The FunGenES database: a genomics resource for mouse embryonic stem cell differentiation.

Abstract

Embryonic stem (ES) cells have high self-renewal capacity and the potential to differentiate into a large variety of cell types. To investigate gene networks operating in pluripotent ES cells and their derivatives, the "Functional Genomics in Embryonic Stem Cells" consortium (FunGenES) has analyzed the transcriptome of mouse ES cells in eleven diverse settings representing sixty-seven experimental conditions. To better illustrate gene expression profiles in mouse ES cells, we have organized the results in an interactive database with a number of features and tools. Specifically, we have generated clusters of transcripts that behave the same way under the entire spectrum of the sixty-seven experimental conditions; we have assembled genes in groups according to their time of expression during successive days of ES cell differentiation; we have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in "Expression Waves" and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic resources. The FunGenES database provides a comprehensive resource for studies into the biology of ES cells.

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The FunGenES Database: A Genomics Resource for
Mouse Embryonic Stem Cell Differentiation
Herbert Schulz2., Raivo Kolde3., Priit Adler3, Ire `ne Aksoy4, Konstantinos Anastassiadis5, Michael
Bader2, Nathalie Billon6, He ´le `ne Boeuf7, Pierre-Yves Bourillot4, Frank Buchholz8, Christian Dani6,
Michael Xavier Doss9, Lesley Forrester10, Murielle Gitton11, Domingos Henrique12, Ju ¨rgen Hescheler9,
Heinz Himmelbauer13,14,Norbert Hu ¨bner2, Efthimia Karantzali15,Androniki Kretsovali15,Sandra Lubitz5,
Laurent Pradier11, Meena Rai1, Ju ¨ri Reimand3, Alexandra Rolletschek16, Agapios Sachinidis9, Pierre
Savatier4, Francis Stewart5, Mike P. Storm17, Marina Trouillas7, Jaak Vilo3, Melanie J. Welham17,
Johannes Winkler9, Anna M. Wobus16, Antonis K. Hatzopoulos1,18*, for the ‘‘Functional genomics in
embryonic stem cells’’ Consortium
1Department of Medicine -Division of Cardiovascular Medicine and Department of Cell & Developmental Biology, Vanderbilt University, Nashville, Tennessee, United
States of America, 2Max-Delbru ¨ck-Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany, 3Institute of Computer Science, University of Tartu, Tartu, Estonia,
4INSERM U846, Stem Cell and Brain Research Institute, Bron, France, 5BioInnovation Zentrum, Technische Universitaet Dresden, Dresden, Germany, 6UMR 6543 CNRS,
Centre de Biochimie, Nice, France, 7Universite ´ Bordeaux 2, CNRS-UMR 5164, Bordeaux, France, 8Max-Planck-Institute of Molecular Cell Biology and Genetics, Dresden,
Germany, 9Institute of Neurophysiology, University of Cologne, Cologne, Germany, 10Queens Medical Research Institute E2.47, University of Edinburgh, Edinburgh,
United Kingdom, 11Sanofi-Aventis, Centre de Recherche de Paris, Paris, France, 12Instituto de Medicina Molecular, Faculdade de Medicina de Lisboa, Lisboa, Portugal,
13Max-Planck-Institute of Molecular Genetics, Berlin, Germany, 14Centre for Genomic Regulation (CRG), UPF, Barcelona, Spain, 15Institute of Molecular Biology &
Biotechnology-FORTH, Heraklion, Greece, 16Leibniz Institute (IPK), Gatersleben, Germany, 17Department of Pharmacy and Pharmacology, Centre for Regenerative
Medicine, The University of Bath, Bath, United Kingdom, 18Institute of Clinical Molecular Biology and Tumor Genetics, German Research Center for Environmental Health,
Helmholtz Center Munich, Munich, Germany
Abstract
Embryonic stem (ES) cells have high self-renewal capacity and the potential to differentiate into a large variety of cell types.
To investigate gene networks operating in pluripotent ES cells and their derivatives, the ‘‘Functional Genomics in Embryonic
Stem Cells’’ consortium (FunGenES) has analyzed the transcriptome of mouse ES cells in eleven diverse settings
representing sixty-seven experimental conditions. To better illustrate gene expression profiles in mouse ES cells, we have
organized the results in an interactive database with a number of features and tools. Specifically, we have generated
clusters of transcripts that behave the same way under the entire spectrum of the sixty-seven experimental conditions; we
have assembled genes in groups according to their time of expression during successive days of ES cell differentiation; we
have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence
Tags; transcripts have been arranged in ‘‘Expression Waves’’ and juxtaposed to genes with opposite or complementary
expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell
differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways;
and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links
to microarray and genomic resources. The FunGenES database provides a comprehensive resource for studies into the
biology of ES cells.
Citation: Schulz H, Kolde R, Adler P, Aksoy I, Anastassiadis K, et al. (2009) The FunGenES Database: A Genomics Resource for Mouse Embryonic Stem Cell
Differentiation. PLoS ONE 4(9): e6804. doi:10.1371/journal.pone.0006804
Editor: Mai Har Sham, The University of Hong Kong, China
Received March 19, 2009; Accepted July 9, 2009; Published September 3, 2009
Copyright: ? 2009 Schulz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The FunGenES Integrated Project was funded by a grant from the European Commission (6th Framework Programme, Thematic Priority: Life sciences,
Genomics and Biotechnology for Health, Contract No. : FunGenES LSHG-CT-2003-503494; http://ec.europa.eu/grants/index_en.htm). H.B. and M.T. were also
supported by the University Bordeaux 2 (http://www.u-bordeaux2.fr/index.jsp) and CNRS (http://www.cnrs.fr/); A.K.H. received NIH support, grant HL08395
(http://www.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: antonis.hatzopoulos@vanderbilt.edu
. These authors contributed equally to this work.
