representation of the time-
line of the Study, showing when treatment was applied,
when observations and husbandry occurred, and other
important evensthat occurred
(Figure 3B). The Study Group Grid provides a rapid
Figure 1. Architecture of CEBS. The green box represents CEBS, and shows the relationships between CEBS components: the SysBio and SysTox
Object models, internal databases and file structure and the caBIO annotation engine and external annotation resources. User access is represented
using blue terminals.
Nucleic Acids Research, 2008, Vol. 36, Databaseissue
overview of the study subjects, and permits the user to
unambiguously identify relevant groups of biological
replicates for analysis and comparison (Figure 3C).
(i) ‘Search by Study Characteristics Workflow’ permits
the user to identify all studies with particular
characteristics, such as the duration of the study,
the species used, the stressor used (e.g. ‘show me all
studies using acetaminophen’), and study factor(s)
(e.g. studies using time course or dose–response or
genotype as study variables). Once the subset of
studies is identified, the user can jump to ‘Show all
Studies’ and browse the subset selected, examine
timelines and retrieve data.
(ii) ‘Search by Subject Characteristics Workflow’ per-
mits the CEBS user to select individual Subjects by
their strain or species, age, sex or biological
response as revealed in assay data from these
subjects. For example, the user can select all animals
with a particular grade of a given histopathology
finding, plus their comparators, and move into the
microarray analysis section of CEBS to identify the
genes that are differentially responsive between the
(iii) ‘Browse Proteomics Data’ permits the user to select
from five proteomics experiments in CEBS and
explore changes in protein profiles in response to
treatments in a time- and dose-dependent manner.
These studies include subcellular fraction analysis of
proteomes such as preparations of nuclei, enriched
cytosol and enriched microsome fractions. Actual
and reference spot lists with pI and apparent MW,
spectra from particular spots chosen for identifica-
tion, and protein identifications can all be browsed
or downloaded in commonly used formats. Images
of the 2D gels used to separate the proteins, and the
mass spectrometry spectra can be downloaded.
(iv) ‘Analyze Microarray Data Workflow’ permits the
user to list all the microarray experiments in CEBS
or to enter this Workflow with either a subset of
Studies or a list of individual hybridizations. The
user can select one or more studies from a given
Figure 2. Workflows in CEBS. The gray box represents CEBS. Search and query workflows are shown in tan, data in green and analysis and
annotation in yellow. Entry points for users are shown with blue arrows, and data download points with magenta arrows.
Nucleic Acids Research, 2008,Vol. 36,Database issue D895
microarray platform and view the details of the
study, including access to the QC data when
available. The next step in the analysis workflow
is to pre-process the data using either default
settings or the normalization and thresholding
options provided and then carry out a global
visualization to check the distribution of data
from individual arrays via box-and-whisker plots
Study Accession PI Institution Details Data
Figure 3. Screenshots of three novel CEBS displays, the Study list and disparate associated data types (A), the Study Timeline (Boxes with
X indicates an event occurred at that time point. Details of protocols are accessible via links at left) (B) and the Study Group Grid (C).
Nucleic Acids Research, 2008, Vol. 36, Databaseissue
and determine the suitability for further analysis by
viewing a multi-dimensional scaling projection and
a hierarchical clustering of all the selected hybridi-
zations. Analysis routines are developed using the R
open source statistical software and BioConductor.
Following this step, the user can select comparisons
between groups of arrays or within arrays (suited to
two color hybridizations only). The user sets the test
for significance from the menus provided, and
retrieves a list of oligonucleotide probes passing
the significance criteria with P-value if this option
was selected. The list of probes is then passed to the
Annotate Gene List option, described below.
(v) ‘Annotate Gene List Workflow’ permits the user to
add annotation to a list of genes entered or in
a comma-delimited text file. The annotation is also
applied to lists of genes generated from analysis
within CEBS. CEBS utilizes the caBIO annotation
engine from the NCICB, which has been modified
to support rat and C. elegans caBIO provides links
to annotation from BioCarta (http://www.biocarta.
com/), the Gene Ontology GO (http://www.geneon
tology.org/), Kyoto Encyclopedia of Genes and
UniProt (for proteomics data, http://www.pir.uni
and UCSC Genome (http://genome.ucsc.edu/) anno-
tation sources. For gene lists computed within
CEBS, a Fisher exact test is performed in order to
identify significantly over- and under-represented
biological categories associated with the identified
genes. Significantly expressed genes by the micro-
array analysis may be projected onto BioCarta and
KEGG pathway diagrams or the Gene Ontology
within CEBS; genes with altered levels of product
are indicated by different colors.
The architecture of BID is given in Figure 4. BID uses
a workflow design, similar to the original MIAME/
MAGE design. The BID database dependencies are set
up to have the study defined prior to the subjects, and
subjects and groups defined prior to specimens, and
specimens defined prior to deposition of any associated
data. Similarly, the microarray data storage portion of
BID has been modified from the ArrayTrack (35) schema
to model a hybridization workflow, capturing the links
from a biomaterial to RNA to labeled RNA to
hybridization to scanned data.
Figure 4. BID architecture. Each table is labeled, and contains the primary and foreign keys to allow relationships to be easily discerned. The
different modules of BID are enclosed in boxes. Study data tables are enclosed in aqua, study protocols in red, microarray workflow and data are in
dark blue, study stressors in spring green, study events in olive green, study subject characteristics in orange, study specimens in magenta. Study
tables, including publication and access rights, are not outlined.
