Published online 3 October 2008Nucleic Acids Research, 2009, Vol. 37, Database issueD499–D508
TB database: an integrated platform for
T. B. K. Reddy1,*, Robert Riley2, Farrell Wymore1, Phillip Montgomery2,
Dave DeCaprio2, Reinhard Engels2, Marcel Gellesch2, Jeremy Hubble3, Dennis Jen2,
Heng Jin1, Michael Koehrsen2, Lisa Larson2, Maria Mao3, Michael Nitzberg1,
Peter Sisk2, Christian Stolte2, Brian Weiner2, Jared White2, Zachariah K. Zachariah1,
Gavin Sherlock3, James E. Galagan2,4,5, Catherine A. Ball1and Gary K. Schoolnik6
1Department of Biochemistry, Stanford University, CA 94305,2Broad Institute of MIT and Harvard, Cambridge,
MA 02142,3Department of Genetics, Stanford University, CA 94305,4Department of Biomedical Engineering,
Boston University, Boston, MA 02215,5National Emerging Infectious Diseases Lab, Boston University, Boston
MA 02118 and6Department of Microbiology & Immunology, Stanford University, CA 94305, USA
Received August 14, 2008; Revised September 17, 2008; Accepted September 18, 2008
The effective control of tuberculosis (TB) has been
thwarted by the need for prolonged, complex and
potentially toxic drug regimens, by reliance on an
inefficient vaccine and by the absence of biomar-
kers of clinical status. The promise of the genomics
era for TB control is substantial, but has been hin-
dered by the lack of a central repository that col-
lects and integrates genomic and experimental
data about this organism in a way that can be readily
accessed and analyzed. The Tuberculosis Database
(TBDB) is an integrated database providing access
to TB genomic data and resources, relevant to the
discovery and development of TB drugs, vaccines
and biomarkers. The current release of TBDB
houses genome sequence data and annotations
for 28 different Mycobacterium tuberculosis strains
and related bacteria. TBDB stores pre- and post-
publication gene-expression data from M. tubercu-
losis and its close relatives. TBDB currently hosts
data for nearly 1500 public tuberculosis microarrays
and 260 arrays for Streptomyces. In addition, TBDB
provides access to a suite of comparative genomics
and microarray analysis software. By bringing
together M. tuberculosis genome annotation and
gene-expression data with a suite of analysis
unique discovery platform for TB research.
In humans, tuberculosis (TB) is caused by the bacterium
Mycobacterium tuberculosis and primarily targets the
lungs (as pulmonary TB), but can also affect other
organs, including the brain and meninges, lymph nodes,
bone and joints, the genitourinary system and the intestine
and liver. TB is today the second highest cause of death
from infectious diseases after HIV/AIDS (1) and is the
biggest killer of people infected with HIV (2). The
World Health Organization’s most recent global data
(from 2005) show that every year 8 million people
become ill with tuberculosis and 2 million people die of
the disease. A third of the world’s population has been
exposed to TB, making this disease one of the greatest
global health challenges facing us today (3). A remarkable
feature of TB is its ability to enter an asymptomatic latent
phase lasting years or even decades. Activation of a latent
infection can be precipitated by changes in the physiolog-
ical and immune status of the host owing to declining cell-
mediated immunity associated with senescence, malnutri-
tion and diabetes or the occurrence of other diseases, espe-
cially HIV/AIDS (4). Chemotherapy for active TB due to
drug-sensitive strains entails the use of multiple antibiotics
administered for 6 months. This complicated and fre-
quently toxic treatment regimen often results in poor
patient compliance. This in turn has led to the emergence
of antibiotic resistant strains that require longer treatment
courses, the use of less effective and more toxic drugs and
higher failure rates (5). As a result, TB remains a wide-
spread and deadly disease whose control will require more
*To whom correspondence should be addressed. Tel: 650 736 0075; Fax: 650 724 3701; Email: email@example.com
? 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
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effective public health measures and the development
of new drugs and vaccines. Recent developments in geno-
mics and the availability of the complete M. tuberculosis
genome sequence (6) has led to the use of genome-wide
expression profiling and comparative genomics methods
to better understand M. tuberculosis pathology, latency,
emerging drug resistance and evolution. However, despite
the wide-spread use of functional and comparative geno-
mics to study M. tuberculosis, there has been no single
repository for these large-scale datasets, complete with
high-quality experimental annotation, and connected to
up-to-date gene annotation and comparative genomic
information. Instead, much of these data have been
located in disparate sites like GenoMycDB: a database
for comparative analysis of mycobacterial genes and
genomes (7) and MGDD: M. tuberculosis genome diver-
gence database (8) that employ diverse and often incom-
patible formats and analytical tools. The Tuberculosis
Database (TBDB) was developed to address this gap.