Introduction
Stem cells hold great promise for tissue repair after injury or as a
result of disease[1]. Studies in animal models and clinical trials
indicate that stem cells and their progeny may replace damaged
tissue improving organ recovery and function [2,3]. For this
reason, understanding the programs controlling self-renewal and
differentiation of stem cells may facilitate the development of tools
to unlock their regenerative potential. To this end, mouse
embryonic stem (ES) cells offer an accessible and pertinent model
system because they give rise to many different cell types in a
reproducible manner, can be propagated practically indefinitely,
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have relatively stable karyotypes, and are easy to genetically
manipulate [4–7]. Moreover, ES cell differentiation in vitro
recapitulates events that take place during early embryonic
development including the formation of the three germ layers of
ectoderm, mesoderm and endoderm, and the emergence of
endothelial, hematopoietic, cardiac, neuronal and hepatic or
pancreatic cells [8,9].
Functional studies have highlighted the critical roles of genes
such as Oct4 (Pou5f1), Nanog and Sox2 in the maintenance of ES
pluripotency and suppression of differentiation pathways [10–19].
Chromatin immunoprecipitation and chip analyses revealed that
both active and silenced genes in ES cells are directly bound by
one or more of these three proteins [17,19]. The recent discoveries
of new pluripotency factors including Klf4, Sall4, Zfp206, Esrrb,
Tcl1, Tbx3 and Zfx suggest that the expansion and fate of ES cells
follows a complex course requiring the coordinated action of a
number of yet to be characterized genes [20–26].
The links between the many genes involved in the maintenance
of pluripotency and the regulation of ES cell differentiation
programs are not well characterized. Microarray studies have the
potential to piece together groups of co-regulated genes and thus
lead to the discovery of novel components of genetic pathways in
ES cells. In recent years, a number of genome-wide approaches
have identified transcripts present in mouse and human ES cells or
their differentiated derivatives using a variety of gene expression
profiling methods [24,27–32]. This wealth of information also
underscored a degree of variability and ‘‘biological noise’’ among
data sets [33,34].
The ‘‘Functional Genomics in Embryonic Stem Cells’’ consor-
tium comprising 20 research groups (acronym FunGenES; http://
www.fungenes.org) has analyzed the transcriptome of ES cells
under a series of diverse stimuli during growth expansion and
differentiation. Besides information gathered to answer specific
experimental questions, as determined by the interests of
individual partners [35–41], the collective data offered the
opportunity to search for coordinated gene expression patterns
in a systematic exploration of the mouse ES transcriptome under a
battery of different experimental settings, thus minimizing possible
site-specific artifacts. The results have been organized in an
interactive, open-access database with a number of novel features
and search tools to promote studies into the biological properties of
embryonic stem cells.
Results
Coordinated analysis of the mouse ES cell transcriptome
The FunGenES consortium collected gene expression profiling
data from mouse ES cells in a coordinated fashion by streamlining
techniques and standardizing experimental protocols among
partners. To this end, consortium members selected three ES cell
lines (CGR8, E14TG2a and R1) for common use; ES cell clones
were karyotyped and tested by alkaline phosphatase staining
before being distributed to most of the consortium groups. A
number of laboratories shared serum batches and used a common
LIF source. Finally, RNA samples were prepared following the
same procedure and subsequent microarray analyses were
performed in a central facility using Affymetrix Mouse 430 v.2
arrays.
The configuration of each of the eleven individual experiments
and the RNA samples collected are summarized in Table 1. In
brief, the studies consisted of seven analyses on gene regulation in
undifferentiated ES cells, focusing on LIF targets, Stat3 and PI3K
regulated genes, as well as global gene expression changes through
epigenetic mechanisms; and, four studies where ES cells were
allowed to differentiate in monolayers or as embryoid bodies.
Differentiation took place either in control culture media, or in the
presence of various agents including retinoic acid (RA), Fibroblast
Growth Factor-2 (FGF2) and Wnt pathway activators. Detailed
descriptions of the individual experimental settings are included in
the Supplemental File S1. The total number of tested conditions
was 67, each performed in up to six, separate, biological replicates
using a total of 258 Affymetrix arrays.
Comparison of gene expression profiles showed a low number
of differentially expressed transcripts among the three ES cell lines.
Using 5% false discovery rate in ANOVA calculation in any of the
3 comparisons (CGR8 vs. E14TG2a vs. R1), there are 137 genes
(0.9% of the analyzed transcripts) that show a 2-fold difference or
higher in expression levels among the three lines; 34 of these genes
Table 1. Outline of the eleven experimental data sets in the FunGenES study.
Samplea
Experimentb
ES clonec
Conditionsd
Repeatse
INS-2 Comparison of ES clones E14TG2a/CGR8/R135
INS-1 Stat-3 targets in ES cellsE14TG2a54–6
CNRS-UMR-5164 LIF targets in ES cellsCGR865
UOB-1PI3-K targets in ES cells E14TG2a25
UOB-2PI3-K targets in ES cells E14TG2a63
IMBB-1 TSA effects on ES cellsCGR833
TUD-1 Tag effects on ES cellsE14TG2a63
UKOE-1 Standard ES differentiationCGR893
AVEF-1 ES differentiation under neurogenic conditions E14TG2a 114f
CNRS-UMR-6543 ES differentiation under adipogenic conditionsCGR893
IPK-1 ES differentiation favoring the pancreatic lineageR175
a: experiment abbreviation.
b: short description of individual experiment.
c: ES cell line used in the corresponding experiment.
d: number of different conditions analyzed.
e: number of independent replicates.
f: four replicates except AVEF-1 eb4 (two repeats).
