Immunome Research was a scientific journal published from 2005-2010 about cutting edge immunology
research, integrating genomics, and bioinformatics. NeuEve supports science, so we have put the
articles originally published in Immunome Research back online.
Database
Open Access
An
ontology for immune epitopes: application to the design of a broad scope
database of immune reactivities
Epitopes can be defined as the molecular structures bound by
specific receptors, which are recognized during immune responses. The
Immune Epitope Database and Analysis Resource (IEDB) project will
catalog and organize information regarding antibody and T cell epitopes
from infectious pathogens, experimental antigens and self-antigens, with
a priority on NIAID Category A-C pathogens (http://www2.niaid.nih.gov/Biodefense/bandc_priority.htm)
and emerging/re-emerging infectious diseases. Both intrinsic structural
and phylogenetic features, as well as information relating to the
interactions of the epitopes with the host's immune system will be
catalogued.
Description
To effectively represent and communicate the information
related to immune epitopes, a formal ontology was developed. The
semantics of the epitope domain and related concepts were captured as a
hierarchy of classes, which represent the general and specialized
relationships between the various concepts. A complete listing of
classes and their properties can be found at http://www.immuneepitope.org/ontology/index.html.
Conclusion
The IEDB's ontology is the first ontology specifically
designed to capture both intrinsic chemical and biochemical information
relating to immune epitopes with information relating to the interaction
of these structures with molecules derived from the host immune system.
We anticipate that the development of this type of ontology and
associated databases will facilitate rigorous description of data
related to immune epitopes, and might ultimately lead to completely new
methods for describing and modeling immune responses.
An
epitope can be defined as the molecular structure recognized by the products
of immune responses. According to this definition, epitopes are the specific
molecular entities engaged in binding to antibody molecules or specific T
cell receptors. An extended definition also includes the specific molecules
binding in the peptide binding sites of MHC receptors. We have previously
described [1]
the general design of the Immune Epitope Database and Analysis Resource
(IEDB), a broad program recently initiated by National Institute of Allergy
and Infectious Diseases (NIAID). The overall goal of the IEDB is to catalog
and organize a large body of information regarding antibody and T cell
epitopes from infectious pathogens and other sources
[2].
Priority will be placed on NIAID Category A-C pathogens
(http://www2.niaid.nih.gov/Biodefense/bandc_priority.htm)
and emerging/re-emerging infectious diseases. Epitopes of human and
non-human primates, rodents, and other species for which detailed
information is available will be included. It is envisioned that this new
effort will catalyze the development of new methods to predict and model
immune responses, will aid in the discovery and development of new vaccines
and diagnostics, and will assist in basic immunological investigations.
The
IEDB will catalog structural and phylogenetic information about epitopes,
information about their capacity to bind to specific receptors (i.e. MHC,
TCR, BCR, Antibodies), as well as the type of immune response observed
following engagement of the receptors (RFP-NIH-NIAID-DAIT-03/31:
http://www.niaid.nih.gov/contract/archive/rfp0331.pdf).
In
broad terms, the database will contain two general categories of data and
information associated with immune epitopes – intrinsic and extrinsic
(context-dependent data). Intrinsic features of an epitope are those
characteristics that can be unequivocally defined and are specified within
the epitope sequence/structure itself. Examples of intrinsic features are
the epitope's sequence, structural features, and binding interactions with
other immune system molecules. To describe an immune response associated
with a specific epitope, context information also needs to be taken into
account. Contextual information includes, for example, the species of the
host, the route and dose of immunization, the health status and genetic
makeup of the host, and the presence of adjuvants. In this respect, the IEDB
project transcends the strict boundaries of database development and reaches
into a systems biology application, attempting for the first time to
integrate structural information about epitopes with comprehensive details
describing their complex interaction with the immune system of the host, be
it an infected organism or a vaccine recipient [1, 2, 3].
For
these reasons, it was apparent at the outset of the project that it was
crucial to develop a rigorous conceptual framework to represent the
knowledge related to the epitopes. Such a framework was key to sharing
information and ideas among developers, scientists, and potential users, and
to allowing the design of an effective logical structure of the database
itself. Accordingly, we decided to develop a formal ontology. Over the
years, the term "ontology" has been defined and utilized in many ways by the
knowledge engineering community [4].