TBDB uses software from the Stanford Microarray
Database (SMD) (9) and the Broad Institute’s Calhoun
system (10,11), and houses gene-expression data paired
with genome sequence and annotation data. Uniting
experimental data with genome sequence data enables
researchers to ask complex questions and draw inferences
that would otherwise be impossible by looking at individ-
ual small datasets. In this context, TBDB brings together
powerful genomics tools to advance M. tuberculosis
research in ways that will contribute to the identification
of new drug targets, vaccine antigens, diagnostics and host
TBDB is an integrated database that houses both anno-
tated genome sequence data and microarray and RT–PCR
expression data from in vitro experiments and TB-infected
tissues. TBDB houses genome sequence data for several
M. tuberculosis strains as well as data for numerous
related species. These data and annotations include pub-
licly available sequences from a number of sequencing
centers and groups, including sequences being produced
by the Broad Institute’s Microbial Sequencing Center.
The microarray data within TBDB are predominantly
from M. tuberculosis, but we are in the process of incor-
porating in vivo data from infected host tissues (principally
human, primate and murine) into TBDB. Experimental
data may be deposited into TBDB by any TB researcher
prior to publication providing prepublication access to
tools for the analysis, annotation, visualization and shar-
ing of data. The data are then made public at the author’s
request or following publication, whichever is first. In
addition, TBDB curators search the literature for publica-
tions containing relevant TB or host microarray data. The
primary data are then requested from the authors of such
publications and are entered into TBDB, where the experi-
ments are annotated and made public so other researchers
can reanalyze the data (often in conjunction with other
datasets within TBDB) using TBDB tools. Table 1 lists
TBDB statistics, including the number of annotated
genomes in TBDB, microarray experiments, publications
and other data types.
The first route of entry into TBDB is the Quick Search
gene name, gene sequence name, author name, title or any
other keyword. The result page of a Quick Search provides
a count of genes, microarray experiments, operons, gene
families and other database objects that match the query.
Links from this results page provide direct access to pages
with detailed information about particular objects, such as
the Gene Detail and Publication pages. Quick Search is
available at the top of every TBDB page, and thus provides
an easily accessible single integrated access point to all
genome annotation and expression data in TBDB.
TBDB currently houses genome sequence data for
M. tuberculosis strain H37Rv (a standard prototype
strain long used for experimental and animal infection
studies), as well as other M. tuberculosis strains and bac-
teria from related taxa, focusing on members of the
Actinomycetes family of high G+C content, Gram-posi-
tive organisms of which M. tuberculosis is a member.
These genomes sequences have been annotated with a vari-
ety of genomic features including genes, operons, sequence
similarity to GenBank sequences using BLAST (12), trans-
fer RNAs using tRNAScan (13), protein domains and
families using PFAM (14) and noncoding RNAs based
on RFAM (15). Known immune epitopes have also been
mapped through collaboration with BioHealthBase (16).
A suite of analytical tools is also provided to allow com-
parative genomic analysis of M. tuberculosis. Table 2 lists
the genomes in TBDB for which sequence data are avail-
able along with their size and the number of annotated
genes. Access to the annotated genome sequences and
comparative data is provided through several search inter-
faces, some of which are described subsequently.
Feature detail pages
All information about annotated features on any genome
sequence is available through Feature Detail pages, of
which the Gene Detail page is the most common example
(Figure 1). Information presented in the Gene Detail page
is organized into different sections. These include, Gene
Info, Gene Expression, Functional Annotation, Transcript
Info, Sequence and genome display options. The Gene
Info section provides complete details about Locus
Name, Gene Symbol, Synonyms, Gene Name, Gene
1. SummaryofTBDBdata content(asof
TBDB data statistics
Number of genomes
Number of all microarrays
Number of public microarrays
Number of publications
Number of experiment sets
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
Product Names, Gene Family, Location, Protein Domains,
External Links to related databases including TubercuList
(17), TB Structural Genomics Consortium (TBSGC)
Protein Structure Information (18) and the Proteome
2D-PAGE Database. Figure 1 shows the gene detail
page for dosR (devR, Rv3133c), which encodes the
response regulator of a two-component signal transduc-
tion system that tightly controls a well-studied M. tuber-
culosis regulon that is activated by oxygen limitation or
exposure to nitric oxide (19).