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are .2-fold higher or lower expressed in E14TG2a, 5 in CGR8
and 11 in R1 cells.
Organizational design and special features of the
FunGenES database
To enhance the analytical power of the collected information,
facilitate data mining and provide public access of the consortium
results to the scientific community, the expression data have been
organized in an open, interactive database (http://biit.cs.ut.ee/
fungenes/) with a number of original features and tools (Figure 1).
These include: a) Global Clusters that consist of a small, tight
subset of genes that are co-expressed under the entire spectrum of
experimental conditions; b) Time Series of gene expression
profiles during successive days of standard ES cell differentiation;
c) Specific Gene Classes based on hierarchical clustering of
transcriptional factors and ESTs; d) Expression Waves of genes
with characteristic expression profiles during ES cell differentia-
tion, juxtaposed to waves of genes that behave in the exact
opposite way; e) Pathway Animations that illustrate dynamic
changes in the components of individual KEGG signaling and
metabolic pathways viewed in time-related manner; and, f)
Search Engines to display the expression pattern of any
transcript, or groups of transcripts, during the course of ES cell
differentiation, or to query the association of candidate genes with
various FunGenES database clusters. In addition, there are cross-
links to annotate and characterize these genes in the context of
other relevant genomic and stem cell resources.
Gene expression profiles are provided for all RNA samples
combined, or separately for the CGR8 and E14TG2a ES cell
lines. The list of genes belonging to a cluster together with the
heatmaps of individual transcripts, appear by clicking on the
corresponding cluster. The heatmaps of gene clusters or single
genes can be displayed in different color codes or configured using
a range of analytical parameters using the ExpressView tool. With
subsequent marking of any gene, or groups of genes, it is possible
to zoom in to the clustering visualization. In addition, when a
subset of genes is selected, it is possible to access functional analysis
and other relevant resources via the URLMAP link aggregator.
This provides crosslinks to external resources such as NCBI
Entrez, Ensembl, iHOP, Pubgene, MEM - Multi Experiment
Matrix, and a number of genomics and stem cell databases. There
is also a link to the g:Profiler tool that provides functional
annotation to assess the biological classification of transcripts with
specific expression patterns [42]. Terms of description in g:Profiler
include GO categories [43], KEGG [44] and Reactome pathways
[45], miRBase microRNA information [46], and TRANSFAC
motifs [47]. In addition to functional explanations, g:Profiler
provides convenient tools for dealing with different gene identifiers
and finding orthologs from other organisms.
Identification of gene sets with similar expression profiles
across all tested experimental conditions
The synchronized genomic analyses among consortium partners
presented the opportunity to search for coordinately expressed
genes, either during ES cell differentiation, or in response to
various stimuli. Towards this goal, we mined the genomics data to
identify sets of genes, the expression of which performed in the
same way over the entire spectrum of experimental conditions.
In order to facilitate the interpretation of the bioinformatic
output, and enhance the biological significance of the computa-
tional data, we pre-selected probe sets corresponding to previously
characterized genes. The initial focus on known genes with
Figure 1. Outline of the FunGenES database. The database home site is separated in six parts. The upper row views provide entry to different
ways the FunGenES data sets have been organized: in clusters using the entire 67 experimental conditions (Global Clusters, top left); according to
time of expression (Time Series) in 50 (Concise) and 200 (Analytical) clusters using a subset of experimental samples representing conditions without
additional stimuli (top middle); or depending on gene class, i.e., transcriptional regulators or ESTs (Specific Gene Classes, top right). In Time Series and
Specific Genes Classes, the expression profiles at various differentiation time points are also provided separately for CGR8 and E14TG2a cells. The
bottom row consists of three tools that offer: detailed organization of specific gene expression patterns (Expression Waves) during the differentiation
process (bottom left); animation of KEGG pathways organized by successive days of differentiation (Pathway Animations, bottom middle); and, a
search engine to obtain the expression of any transcript or groups of transcripts in the Affymetrix 430 v.2 arrays during the ES cell differentiation
process (bottom right). In all entry points, buttons provide a ‘‘Description’’ of how the data have been organized and the ‘‘Methods’’ used to
construct the various tools.
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common expression profiles across many conditions, allowed us to
interpret differences between conditions, as well as to identify
specific core groups of genes that could serve as anchor-points for
mapping gene function in future analyses. Specifically, we applied
exclusion criteria to screen out transcripts without annotation and
of unknown origin, as well as hypothetical transcripts or proteins.
This selection reduced the number of transcripts from 45,101 to
32,020. We then removed redundant probe sets, and probe sets
that showed minor differences in expression levels across all tests
setting the standard deviation of the log2signal over 67 conditions
to less than 0.45. The selection criteria brought the number of
transcripts used for cluster analysis to 5,959.
Unsupervised hierarchical clustering of the 5,959 genes, using
100 random permutations, gave rise to 115 groups, containing a
total number of 2,855 transcripts, with a probability of 95% or
higher that clustering was not random (Supplemental File S2).
Eighteen clusters had .20 transcripts, fifteen clusters contained
between 10–19 transcripts, whereas the remaining eighty two
clusters had 3–9 members. The heatmaps of the eighteen largest
clusters with .20 transcripts are shown in Figure 2. The heatmaps
and the complete list of genes belonging to each cluster, ordered
by cluster size, are available in Supplemental File S3 and in the
FunGenES database under the heading ‘‘Global Clusters’’.