We will adopt the definition of "ontology" as "the explicit formal
specifications of the terms in a domain and the relationships among them"
[5].
According to Noy and McGuinness [6],
"ontology defines a common vocabulary for researchers who need to share
information in a domain and helps separate domain knowledge from operational
knowledge". Thus, availability of a formal ontology is relevant in designing
a database, in cataloging the information, in communicating the database
structure to researchers, developers and users, and in integrating multiple
database schema designs and applications.
Several
existing databases catalog epitope related data. We gratefully acknowledge
that we have been able use these previous experiences in the design and
implementation of the IEDB. MHCPEP [7],
SYFPEITHI [8],
FIMM [9],
HLA Ligand Database [10],
HIV Immunology Database [11],
JenPep [12],
AntiJen [13],
and MHCBN [14]
are all publicly available epitope related databases. In general, these
databases provide information relating to epitopes, but do not catalog
in-depth information relating to their interactions with the host's immune
system. It should also be noted that none of these databases has published a
formal ontology, but all of them rely on informal or implicit ontologies. We
have taken into account as much as possible these ontologies, inferring
their structure by informal communications with database developers or
perusal of the databases websites.
The
ontology developed for IEDB and described herein complements two explicit
ontologies that are presently available: the IMGT-Ontology and the Gene
Ontology (GO). The IMGT-Ontology [15]
was created for the international ImMunoGeneTics Database (IMGT), which is
an integrated database specializing in antigen receptors (immunoglobulin and
T Cell receptors) and MHC molecules of all vertebrate species. This is, to
the best of our knowledge, the first ontology in the domain of
immunogenetics and immunoinformatics. The GO project
[16]
provides structured, controlled vocabularies that cover several domains of
molecular and cellular biology. GO provides an excellent framework for
genes, gene products, and their sequences, but it does not address the
specific epitope substructure of the gene products. The IMGT provides an
excellent ontological framework for the immune receptors but lacks
information relating to the epitopes themselves. Therefore it was necessary
to expand the available ontologies and to create an ontology specifically
designed to represent the information of immune receptor interaction with
immune epitopes. Wherever possible, the IEDB ontology conforms to standard
vocabularies for capturing values for certain fields. For capturing disease
names, IEDB uses the International Classification for Diseases (ICD-10)
[17].
The NCBI Taxonomy database nomenclature [18,
19]
is used to capture species and strain names, and HLA Allele names are
consistent with the HLA nomenclature reports [20].
The
IEDB is being developed as a web-accessible database using Oracle 10g and
Enterprise Java (J2EE). Industry standard software design has been followed
and it is expected that IEDB will be available for public users by the end
of 2005.
Protégé
http://protege.stanford.edu
was used to design and document the IEDB ontology. Protégé is a free, open
source ontology editor and knowledge-base framework, written in Java. It
provides an environment for creating ontologies and the terms used in those
ontologies. Protégé supports class, slot, and instance creation, allowing
users to specify relationships between appropriate entities. Two features
that IEDB ontology effort used extensively were Protégé's support for
creating ontology terms and for viewing the term hierarchies and the
definitions. The support for a central repository on ontologies, along with
browsing support, is key in reviewing and reusing ontologies.
While
there are several open source tools available [21]
for developing ontologies, we selected Protégé because of its extensibility
to a variety of plug-ins that are readily available for integration. It also
has the ability to export to different formats including the Ontology Web
Language (OWL) (http://www.w3.org/TR/owl-features/),
which allows interoperability with other ontologies.
We
have previously described some of the general concepts relating to the IEDB
design [1,
2].
More information relating to various aspects of the project can be accessed
at http://www.immuneepitope.org/.
Herein, we report a detailed description of the novel aspects of the IEDB
ontology. In designing our application architecture, we have followed the
common system engineering practice of first determining the scope and nature
of the data involved. A first essential step is to understand the semantics
of the domain and to capture that knowledge in an agreed-upon format.