Genome visualization andcomparative analysis
Researchers can retrieve DNA or protein sequence for
segments of any of the genome sequences in TBDB from
many locations within the site, including the Browse
Regions search tool. The sequences can then be visualized
using a number of different tools. The Argo Genome
Browser (an interactive applet) and the Feature Map
(a lighter weight version of the Argo Genome Browser)
provide linear views of genome sequences along with all
associated annotated features. Argo in particular provides
a dynamic interface to visualizing genome data that allows
users to zoom from whole chromosomes to individual
nucleotides, navigate within sequences, and select individ-
ual features to retrieve additional information. A Circular
Genome Viewer provides a circular plot of genome
sequences along with a plot of the density of particular
features, GC content and GC skew. Finally, the Genome
Map tool provides a dynamic linear view of one or more
genome sequences and associated annotations, and
displays conserved synteny between the displayed gen-
omes for regions selected by the user (Figure 2).
An additional number of tools are also provided speci-
sequences, including the Synteny Map, Dot Plot, Operon
Browser (Figure 3) and Gene Family Search. The Synteny
Map uses precomputed genome alignments to graphically
display regions of genomic similarity between a single
reference genome and one or more other genomes—in
effect providing the results of an in silico genome hybridi-
zation between sets of genomes. Using this tool, the user
can select regions of interest and then click a region to
zoom in and view genes, genome sequence, and features.
The Dot Plot displays a navigable map of computed syn-
teny between genomes in the form of dot-plot lines. When
comparing multiple genomes, the color of the plotted syn-
teny indicates which genome is aligned to the reference at
that position. The Operon Browser is a tool that simulta-
neously displays the expression correlation between genes
in a genomic region of the M. tuberculosis H37Rv strain
while showing syntenic gene order of orthologs in related
species. A heatmap derived from expression correlation
data is provided along with an alignment of syntenic
areas. Mousing over the genes provides additional infor-
mation such as locus ID, gene symbol and description.
Color coding of genes indicate orthologous relationships
across different species. Finally, the Gene Family Search
displays phylogenetic trees and sequence alignments of
predicted orthologous gene families within the genome
sequences in TBDB. The basic search feature lets the user
choose the number of genomes to query and whether to
limit the search to strict orthologs or not. In addition, an
advanced search option chooses which genomes to include
TBDB GENE EXPRESSION DATA
TBDB houses public and prepublication microarray and
RT–PCR expression data. Public data are freely accessible
and can be downloaded or reanalyzed using TBDB anal-
ysis tools. Access to prepublication data is restricted to the
researchers who generated the data until they publish or
decide to make their data public. TBDB users can estab-
lish a free user account to enter microarray data, share
prepublication microarray or RT–PCR data with collea-
gues or store datasets for analysis in a data repository.
Data in the repository can be shared with other research-
ers at the discretion of the TBDB user.
Expression data in TBDB can be accessed by searching
for data from individual microarrays or RT–PCR assays
or by searching for data from a publication. For a novice
user, the publication search is an easy place to start
exploring expression data in TBDB. The expression
Basic Search is an interactive search option that queries
TBDB via publication, organism or dataset. The expres-
sion Advanced Search finds microarray data by experimen-
ter, category, subcategory and organism. The Gene Search
for Expression searches for genes or reporter sequences
used on microarrays. Reporter sequences correspond
to a piece of DNA deposited on a microarray slide.