The functional annotations of all the clusters with $10
transcripts, which were obtained using the on GO classification
categories of the g:Profiler tool for all the genes in each cluster, are
shown in Table 2 (for downregulated genes during ES cell
differentiation) and Table 3 (for upregulated genes). Inspection of
the data illustrates that in many instances, hierarchical clustering
grouped genes that have been functionally associated with
particular developmental and/or cellular processes. For example,
clusters containing genes that are upregulated during the course of
ES cell differentiation (Table 3) include in order of time of
expression: cluster 30 that represents genes which take part in the
formation of the three embryonic germ layers during gastrulation,
i.e., Goosecoid, Cerberus like 1 homolog, Wnt3, Mesp1, Mixl1, mEomes
and Even-skipped 1; cluster 15 containing molecular regulators of
early mesoderm development including Bmp2, Bmp5, Msx1, Msx2,
Cripto, Tbx20, Hey2, Smad6, Vegfr2 (Kdr), Foxf1 and Hand1; cluster
20, which comprises regulatory and structural genes linked to
Figure 2. Global hierarchical clustering analysis of the FunGenES microarray data. The average heatmaps of the eighteen largest clusters
with at least 20 members are displayed. Hierarchical clustering organized samples according to differentiation stages, with undifferentiated ES cells
and early differentiation states at the left and progressively more differentiated states toward the right. The cluster number is depicted on the left
side, the cluster size, i.e., the number of transcripts in the cluster, is shown on the right. Heatmap colors range from cyan (very low or no expression)
over black (low/middle) to yellow (high expression levels). The names of the 67 RNA samples are indicated on the top. The description of the
acronyms and the experimental conditions for each sample are provided in Table 1 and in Supplemental File S1, respectively. The names of the ES cell
clones used in each experiment and the time of RNA isolation during the ES cell differentiation process are coded with bars above the heatmaps.
Explanatory notes of the codes are indicated below the heatmaps.
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hemopoiesis such as Gata1, Nfe2, Klf1, Tie1, hemoglobins (Hba-x,
Hbb-b1) and Glycophorin A; cluster 12, which is rich in genes
involved in cardiac development, e.g., Mef2c, Myl4, cardiac Troponin
T2, Tropomodulin 1, myosin binding protein C, Bves, Angiopoietin 1 and
Angiopoietin 2; and, cluster 4, which consists mostly of genes
associated with neuronal development and differentiation, for
example, Neurog1, Neurog2, Olig2, Nkx6.1, Neurod4, Pou3f2, Pou3f4,
Cacna2d3, Cacng4, Kcnq2 and EphA5. The average expression
pattern of all the genes in these clusters is depicted in Figure 3A.
Taking into account that ES cells are isolated at embryonic day
3.5 post fertilization, the sequential appearance of genes specific
for gastrulation, mesoderm formation, hemopoiesis, cardiopoiesis
and neurogenesis during ES cell differentiation follows the timing
of comparable developmental stages in embryonic development.
For example, the transient expression of cluster 30 genes at day 3
in vitro, which corresponds to embryonic day E6.5 (3.5+3), matches
the expression timing of genes such as Cerberus-like 1 and Wnt3 in
vivo [48,49]. In a similar manner, the induction of hematopoietic
(cluster 20, day 3.5+4=E7.5) and cardiovascular-specific (cluster
12, 3.5+5=E8.5) genes follows the chronological order of the
appearance of blood islands and the formation of the heart tube
during embryonic development [50,51].
In contrast to the complex induction scheme of clusters
representing upregulated genes, clusters containing genes that
decrease upon differentiation form fewer clusters that fall mainly in
two categories, of genes suppressed early, at the onset of
differentiation (clusters 3 and 18), and of genes downregulated in
more gradual fashion (clusters 1, 8 and 13; Figure 3B). Downreg-
ulated clusters include mostly genes that take part in cell cycle,
proliferation and metabolism, as well as genes that have been
implicated in the maintenance of ES cell pluripotency (Table 2).For
instance, Cluster 1 contains genes such as cyclin A2, cyclin B1, cyclin
E1, cyclin F, polymerase alpha 2, RNA polymerase II polypeptide H, and
RNA polymerase III polypeptide G, whereas Cluster 3 genes include
Nanog, Sox2, Pou5f1 (Oct4), Klf2, Zpf42 (Rex1) and Esrrb.
The validation rate of the microarray expression profiling data
was 90.7%, based on results obtained independently in eleven
consortium laboratories. In brief, 330 of 364 genes, tested by
quantitative or conventional PCR, gave comparable expression
patterns to the data obtained by microarray analysis. A
representative comparison of expression profiles obtained by Q-
PCR and array analysis for fifteen genes, belonging to five of the
clusters depicted in Figure 3, is shown in Supplemental File S4.
Since there is a higher than 95% chance that cluster
assignments are accurate (Supplemental File S2), and our
validation analysis shows that 90.7% of the array expression
patterns match the RNA analysis results using other techniques
(e.g., Q-PCR), we estimate that more than 86% of the genes in a
cluster follow the corresponding average expression profile. It is
likely that these genes are components of related molecular or
cellular pathways, or they might be targets of common regulatory
mechanisms, or both [52–54]. Next to well-characterized genes,
clusters often contain transcripts the function of which is poorly
understood. Our analysis predicts that the latter participate in the
same biological processes as the known genes in the corresponding
clusters – thus providing a starting point to study the function of
poorly characterized transcripts.
Time Series and Specific Gene Classes of the FunGenES
Database
To better visualize changes in gene expression programs during
differentiation, we performed k-means clustering analysis, followed
Table 2. Functional annotation of Global Clusters of
downregulated genesa.