Arranging the domain concepts in a taxonomy is one of the initial organizing
steps in the ontology design process. The class hierarchy represents the
generalization and specialization relationships between the various classes
of objects in a domain [6].
Briefly, classes describe concepts in the domain. Subclasses represent
concepts (classes) that are more specific than the superclass and these
subclasses can have their own unique properties. Slots represent properties
of the classes. For example, in Figure 1,
we see that there is a class named Reference and three more specific
subclasses of Reference: Journal Article, Patent Application, and Direct
Submission. Figure 1
also shows that the class Epitope has a number of properties (slots)
associated with it such as "has Epitope Structure" and "has Epitope Source".
Figure 1
Overview of IEDB Class
Hierarchy.
The IEDB Ontology: Reference, Epitope Structure, Epitope
Source, and Assay Information classes
Our
approach for creating the class hierarchy was a top-down development
process where we defined each class in a domain and then identified its
properties before building the hierarchy. The main classes identified
for IEDB are Reference, Epitope Structure, Epitope Source, MHC Binding,
Naturally Processed Ligand, T Cell Response, and B Cell Response (Figure
1). The Epitope
class is the main class that encompasses all the individual concepts
that were identified. The individual concepts are related to other
classes. The primary relationships use the sub-class relationship or use
a property (shown in the figures by the arcs labeled "has") that has a
restriction on the type of the value that may fill that slot.
"Reference"
is the class encompassing information related to the data source from
which an epitope and its related information are extracted into the
IEDB. We have identified three broad subclasses of References that
describe where epitope information will be obtained. They are Journal
Article, Patent Application, and Direct Submission. The complete listing
of slots (fields) encompassed by the Journal Article, Patent
Application, and Submission classes are provided in Figure
2. The Journal
Article class refers to manuscripts published in peer-reviewed journals.
The Patent Application class captures all the reference fields for a
patent application that contain epitope information. The Submission
class captures information about sources that contribute data to the
IEDB directly. Data deposited by the Large Scale Antibody and T Cell
Epitope Discovery contracts [3]
and those transferred from other websites fall into this class.
Figure 2
Detailed classification of
Reference class showing its subclasses and slots.
The
Epitope Structure and Epitope Source classes capture intrinsic features
of an epitope. The Epitope Structure class captures the physical and
chemical features of an epitope. Virtually any molecular structure may
provoke an immune response, such as proteins, carbohydrates, DNA, and
lipids. In the Epitope Structure class, structural information relating
to linear sequences and 2-D structures of the epitope, if available, are
catalogued. The Epitope Source class captures the phylogenetic source of
an epitope, including species of origin, gene name, protein name, and
links to other databases for more detailed information about proteins
and genes. Figures 3A
and 3B show the
listings of properties (slots/fields) encompassing the Epitope Structure
and Epitope Source classes.
Figure 3
Detailed listing of
properties of Epitope Structure (A), Epitope Source (B),
and Assay Information (C) class.
The
experimental data and information about specific experiments and the
methodology utilized are captured in the Assay Information class. The
name of the assay used, the type of response measured in the assay, and
the readout of the assay are examples of information captured in the
Assay Information class. This important class is used as a superclass of
several other classes (and thus its properties are inherited by those
classes). A complete listing the properties (slots/fields) in the Assay
Information class is shown in Figure 3C.
Immunization, Antigen, and Antigen Presenting Cell
classes
As
with Assay Information, the classes Immunization, Antigen, and Antigen
Presenting Cell are used in multiple other class descriptions. Features
relating to the induction of the immune response are captured in the
Immunization class (Figure 4A).
It has relationships to other classes like Immunized Species, Immunogen,
In vivo Immunization, and In vitro Immunization. Immunized Species
contains information relating to the host that is being immunized. The
Immunogen class describes the molecules that induce the immune response
and an associated carrier molecule, if present. Features relating to how
the immunogen was introduced to the immunized species are captured under
the In vivo and In vitro Immunization classes.
Figure 4
High-level classification of
Immunization (A), Antigen (B), and Antigen Presenting
Cells (C) class.