Nucleic Acids Research, 2008,Vol. 36,Database issueD897
The majority of the work in BID has been in the area of
study design and phenotypic data (primarily clinical
chemistry and histopathology). Each subject type requires
a spectrum of characteristics and protocols specific to that
subject type. For example, if the subject is a lab animal,
then the protocols are husbandry and euthanasia, whereas
if the subject is a cell culture then the protocols are culture
and harvest. Subject characteristics collected might be
strain, sex, age, gut microflora characterization if the
subject is a lab animal, while a cell culture might be
characterized by cell cycle time, number of passages,
ploidy, etc. This database design allows the user to focus
quickly on relevant details both in data deposition and in
querying. Similarly, details of stressor characteristics
and protocols are specific for chemical, genetic and
environmental stressors. A recent publication describes
a checklist for the minimum information needed to
interpret/exchange toxicology data, and BID adheres to
this standard (32). In addition, the Ontology for
HomePage) is developing an ontology to be used to
annotate a biomedical investigation, as would be done for
automatic depositions of data into CEBS.
CEBS2 and BID are released, and can be accessed at
http://cebs.niehs.nih.gov and https://dir-apps.niehs.nih.
gov/arc/. Going forward we hope to integrate the best
features of CEBS2 and BID and concentrate on three new
areas as we develop CEBS3: facilitating data loading and
exchange, addition of novel data modules, addition of
enhanced analysis. CEBS3 will be made available to the
public after development and testing.
Integration of BID and CEBS2. The architecture of
CEBS3 will be streamlined to facilitate data entry and
retrieval, and the workflow dependencies will become
optional, so that data from observational and genetic
studies may be more easily entered.
Facilitating data loading and exchange. Currently the
biggest obstacle to increasing the content of CEBS is
the unique formats used by each depositor, which reflects
the lack of a common standard for formatting study data.
There are efforts to address this need, led by the OBI
working group and the CDISC/SEND projects. Once the
data fields and format are addressed, then a minimum
checklist for a study must be developed. Towards this, the
MIBBI (Minimal Information about Biological and
Biomedical Investigations) group has been formed,
and a recent publication describing a checklist for a
toxicology study written (32). Exchange formats for
microarray data include the SOFT format (36), MAGE-
tab (37) and MAGE-ML (38). BIO-tab is under develop-
ment at the EBI to exchange functional genomics
ne_biomap_info.pdf). We are developing a format for
study data termed SIFT (Simple Investigation Formatted
Text), and will collaborate with BIO-tab as it is developed.
Ideally, applications will be written to permit a user
to create a SIFT file for a study and associated data,
verify the format, validate the contents and then
transfer to NIEHS for automated loading into CEBS3,
and rely on current microarray data formats for associa-
Addition of new data modules. It is of interest to expand
the capability of CEBS to be searched on the basis of
chemical information, and towards this end we are
collaborating with the NTP to permit views of short-
term testing results obtained by the NTP Interagency
Center for the Evaluation of Alternative Toxicological
Methods (NICEATM), and of high-throughput screening,
and with the EPA to access a public chemical structure
viewer. Additionally, we hope to use the standards
Initiative) to house experimental metabolomics data.
Enhanced analytical features. At the moment CEBS
permits the user to identify genes with a significantly
altered transcript levels, and to combine subjects from
different studies if they were tested using the same
microarray platform. However, the user must begin each
analysis with raw data from the entire microarray. We
plan to permit additional analytical tools, for example
ANOVA and unsupervised pattern finding, and also
store normalized data values so that the user does not
need to re-analyze the array with each query. We
anticipate that this will permit integration of data across
microarray platform if the user chooses to do so.
CEBS is a public repository integrating data describing
study timeline and design, histopathological and biologi-
cal measures and’omics data. This permits the user to
anchor’omics data in the unfolding biological response
pattern captured in the study data in CEBS. Users can
access CEBS either by accessing’omics data directly, or by
way of the search and query workflows, using character-
istics of studies or subjects to select’omics data. To
illustrate the various options available we have posted
material at the CEBS Development Forum (http://
forum) and as Supplementary Data here. CEBS integrates
data from a number of contributors, making it possible to
integrate disparate data and develop comprehensive
answers to questions posed in the database.The BID
data management tool is used to house data prior to
loading into CEBS, and to expand the data management
capabilities of the CEBS/BID system by permitting the
user to deposit novel data types and attributes. The BID
user interface permits the users to access the data in BID
analogously to the searching capabilities of CEBS.
We anticipate that with publication of CEBS2 that
more data will be contributed, making it possible to
identify an ever-increasing number of gene signatures and
mechanistic pathways and networks relevant to toxicoge-
nomics. Instructions for CEBS contributors can be found
at the CEBS Development Forum (http://www.niehs.nih.-
Nucleic Acids Research, 2008, Vol. 36, Databaseissue
CEBS is available at http://cebs.niehs.nih.gov. BID
can be accessed via the user interface from https://
Supplementary Data are available at NAR Online.
McCrimmon, Sumeet Muju, Larry Schuler and Rona
Zhoufrom the Science
Corporation contract to the development of CEBS. This
research was supported by the Intramural Research
Environmental Health Sciences. Funding to pay the
Open Access publication charges for this article was
provided by NIEHS Division of Intramural Research.
Conflict of interest statement. None declared.
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