ClusterGene Number Functional Annotation
1 593Cellular Machinery: Cell Cycle, Organelles Nucleic
Acid Metabolism, Synthesis and Processing
3349 Transcriptional Regulation, Metabolism
8 62Cell Cycle, Organelles, Nucleic Acids metabolism
and binding
1336Cell Cycle, Signal transduction, Nucleic Acids
metabolism, Transcription
18 23Cell Cycle, Meiosis
21 14No strong annotation: Signal Transduction
24 13Ribosomal proteins
25 13 Cell Cycle, Replication
2712 No strong annotation
28 12 t-RNA biosynthesis
3310No strong annotation
a: clusters containing 10 or more genes that are downregulated during ES cell
differentiation.
doi:10.1371/journal.pone.0006804.t002
Table 3. Functional annotation of Global Clusters of
upregulated genesa.
ClusterGene NumberFunctional Annotation
2 423Development, Morphogenesis, Signal
transduction, Apoptosis
4 193Neurogenesis, Development, Morphogenesis
5 129No strong annotation
6 113No strong annotation: Cell adhesion, Signal
transduction, Neuronal
7 101Cardiovascular development, Branching
morphogenesis, extracellular matrix
9 58Extracellular matrix biosynthesis, Cell adhesion
10 43 Immune response
1142Extracellular matrix biosynthesis
1241Cardiovascular development
14 33Development, Morphogenesis, Transcriptional
regulation
15 30Mesoderm development, Branching
morphogenesis, Vascular development
1628 Extracellular matrix, Collagen pathway
17 27Extracellular matrix, Chemokines, Cell adhesion
1918No strong annotation: Cell proliferation, immune
response
20 17Hematopoiesis
2214No strong annotation: Signal Transduction
2313No strong annotation
2613No strong annotation
2911No strong annotation
30 10Gastrulation, Cell migration
3110Calcium binding, structural proteins
32 10No strong annotation
a: clusters containing 10 or more genes that are upregulated during ES cell
differentiation.
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by hierarchical clustering, to group genes by their timing of
induction or suppression during the normal ES cell differentiation
process. For this purpose, we used the data from a subset of
consortium samples representing untreated states without addi-
tional stimuli (26 conditions listed in Supplemental File S5) and,
we included transcripts with significant differential expression
among samples (standard deviation.0.45). The resulting ‘‘Time
Series’’, containing 8,211 genes, have been organized in 50
Concise (Figure 4) and 200 Analytical clusters.
The ‘‘Time Series’’ clusters expanded the number of genes that
follow a specific expression pattern revealed in the previous global
hierarchical clustering. For example, cluster 5 of the concise
‘‘Time Series’’, which consists of transiently induced genes around
day 3 of differentiation, similarly to Global Cluster 30, contains the
same transcripts, but in addition, it also includes T-brachyury, Axin2,
Mesp2, Fgf8, Wnt8a, Sp5, Sp8, Follistatin, Mix1 and Lim1. These
genes have been also implicated in the gastrulation phase of
embryogenesis [55] indicating that ‘‘Time Series’’ clusters provide
a comprehensive collection of genes expressed at specific stages of
ES cell differentiation and early embryonic development.
To assist searches of interconnected circuits of gene expression
regulators, we carried out clustering of genes related to
transcriptional activation (Figure 4). Finally, we analyzed ESTs
separately to distinguish the ones that are expressed in ES cells or
during the differentiation process. From approximately 12,000
ESTs, only 1,027 show a specific expression pattern (8.6% of all
ESTs present in the Affymetrix 430 2.0 microarray). This is in
contrast to known genes where 21% have a particular pattern,
possibly because a number of ESTs included in the microarrays
are cloning artifacts. However, the remaining 1,027 ESTs might
represent novel transcripts with potentially important functions in
stem cell biology and embryonic development. The 1,027 ESTs
have been grouped in 50 clusters based on their timing of
appearance (Figure 4). About half are expressed specifically in ES
cells, the rest in ES cell derivatives. Transcription factor and EST
clusters can be accessed through the ‘‘Specific Gene Classes’’
window of the FunGenES Database.
Gene ‘‘Expression Waves’’
To better illustrate and map co-regulated genes with different
activation and deactivation profiles, the levels of every transcript
have been assigned to graphs of ‘‘Expression Waves’’ that follow a
particular, predetermined, expression pattern (Figure 5). The
names of genes belonging to the corresponding ‘‘Expression
Wave’’ are included below the graph. The graph and gene content
representing transcripts expressed in the opposite manner is
available on the same page for side-by-side comparisons. In this
way, it is possible to search the database for groups of potentially
interconnected genes as a starting point to decipher regulatory
networks of transcription factors, signaling molecules and
membrane receptors, or for indications of genes that might be
co-regulated by the same genetic pathways.
Search Engines of the FunGenES database and links to
external databases
To maximize the analytical power of the database and integrate
it with the existing genomic and stem cell resources, we included
the ‘‘Study your Gene(s) of Interest’’ search engine. For any gene
of interest, or group of genes, it provides via URLMAP links to
display the expression profile across the entire spectrum of the
FunGenES data. This provides electronic analysis of the
expression profile of any gene(s) in mouse ES cells and during
the subsequent stages of differentiation by using standard
abbreviated gene names, Affymetrix probe set IDs, or any
identifier supported by the Ensembl database. An example of
the expression profiles for the 19 members of the Wnt protein
family of morphogens in ES cells and during the first 10 days of
differentiation, obtained using the FunGenES search engine, is
shown in Figure 6. The search tool also provides a fast assessment
of expression profiling data obtained by RT-PCR or other
techniques. The design allows easily addition of future data sets to
expand and update the analytical power of the search engines.