Similarly,
antigens are defined as the whole molecules that react with the products
of an immune response (as opposed to the epitopes which are the specific
structures, contained within the antigen that engages the immune
receptor). Information relating to the antigen and any associated
carrier molecule is captured in the Antigen class (Figure
4B). During
immune responses, antigen-presenting cells process antigens and present
peptide epitopes complexed with MHC molecules. This information is
captured in the Antigen Presenting Cells class, which has a relationship
to the MHC Molecules and the Source Species classes (Figure
4C). The Source
Species class describes the species information from which the antigen
presenting cells are derived.
The MHC Binding, Naturally Processed Ligand, T Cell
Response, and B Cell Response classes
The
MHC Binding class captures the details relating to the interaction of
the epitope with specific MHC molecules and information relating to the
MHC molecule along with any available Epitope-MHC complex structure
details. This class also has a slot that is restricted to be an instance
of the Assay Information class (Figure 5A).
Figure 5
High-level classification of
MHC Binding (A) and Naturally Processed Ligand (B)
class.
Extrinsic
features of an epitope are captured by Naturally Processed Ligand, T
Cell Response, and B Cell Response classes. Extrinsic features are
context-dependent attributes, being dependent upon specific experimental
conditions. The Naturally Processed Ligand class captures data related
to epitopes that are naturally processed and presented on the cell
surface. This class has properties that are instances of classes
including Antigen Presenting Cell, Antigen, and Assay Information
(Figure 5B).
The
Naturally Processed Ligand class differs from the MHC Binding class in
that information related to the antigen that was processed and the cell
types in which the processing occurred is represented. MHC Binding class
captures data relating to in vitro MHC binding assays, which assess the
epitope's binding capacity to the MHC molecule. Hence the MHC Binding
class does requires neither the Antigen class not the Antigen Presenting
Cells class. In general, naturally processed ligands are assessed in the
absence of a T cell response, for example, identified by direct elution
from MHC molecules extracted from infected cells or antigen presenting
cells. Thus, the Immunization class is not used as a value restriction
by the Naturally Processed Ligand class.
The
T Cell Response class captures all of the T cell mediated
immunity-related information (Figure 6A).
It has properties that are of type: Immunization, Effector Cells,
Antigen Presenting Cell, Antigen, Assay Information, and Epitope-MHC-TCR
Complex. The Effector Cell class describes the cells that are elicited
upon immunization and that acquire measurable functions as a result. The
B Cell Response class describes antibody responses that are related to
the epitope (Figure 6B).
This class has properties that are of type: Immunization, Antibody
Molecule, Antigen, Assay Information, and Antigen-Antibody Complex.
Because B cell responses do not require MHC binding and antigen
presenting cells, the respective classes related to MHC Molecule and
Antigen Presenting Cells are not used as restrictions on properties of
the B Cell Response class.
Figure 6
High-level classification of
T Cell Response (A) and B Cell Response (B) class.
Classes capturing 3D structures
There
are three classes that capture information about the 3D structure of
complexes: Epitope-MHC Complex, Epitope-MHC-TCR Complex, and
Antigen-Antibody Complex. The Epitope-MHC Complex, Epitope-MHC-TCR
Complex, and Antigen-Antibody Complex classes are used as restrictions
on properties of the MHC Binding, T Cell Response, and B Cell Response
classes respectively (Figures 5A,
6A, and
6B). These
Complex classes capture the Protein Data Bank (PDB) Identifier, which
provides detailed information about 3D structures. The Protein Data Bank
[22,
23]
contains approximately 1600 3D structures that are of immunological
interest. Other information that is not available in PDB, such as the
atom pairs that are involved in the interactions between molecules, the
specific residues, the contact area of the molecules, and allosteric
effect, is also captured here.
IEDB Class Hierarchy and Data Dictionary
Each
class has numerous slots that capture detailed information associated
with epitopes. As mentioned above, a complete list of all the classes,
their properties, and relationship, can be found at
http://www.immuneepitope.org/ontology/index.html.