Table 2. List of annotated genomes in TBDB
Organism Size (mb)Genes
M. tuberculosis H37Rv
M. tuberculosis CDC1551
M. tb. F11 (finished)
M. tb. C
M. tb. Haarlem
M. bovis AF2122/97
M. bovis BCG
M. leprae TN
M. avium 104
M. avium k10
M. smegmatis MC2 155
M. ulcerans Agy99
M. vanbaalenii PYR-1
M. sp. KMS
M. sp. MCS
Rhodococcus sp. RHA1
Nocardia farcinica IFM 10152
Corynebacterium glutamicum ATCC 13032
C. diphtheriae NCTC 13129
C. efficiens YS-314
C. jeikeium K411
Streptomyces avermitilis MA-4680
S. coelicolor A3(2)
Propionibacterium acnes KPA171202
Acidothermus cellulolyticus 11B
Bifidobacterium longum NCC2705
Nucleic Acids Research, 2009,Vol. 37,Database issueD501
Figure 1. TBDB Gene Detail page. The Gene Detail page provides at-a-glance information for a given gene, including known names and synonyms,
predictedfunction(s) and protein domains. It alsoserves as a jumping off point to varioussequence tools,and to expression datafor that gene. In addition,
it provides several links to external resources such as TubercuList, TBSGC Protein Structure Information, Proteome 2D-PAGE Database at Max Planck
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
Figure 2. Genome Map tool. This tool provides a linear view of one or more genome sequences and associated annotations as well as conserved
synteny between genomes. Annotations are provided as tracks above (forward strand) and below (reverse strand) the midline. When zoomed out,
annotations are viewed as density plots; when zoomed in individual features are displayed. Users may select regions of a genome sequence by
dragging along the midline. Syntenic regions in the other sequences associated with the selection are then displayed as red bands.
Nucleic Acids Research, 2009,Vol. 37,Database issue D503
Figure 3. Comparative genome analysis. The Genomes Synteny Map (A), Dot Plot (B) and Operon Map Browser (C) provide different ways to access
comparative genomic data between M. tuberculosis reference genome and selected related species. These tools provide an interactive means to explore
comparative genomic data.
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
This search returns all microarray spots associated with a
reporter sequence or gene, and the search results link to
the Spot History page that lets users explore all associated
Using Expression Connection, researchers can visualize
and explore clustered microarray datasets from publica-
tions whose data are present within TBDB. Clustering
organizes expression data for genes or reporter sequences
into groupsthathave similar
This enables a user to directly view and explore already
clustered data within TBDB without needing to go
Figure 4, a publication detail page can be accessed by
Publications ! ‘Data in TBDB’. Interactive clustered
data images for a publication can be navigated using
GeneXplorer (20), which provides views of the most cor-
related genes for a gene of interest or searches for genes
using text queries (Figure 4). Thus, this option enables a
user to explore and interrogate TBDB for expression data
TBDB provides a suite of microarray data analysis tools
for its users. All tools are freely available to analyze both
public and prepublication data in TBDB. A typical data
analysis process at TBDB involves several steps in the
following order: Experiment Selection ! Gene Selection
and Annotation ! Data Filtering Options ! Data
Retrieval ! Gene Filtering ! Clustering and Image
Generation. At each step, a user is presented with various
options that allow them to filter and cluster the data
according to their needs. For example, a user can
employ either the Basic or Advanced Expression Search
to choose a set of microarray data for further analysis.
Clicking on the ‘Data Retrieval and Analysis’ option
invokes the data analysis pipeline, where a user can
select various microarray data filtering and transforma-
tion options. Many microarray data analysis tools can
be applied to datasets, including hierarchical clustering,
imputation of missing values, Gene Set Enrichment
Analysis (21), Singular Value Decomposition (22) and
described previously (9)] have been made available
through TBDB. At each step in the data analysis, pipeline
a link to a relevant ‘Help’ page is provided, which explains
in detail the various available options. In addition, the
TBDB data repository provides access to the suite of
gene-expression analysis tools provided through the
Gene Pattern software (23).
Curating microarray expression data from publications is
an important part of TBDB’s efforts. We actively search
PubMed for relevant publications containing microarray
experiments, then obtain the raw data from researchers
and load them into TBDB, with detailed experimental
Figure 3. Continued.
Nucleic Acids Research, 2009,Vol. 37,Database issue D505
Figure 4. Publication microarray data and expression connection. Researchers can access the full raw microarray data associated with a publication,
either for download, or retrieval through the data retrieval and analysis pipeline. In addition, users can explore clustered microarrays data, whereby
they can search for particular genes, or identify which genes show coexpression across a particular dataset.