In addition to the visualization of expression profiles during ES
cell differentiation, the search engine provides links to analyze the
Figure 3. Average expression profiles of selected Global Clusters. A. Average expression levels of Global Clusters 4, 12, 15, 20 and 30 that
contain up regulated transcripts during ES cell differentiation. The clusters consist of genes specific for gastrulation (cluster 30), mesoderm formation
(15), hemopoiesis (20), cardiopoiesis (15) and neurogenesis (4). The sequential appearance of genes specific to early developmental stages matches
the timing of their induction during embryonic development. B. Average expression levels of all the transcripts in Global Clusters 1, 3, 8, 13 and 18
containing genes the expression of which decreases upon differentiation. Expression in Clusters 3 and 18 is suppressed early, at the onset of
differentiation, whereas the expression of genes in clusters 1, 8 and 13 declines in gradual fashion. Clusters 1, 3, 8, 13 and 18 include genes that take
part in cell cycle, proliferation and metabolism, as well as in self-renewal and maintenance of ES cell pluripotency.
doi:10.1371/journal.pone.0006804.g003
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selected genes using many publicly available tools and resources.
As mentioned above, such links include external resources such
as NCBI Entrez, Ensembl, iHOP, Pubgene, MEM - Multi
Experiment Matrix, and a number of genomics and stem cell
databases. Moreover, the g:Profiler toolset provides functional
annotation.
Pathway Animations
To examine the action of individual pathways in toto during ES
cell differentiation, the FunGenES database was given an
additional feature called ‘‘Pathway Animations’’ that depict
dynamic changes in specific genetic, signaling or metabolic
pathways viewed in time-related animations based on the KEGG
annotation [44,56]. The resource also offers a set of tools that
allow the users to reanimate the graphs by selecting specific time
points and/or subsets of pathway components.
Figure 7 depicts a stationary view of the KEGG pathways for
‘‘Cell Cycle’’ and ‘‘Apoptosis’’ at three time points; it appears that
ES cells (day 0) have higher numbers of expressed genes involved
in cell cycle (rectangles in red color) compared to differentiated
cells (day 10). Almost all of the genes expressed at day 0 have been
silenced by day 10 (green) and replaced by a new set of genes. The
extensive changes in the expression profile from ES cells (day 0) to
differentiated cells at day 10 are suggestive of a broad overhaul of
the self-renewal machinery. The ‘‘Cell Cycle’’ animated pathway
shows that genes encoding regulators of DNA replication are
expressed at high levels in pluripotent, self-renewing ES cells and
are progressively down regulated during differentiation. They
include genes of the origin of replication complex (orc), the
minichromosome maintenance (mcm), and the cell division cycle
(cdc) families. Genes involved in DNA damage control and
inhibition of DNA synthesis [49], in particular Atm, Chk1 and
Chk2, are also highly expressed in ES cells, but decline during
differentiation. These changes are indicative of the active
replication machinery and the tightly controlled replication fidelity
in proliferating ES cells [57–59].
Undifferentiated ES cells are also characterized by elevated levels
of transcripts encoding the G1/S transition-promoting complex
Figure 4. Time Series and Specific Gene Classes of the FunGenES database. Heatmaps of the 50 concise clusters for All Genes, Transcription
Factors and ESTs according to their timing of induction or suppression during the normal ES cell differentiation process. Day 0 represents
undifferentiated ES cells, days 1–7 and 10–11, the corresponding differentiation days. The first column to the right of each cluster is the cluster
number (cluster ID); the second column is the number of genes that belong to the cluster (cluster size).
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Figure 5. The ‘‘Expression Waves’’ tool of the FunGenES database. A. Partial snap shot of the ‘‘Expression Waves’’ window that depicts
detailed specific expression patterns of genes during consecutive days of ES cell differentiation. Numbers in individual panels represent the number
of genes the expression of which matches the specific profile. For example, the top left panel (red asterisk) represents 93 genes that become down
regulated as soon as differentiation begins. After clicking on any of the panels, for example on the top left panel marked with a red asterisk, the
expression profiles of the individual genes that follow the corresponding pattern as well as their names appear in a new window (as shown in B). B.
Individual expression profiles of the 93 genes marked with a red asterisk in A are shown in the top panel. The graph and gene content representing
transcripts expressed in the opposite manner is available on the same page for side-by-side comparison (lower graph on the left). Clicking on this
graph opens a new window with the corresponding genes (bottom panel). Representative examples of genes belonging in the depicted Expression
Waves are shown on the right of the graphs. Using the two link buttons on the left, it is possible to further study the gene list of a particular ‘‘wave’’ in
the context of publicly available databases, or view the gene expression profiles in ExpressView.
doi:10.1371/journal.pone.0006804.g005
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Figure 6. The FunGenES database Search Engine for gene expression profiles during ES cell differentiation. Expression profiles of the
19 Wnt genes during ES cell differentiation with the corresponding Affymetrix IDs using the ExpressView feature. Wnts have grouped in members
expressed during early differentiation stages (Wnt2b, Wnt3a, Wnt6, Wnt8b, Wnt9b, Wnt10b, Wnt16); transiently around day 3 of differentiation (Wnt3,
Wnt5b, Wnt8a); or, Wnt genes that appear primarily after day 4 (Wnt2, Wnt4, Wnt5a, Wnt7a, Wnt7b, Wnt9a, Wnt11). The heatmaps show weak
expression of Wn1 and Wnt10a throughout differentiation and high levels of Wnt7b in undifferentiated ES cells. Besides Affymetrix IDs, searches can
be performed with any standard gene name or identifier, as well as by mixing ID types. The expression profiles in different ES cell lines can be
obtained by selecting the corresponding dataset. A number of options (top right) allow custom configuration of data analysis and presentation.
doi:10.1371/journal.pone.0006804.g006
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cyclin E1:Cdk2 and, by contrast, low levels of transcripts encoding D-
type cyclins and Cdk4/6 inhibitors of the INK4 family (p15, p16,
p18, p19; Figure 7). Differentiation is associated with a decrease in
cyclin E1 and a concomitant elevation in D-type cyclins and cdk
inhibitor transcripts [57,60]. These results are likely due to the
progressive switch from a cyclin E-based autonomous cell cycle,
which characterizes self-renewing ES cells, to the D-type cyclins/
Retinoblastoma (Rb) protein-regulated somatic cell cycle [61].