One of the files provided as supplementary material contains two
examples of how two literature references [24,
25]
containing epitope information are extracted into the IEDB ontology
(additional file 1).
Along with the class hierarchy, the IEDB's data dictionary
(additional file 2)
provides more detailed information about the fields that are defined for
the IEDB. The data dictionary contains a textual overview description
and a listing of fields that are required to be completed for IEDB
entries. The data dictionary also allows database users to provide
comments and suggestions to IEDB team to enhance the formal ontology.
The
IEDB will be a comprehensive resource pertaining to epitopes of the immune
system. By extensively curating both intrinsic and extrinsic features
associated with epitopes, the IEDB is expected to provide substantially
greater detail about specific epitopes than any other databases presently
available. The IEDB will be populated with data derived from three main
sources, namely the peer-reviewed literature, patent applications, and
direct submission. Epitope data published in the literature and patent
applications are curated manually by the IEDB's curation team. Data from
already existing epitope databases, whose authors have agreed to share their
data, will also be imported into the IEDB. Apart from these, a main data
source will be the direct submission of data from the Large-scale Antibody
and T Cell Epitope Discovery programs [3]
that are funded by NIAID. Presently, fourteen contracts have been awarded
under this program, and all of them will submit their data to the IEDB.
Direct antibody and T cell epitope submissions will also be sought from the
broader research community, with an emphasis on antibody epitopes to NIAID
Category A-C pathogens. Because of the large scale of the IEDB project, a
formal ontology is critical to ensure consistency in the representation of
data.
Communication
between database developers, researchers, analysis tool developers, and team
members is crucial, and can be performed in harmony only when a common
vocabulary is established. An ontology, which is an explicit formal
specification of the terms in the domain and relationships among them, is an
effective way to share the knowledge contained in that domain. Accordingly,
since the IEDB's domain is epitope-related data, we have created an ontology
that captures detailed conceptual structure related to these data.
The
development of this ontology has relevance for the expansion and
modification of the epitope knowledge base. Our ontology design defines
individual concepts as separate classes and then defined relationships
between these classes and other objects in the domain. These classes serve
to restrict the values that will describe properties of objects in the
database. For example, the species is a separate concept defined in its own
class. Depending on the context, this can refer to an immunized species or
the species from which antigen presenting cells are derived. Similarly MHC
Molecules is defined as a separate class, and it is used as a value
restriction by concepts like MHC Binding and Antigen Presenting Cells.
Defining concepts as separate classes and using them to restrict the values
of properties in other classes facilitates the expansion and modification of
our ontology. Adding properties (slots) to concepts is a task easily
accomplished when there are well-defined class descriptions that may serve
as value restrictions on the properties, and providing that these class
descriptions are general enough to apply in all instances. We have ensured
in our design that each concept is atomic and that it can be re-used by
various classes.
The
development of a formal ontology is valuable to database users and in
particular to scientists contributing data to, and downloading data from,
the IEDB. We anticipate that the availability of a formal ontology will
ensure that a common language and shared understanding of concepts will
inspire this type of communication, thus ensuring maximum efficiency and
accuracy. The formal ontology developed will most likely require refinement
and fine tuning when users provide suggestions and new technologies for
performing experiments are discovered. The IEDB website will provide
mechanisms for the users to provide suggestions and participate in the
enhancement of the ontology. The IEDB Data Dictionary has a separate column
for the users to provide comments on specific data fields. The IEDB website
will also host web forms that will guide users to conform to the ontology
definitions when submitting data. Apart from the web forms, an XML schema
definition (XSD) will be available on the website for users to inspect and
use in their data submission. Users will also be able to download epitope
records from the website.
In
the process of developing new ontologies, it is good practice to leverage
existing community standards. In our initial analysis, we confirmed that
there were no explicit ontologies that efficiently captured epitope details
as per the scope of the IEDB program. As mentioned above, we have utilized,
as much as possible, inferred ontologies from existing epitope databases.