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
We are working to increase the quality and quantity of
data within TBDB and to incorporate additional data
types. One of our priorities is to acquire host expression
data from M. tuberculosis-infected tissues (mouse, primate
and human), and we also plan to expand TBDB’s capacity
to house and analyze RT–PCR data and will develop tools
for comparative analysis of RT–PCR and microarray
expression data. We will also implement tools such as
GO::TermFinder (24), which allows users to determine
whether there are biological themes associated with a list
of genes of interest, and tools for the analysis of replicate
microarray experiments. We are also working to improve
the depth and quality of our genome annotations. We are
currently curating TB literature and associating these data
with genes and other genomics features. Moreover, we
have implemented and will deploy a community annota-
tion infrastructure to allow TB researchers to submit addi-
tions and improvements to existing annotations through
the TBDB website. We are also using the comparative
sequence integrated into TBDB to improve on the accu-
racy of structural gene annotations and to predict addi-
tional potential noncoding genes. Finally, as new TB
Sequencing Center, they will be deposited and made pub-
licly available in TBDB. Ultimately, we hope that TBDB
will serve as a community hub for TB research; a TB
research community information page will be implemen-
ted with a listing of TB research labs and colleagues; this
will also provide a forum for the community of users
including feedback and suggestions from the community
that will help us better serve them.
TBDB contains annotated genome and expression (micro-
array and RT–PCR) data and a suite of data analysis
tools designed to serve as a unique resource for TB
research and for the discovery of new drugs, vaccines
and biomarkers. Data within the TBDB and all analysis
tools are freely available to researchers. Only prepublica-
tion gene-expression data require a password.
We are grateful to the research community for their valu-
able input and suggestions in building and maintaining
The Bill and Melinda Gates Foundation. Funding for open
access charge: The Bill and Melinda Gates Foundation.
Conflict of interest statement. None declared.
1. Arentz,M. and Hawn,T.R. (2007) Tuberculosis infection: insight
from immunogenomics. Drug Discov. Today, 4, 231–236.
2. Corbett,E.L., Watt,C.J., Walker,N., Maher,D., Williams,B.G.,
Raviglione,M.C. and Dye,C. (2003) The growing burden of
tuberculosis: global trends and interactions with the HIV epidemic.
Arch. Intern. Med., 163, 1009–1021.
3. Young,D.B., Perkins,M.D., Duncan,K. and Barry,C.E. III. (2008)
Confronting the scientific obstacles to global control of tuberculosis.
J. Clin. Invest., 118, 1255–1265.
4. Flynn,J.L. and Chan,J. (2001) Tuberculosis: latency and
reactivation. Infect Immun., 69, 4195–4201.
5. Gandhi,N.R., Moll,A., Sturm,A.W., Pawinski,R., Govender,T.,
Lalloo,U., Zeller,K., Andrews,J. and Friedland,G. (2006)
Extensively drug-resistant tuberculosis as a cause of death in
patients co-infected with tuberculosis and HIV in a rural area of
South Africa. Lancet, 368, 1575–1580.
6. Cole,S.T., Brosch,R., Parkhill,J., Garnier,T., Churcher,C.,
Harris,D., Gordon,S.V., Eiglmeier,K., Gas,S., Barry,C.E. III et al.
(1998) Deciphering the biology of Mycobacterium tuberculosis from
the complete genome sequence. Nature, 393, 537–544.
7. Catanho,M., Mascarenhas,D., Degrave,W. and Miranda,A.B.
(2006) GenoMycDB: a database for comparative analysis of
mycobacterial genes and genomes. Genet. Mol. Res., 5, 115–126.
8. Vishnoi,A., Srivastava,A., Roy,R. and Bhattacharya,A. (2008)
MGDD: Mycobacterium tuberculosis genome divergence database.
BMC Genomics, 9, 373–376.
9. Demeter,J., Beauheim,C., Gollub,J., Hernandez-Boussard,T.,
Jin,H., Maier,D., Matese,J.C., Nitzberg,M., Wymore,F.,
Zachariah,Z.K. et al. (2007) The Stanford Microarray Database:
implementation of new analysis tools and open source release of
software. Nucleic Acids Res., 35, D766–D770.