Conversely, few pro-apoptotic genes are expressed in ES cells
(day 0; most boxes appear in green), but many are gradually
induced during the differentiation process showing the exact
opposite pattern from the genes involved in cell proliferation
(Figure 7). As observed for cell-cycle genes, there is minimal
overlap between apoptosis-associated genes expressed at days 0
and 10. This strikingly complementary pattern suggests a
reciprocal interrelationship between the balance of pro- and
anti-apoptotic genes in ES cells and their differentiated progeny.
Discussion
Functional analyses using loss-of-function and protein-protein
interaction approaches, as well as bioinformatics tools, have began
to piece together the regulatory networks active in ES cells [24,62].
Furthermore, genome-wide studies, combining chromatin immu-
noprecipitation (ChIP) and array hybridization (ChIP-on-chip),
have revealed that both active and silenced genes are directly
bound in ES cells by one or more of the core pluripotency factors
Oct4, Sox2 and Nanog [17,19,63].
Figure 7. Snap shots from the animated Cell Cycle and Apoptosis KEGG pathways. The images depict day 0 (undifferentiated ES cells), day
4 and day 10 of differentiation. Each box in a pathway box represents a gene or gene family that can be visualized by marking the box during
animation. The different family members are represented with juxtaposed vertical rectangles within the same box. For genes having multiple probe
sets in the Affymetrix microarray, the corresponding gene rectangle is divided horizontally with each line depicting the expression level of an
individual probe set. Red color marks expressed genes and green color indicates absence of detectable expression. Color intensity reflects expression
levels. The table below lists the genes from the Cell Cycle and Apoptosis KEGG pathways that are expressed at day 0 and day 10. ORC: origin of
replication complex; MCM: mini-chromosome maintenance.
doi:10.1371/journal.pone.0006804.g007
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However, it appears that the actual core factor set regulating
pluripotency and early differentiation in ES cells is larger and
more highly interconnected than previously suspected. Kim et al.
have performed a genome-wide analysis of target promoters for
nine transcription factors, namely Oct4, Sox2, Klf4, c-Myc,
Nanog, Dax1, Rex1, Zpf281, and Nac1 [64]. They found that
target promoters bound by a single or few factors tend to be
inactive or repressed, whereas promoters bound by more than four
factors are active in the pluripotent state and become repressed
upon differentiation. Interestingly, targets of Myc or Rex1 are
implicated in protein metabolism, whereas targets of the other
factors are enriched in genes involved in developmental processes.
The results also established a hierarchy within the key pluripo-
tency factors such that Klf4 serves as an upstream regulator of
feed-forward circuits involving Oct4, Sox2, Nanog and Myc.
The increasing complexity of gene regulatory networks
emerging from these studies, combined with the surging amount
of genomics and proteomics work, underscore the need for
resources that would enable the scientific community to readily
mine available and prospective data. The FunGenES database
provides such a template with a number of tools including
Animation of KEGG Pathways, Expression Waves, Time Series,
Specific Gene Classes, such as ESTs and transcription factors, and
searches for the expression pattern of any gene or transcript during
ES cell differentiation using standard gene names and IDs. Search
results are linked to: comprehensive annotation tools using the
g:Profiler tool, which includes the presence of common regulatory
motifs in promoter areas and miRNA targeting information; and,
to available resources such as NCBI Entrez, Ensembl, etc.
Genomic studies, which in principle group together co-
regulated genes, can potentially identify new components of
known regulatory pathways in ES cells that can subsequently be
explored in functional studies. In addition to well-described genes,
clusters often contain transcripts the function of which has not yet
been associated with a specific biological process thus providing
novel unexplored links to known molecular pathways.
Although the database described here was based on the gene
expression profiling results of the FunGenES consortium, it can be
easily adapted to incorporate available or future genomics data
obtained in ES cells. Moreover, the analytical paradigms and
expression pattern clusters presented here could provide a scaffold
for comparative analyses with human ES cell lines. This
information will be particularly important for future evaluation
of ES-like induced pluripotent stem (iPS) cells reprogrammed from
somatic tissues that can be potentially used to derive pancreatic
cells, cardiomyocytes or neurons for organ regeneration [21,65–
67]. For example, the g:Profiler tool provides the possibility to
convert mouse Affymetrix probe set numbers to any Affymetrix
probe set numbers from other organisms, allowing gene profiling
comparisons among data sets generated in different species. This
tool also allows conversion of previous Affymetrix probe set
numbers (i.e., the first generation of Affymetrix microarrays -
U74v2) to the more recent microarray probe set numbers (like the
MG430v2 used in this study).
During the last years, a growing number of repositories of
microarray data and other forms of gene expression profiles for
stem cell research have been developed [68–71]. Data presenta-
tion is heterogeneous and ranges from: simple storage of
expression data and experiment information (StemDB); presenta-
tion of lists of specific regulated transcripts (HESC); specific
analysis results of a closed dataset (SCDb); or storage and
visualization of variable resources with correlative and mutual
information about single transcripts (one to many relationship,
StemBase) [68–70]. To facilitate data comparison between the
FunGenES database and other resources, we have included a
series of links to other Stem cell databases, i.e., to SCDb,
Amazonia in the Study your Gene(s) of Interest search engine.
This way it is possible to obtain and compare the expression
pattern of genes in the FunGenES database to the expression
profiles in other tissues, experimental settings, or different stem
cells types.