Among the ontologies that we analyzed, IMGT-Ontology and Gene Ontology were
the only two formal ontologies that were related to the epitope domain. The
IMGT-Ontology was designed for the ImMunoGeneTics database. IMGT is an
integrated database specializing in antigen receptors (immunoglobulins and
T-cell receptors) and the major histocompatibility complex of all vertebrate
species. The ontology developed for this database has specific immunological
content, describing the classification and specification of terms needed for
immunogenetics. The IEDB does conform to IMGT's standards about receptors
and MHC molecule chains in the sense that all the chain names follow IMGT's
controlled vocabulary.
GO
provides structured controlled vocabularies for genes, gene products, and
sequences annotated for many organisms. The IEDB complements GO in terms of
epitopes of immunological interest since GO is incomplete in this area.
Antigens, which are primary sources of epitopes, are annotated in GO. Thus,
in essence, the IEDB could be utilized to provide an extension of GO for
antigens that contain epitope-related information.
Perhaps
the most important element in the development of the IEDB ontology is that,
to the best of our knowledge, this represents the first immunological
ontology specifically designed to capture both intrinsic biochemical and
extrinsic context dependent information. In this respect, it is similar in
spirit, but different in approach, from other knowledge resources relating
to systems biology. We anticipate that the development of this type of
ontology and associated databases might lead to completely new methods for
describing and modeling immune responses. Accordingly, this new program
might represent a novel tool to assist in the design, testing, and
development of new ways to combat infectious diseases and other immune
related pathologies such as cancer and autoimmune diseases.
This work was supported by the National
Institutes of Health Contract HHSN26620040006C. The authors like to
thank Bette Korber, Marie-Paule LeFranc, William Hildebrand, Vladimir
Brusic, Anne De Groot, Darren Flower, Pam Surko, Scott Stewart, and
Scott Way for the helpful discussions. The authors also thank Alison
Deckhut for critical review of the manuscript.
Peters B, Sidney J, Bourne P, Bui HH, Buus S, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Ponomarenko JV, Sathiamurthy M, Schoenberger SP, Stewart S, Surko P, Way S, Wilson S, Sette A: The design and implementation of the immune epitope database and analysis resource.
Immunogenetics 2005.Google Scholar
Peters B, Sidney J, Bourne P, Bui HH, Buus S, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Ponomarenko JV, Sathiamurthy M, Schoenberger S, Stewart S, Surko P, Way S, Wilson S, Sette A: The immune epitope database and analysis resource: from vision to blueprint.PLoS Biol 2005, 3:e91.View ArticlePubMedGoogle Scholar
Sette A, Fleri W, Peters B, Sathiamurthy M, Bui HH, Wilson S: A roadmap for the immunomics of category A-C pathogens.Immunity 2005, 22:155–161.View ArticlePubMedGoogle Scholar
Guarino N, Giaretta P: Ontologies and Knowledge Bases: Towards a Terminological Clarification.Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing(Edited by: Mars NJI). Amsterdam, IOS Press 1995, 25–32.Google Scholar
Gruber T: A Translational Approach to Portable Ontologies.Knowledge Acquisition 1993, 5:199–220.View ArticleGoogle Scholar
Noy NF, McGuinness DL: Ontology Development 101: A Guide to Creating Your First Ontology.