10. Galagan,J.E., Calvo,S.E., Borkovich,K.A., Selker,E.U., Read,N.D.,
Jaffe,D., FitzHugh,W., Ma,L.J., Smirnov,S., Purcell,S. et al. (2003)
The genome sequence of the filamentous fungus Neurospora crassa.
Nature, 422, 859–868.
11. Galagan,J.E., Nusbaum,C., Roy,A., Endrizzi,M.G., Macdonald,P.,
FitzHugh,W., Calvo,S., Engels,R., Smirnov,S., Atnoor,D. et al.
(2002) The genome of M. acetivorans reveals extensive metabolic
and physiological diversity. Genome Res., 12, 532–542.
12. Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J.
(1990) Basic local alignment search tool. J. Mol. Biol., 215, 403–410.
13. Lowe,T.M. and Eddy,S.R. (1997) tRNAscan-SE: a program for
improved detection of transfer RNA genes in genomic sequence.
Nucleic Acids Res., 25, 955–964.
14. Finn,R.D., Mistry,J., Schuster-Bockler,B., Griffiths-Jones,S.,
Hollich,V., Lassmann,T., Moxon,S., Marshall,M., Khanna,A.,
Durbin,R. et al. (2006) Pfam: clans, web tools and services.
Nucleic Acids Res., 34, D247–D251.
15. Griffiths-Jones,S., Moxon,S., Marshall,M., Khanna,A., Eddy,S.R.
and Bateman,A. (2005) Rfam: annotating non-coding RNAs in
complete genomes. Nucleic Acids Res., 33, D121–D124.
16. Squires,B., Macken,C., Garcia-Sastre,A., Godbole,S., Noronha,J.,
Hunt,V., Chang,R., Larsen,C.N., Klem,E., Biersack,K. et al. (2008)
BioHealthBase: informatics support in the elucidation of influenza
virus host pathogen interactions and virulence. Nucleic Acids Res.,
17. Cole,S.T. (1999) Learning from the genome sequence of
Mycobacterium tuberculosis H37Rv. FEBS Lett., 452, 7–10.
18. Terwilliger,T.C., Park,M.S., Waldo,G.S., Berendzen,J., Hung,L.W.,
Kim,C.Y., Smith,C.V., Sacchettini,J.C., Bellinzoni,M., Bossi,R.
et al. (2003) The TB structural genomics consortium: a
resource for Mycobacterium tuberculosis biology. Tuberculosis,
19. Sherman,D.R., Voskuil,M., Schnappinger,D., Liao,R., Harrell,M.I.
and Schoolnik,G.K. (2001) Regulation of the Mycobacterium
tuberculosis hypoxic response gene encoding alpha-crystallin.
Proc. Natl Acad. Sci. USA, 98, 7534–7539.
20. Rees,C.A., Demeter,J., Matese,J.C., Botstein,D. and Sherlock,G.
(2004) GeneXplorer: an interactive web application for
microarray data visualization and analysis. BMC Bioinformatics,
21. Reich,M., Liefeld,T., Gould,J., Lerner,J., Tamayo,P. and
Mesirov,J.P. (2006) GenePattern 2.0. Nat. Genet., 38, 500–501.
22. Subramanian,A., Tamayo,P., Mootha,V.K., Mukherjee,S.,
Ebert,B.L., Gillette,M.A., Paulovich,A., Pomeroy,S.L., Golub,T.R.,
Lander,E.S. and Mesirov,J.P. (2005) Gene set enrichment
Nucleic Acids Research, 2009,Vol. 37,Database issueD507
analysis: a knowledge-based approach for interpreting genome-wide
expression profiles. Proc. Natl Acad. Sci. USA, 102, 15545–15550.
23. Alter,O., Brown,P.O. and Botstein,D. (2000) Singular value
decomposition for genome-wide expression data processing and
modeling. Proc. Natl Acad. Sci. USA, 97, 10101–10106.
24. Boyle,E.I., Weng,S., Gollub,J., Jin,H., Botstein,D., Cherry,J.M. and
Sherlock,G. (2004) GO::TermFinder—open source software for
accessing Gene Ontology information and finding significantly
enriched Gene Ontology terms associated with a list of genes.
Bioinformatics, 20, 3710–3715.
Nucleic Acids Research, 2009, Vol. 37, Databaseissue