Stanford Knowledge Systems Laboratory technical report KSL-01–05 and Stanford Medical Informatics technical report SMI-2001–0880 2001.Google Scholar
Brusic V, Rudy G, Harrison LC: MHCPEP, a database of MHC-binding peptides: update 1997.Nucleic Acids Res 1998, 26:368–371.View ArticlePubMedGoogle Scholar
Schonbach C, Koh JL, Flower DR, Wong L, Brusic V: FIMM, a database of functional molecular immunology: update 2002.Nucleic Acids Res 2002, 30:226–229.View ArticlePubMedGoogle Scholar
Sathiamurthy M, Hickman HD, Cavett JW, Zahoor A, Prilliman K, Metcalf S, Fernandez Vina M, Hildebrand WH: Population of the HLA ligand database.Tissue Antigens 2003, 61:12–19.View ArticlePubMedGoogle Scholar
Korber BTM, Brander C, Haynes BF, Koup R, Moore JP, Walker BD, Watkins DI: HIV Immunology and HIV/SIV Vaccine Databases. , Los Alamos National Laboratory, Theoretical Biology and Biophysics.Los Alamos, New Mexico. LA-UR 04–8162 2003.Google Scholar
Blythe MJ, Doytchinova IA, Flower DR: JenPep: a database of quantitative functional peptide data for immunology.Bioinformatics 2002, 18:434–439.View ArticlePubMedGoogle Scholar
Bhasin M, Singh H, Raghava GP: MHCBN: a comprehensive database of MHC binding and non-binding peptides.Bioinformatics 2003, 19:665–666.View ArticlePubMedGoogle Scholar
Giudicelli V, Lefranc MP: Ontology for immunogenetics: the IMGT-ONTOLOGY.Bioinformatics 1999, 15:1047–1054.View ArticlePubMedGoogle Scholar
Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA, Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M, Balakrishnan R, Cherry JM, Christie KR, Costanzo MC, Dwight SS, Engel S, Fisk DG, Hirschman JE, Hong EL, Nash RS, Sethuraman A, Theesfeld CL, Botstein D, Dolinski K, Feierbach B, Berardini T, Mundodi S, Rhee SY, Apweiler R, Barrell D, Camon E, Dimmer E, Lee V, Chisholm R, Gaudet P, Kibbe W, Kishore R, Schwarz EM, Sternberg P, Gwinn M, Hannick L, Wortman J, Berriman M, Wood V, de la Cruz N, Tonellato P, Jaiswal P, Seigfried T, White R: The Gene Ontology (GO) database and informatics resource.Nucleic Acids Res 2004, 32:D258–61.View ArticlePubMedGoogle Scholar
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Rapp BA, Wheeler DL: GenBank.Nucleic Acids Res 2000, 28:15–18.View ArticlePubMedGoogle Scholar
Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL, Schuler GD, Tatusova TA, Rapp BA: Database resources of the National Center for Biotechnology Information.Nucleic Acids Res 2000, 28:10–14.View ArticlePubMedGoogle Scholar
Marsh SG, Albert ED, Bodmer WF, Bontrop RE, Dupont B, Erlich HA, Geraghty DE, Hansen JA, Hurley CK, Mach B, Mayr WR, Parham P, Petersdorf EW, Sasazuki T, Schreuder GM, Strominger JL, Svejgaard A, Terasaki PI, Trowsdale J: Nomenclature for Factors of the HLA System, 2004.Hum Immunol 2005, 66:571–636.View ArticlePubMedGoogle Scholar
Duineveld AJ, Stoter R, Weiden MR, Kenepa B, Benjamins VR: Wondertools? A comparative study of ontological engineering tools.International Journal of Human-Computer Studies 2000, 52:1111--1133.View ArticleGoogle Scholar
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank.Nucleic Acids Res 2000, 28:235–242.View ArticlePubMedGoogle Scholar
Berman HM, Bhat TN, Bourne PE, Feng Z, Gilliland G, Weissig H, Westbrook J: The Protein Data Bank and the challenge of structural genomics.Nat Struct Biol 2000, 7 Suppl:957–959.View ArticlePubMedGoogle Scholar
Terajima M, Cruz J, Raines G, Kilpatrick ED, Kennedy JS, Rothman AL, Ennis FA: Quantitation of CD8+ T cell responses to newly identified HLA-A*0201-restricted T cell epitopes conserved among vaccinia and variola (smallpox) viruses.J Exp Med 2003, 197:927–932.View ArticlePubMedGoogle Scholar
Lin Y, Shen X, Yang RF, Li YX, Ji YY, He YY, Shi MD, Lu W, Shi TL, Wang J, Wang HX, Jiang HL, Shen JH, Xie YH, Wang Y, Pei G, Shen BF, Wu JR, Sun B: Identification of an epitope of SARS-coronavirus nucleocapsid protein.Cell Res 2003, 13:141–145.View ArticlePubMedGoogle Scholar
This article is published under license
to BioMed Central Ltd. This is an Open Access article distributed
under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.