In contrast to existing microarray database resources, the
FunGenES database includes a state of the art tools for the
interactive visualization of gene to gene relationships. It provides
gene lists and hierarchical matrices using co-expression analysis by
distance-base clustering (k-means, hierarchical clustering), as well
as integrated gene expression analyses by mapping observed gene
expression changes onto specific signaling and metabolic path-
ways. We expect that not only regenerative medicine applications,
but also basic science studies will benefit from the resources
described here, especially when compared to expression profiling
data obtained from loss- and gain-of-function approaches
[19,27,72]. Furthermore, the assignment of ESTs and genes to
specific pathways provide a fresh collection of novel components
that can be further explored in functional assays during embryonic
development and in human diseases.
Materials and Methods
RNA isolation and microarray hybridization
Total RNA was isolated using the RNeasy Mini Kit from
Qiagen and treated with RNase-free DNase I (5 units/100 mg of
nucleic acids, Sigma). Biotinylated cRNA was prepared according
to the standard Affymetrix protocol [73]. In brief, double-stranded
cDNA was synthesized from 10 mg total RNA using the
SuperScript Choice System (Invitrogen) and the Affymetrix
T7-(dT)24primer. Following phenol/chloroform extraction and
ethanol precipitation, the cDNA was transcribed into biotin-
labeled cRNA using T7 polymerase (Ambion MEGAScript T7).
cRNA products were purified using the RNeasy kit (Qiagen) and
fragmented to an average size of 30–50 bases according to
Affymetrix recommendations. 15 mg of fragmented cRNA were
used to hybridize to the Mouse Genome 430 version 2.0 Array for
16 hrs at 45uC. The arrays were washed and stained in the
Affymetrix Fluidics Station 450 and scanned using the Affymetrix
GeneChip Scanner 3000 7G. The image data were analyzed with
the GeneChipH Operating Software (GCOS) 1.4 using Affymetrix
default analysis settings. Arrays were normalized by the log scale
robust multi-array analysis (RMA) [74].
We used 258 Affymetrix GeneChips to analyze 67 individual
experimental conditions (outlined in Table 1). A detailed
description of the individual experiments is provided in Supple-
mental File S1. The eleven microarray data sets have been
annotated in a MIAME compliant manner and deposited in EBI
ArrayExpress (http://www.ebi.ac.uk/microarray-as/ae/. The ac-
cession numbers are as follows: AVEF-1: E-TABM-669, CNRS-
UMR-5164: E-TABM-667, CNRS-UMR-6543: E-TABM-668,
IMBB-1: E-TABM-670, INS-1: E-TABM-562, INS-2: E-TABM-
671, IPK-1: E-TABM-493, TUD-1: E-TABM-675, UKOE-1: E-
TABM-672, UOB-1: E-TABM-673, UOB-2: E-TABM-674).
Each array was checked for general assay quality (39-59 ratio of
Gapdh ,1.5, noise (RawQ) ,4 and scaling factor at a TGT value
of 200 ,4). The robust multi-array average (rma) normalization
(background-adjustment, quantile normalization and median
polish summarization) has been performed using RMAExpress
version 1.0 beta 4. In addition, we assessed data integrity by
calculating Pearson correlation z-values over the complete dataset
of 45,101 probe sets. The difference between array to array
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correlation within biological replicates (z=2.7360.38) and
between non replicates (z=1.9060.35) indicates that there is a
sufficiently high signal to noise ratio.
Comparison of gene expression profiles in the three ES
cell lines
For comparison analysis, from the 45,101 probe sets represent-
ed on the Mouse 430 version 2 array, we selected 30,526 gene-
associated transcripts (eliminating transcripts without annotation
and of unknown origin, as well as hypothetical transcripts or
proteins). In addition, for genes represented multiple times on the
microarray, we selected the transcript with the strongest average
signal as representative for the respective gene. This brought the
number of analyzed transcripts to n=15,263. A 5% false discovery
rate in ANOVA calculation and a 2-fold difference or higher in
any of the 3 comparisons (CGR8 vs. E14TG2a vs. R1) led to a set
of 137 differentially expressed genes (0.9% of the analyzed
transcripts).
Data preparation for unsupervised hierarchical
clustering – Global Clusters
The first step in our data analysis was to average the biological
replicates for each of the 67 experimental conditions. To identify
genes that cluster together under the tested conditions, we
excluded probe sets with a standard deviation in expression values
of , log2 (0.45) from the vector mean. We then removed
redundant gene/probe sets taking into account the ENTREZ,
Unigene and RefSeq gene-id annotations. Among redundant
probe sets, we selected the probe set with the highest average
expression signal. We also removed probe sets of unknown origin,
for example RIKEN sequences, or hypothetical transcripts/
proteins. These criteria led to a data set of 5,959 transcripts.
Unsupervised hierarchical clustering
Correlation of differentially expressed transcripts was detected
by hierarchical clustering of expression values with the Cluster
version 2.11 software [52] applying mean centering and
normalization of genes and arrays before the computational
clustering analysis. Average linkage hierarchical clustering of the
data was carried out as described [75].
Permutations
The correlation significance of expression profiles between
probe sets was assessed empirically by one hundred rounds of
random permutations. For each round, the 67 values for each
probe set were randomly redistributed [76] and data sets clustered
as described [75]. The best similarity scores of each permuted and
clustered data set was collected to evaluate the 95th percentile of
significant clusters in the original data set. 5% of the permuted
data sets gave rise to clusters containing no more than two genes at
a similarity score .0.85071 (Supplemental File S6). Clusters with
3 or more genes (115 clusters) were documented and selected for
further analysis.
Clustering of the data in Time Series
Besides the clustering described above that was based on the
entire spectrum of experimental conditions, expression data were
clustered according to timing of expression in a two-step strategy.
First, probe sets were clustered with k-means into a small number
of clusters using chord distance (Euclidean distance over vectors
normalized to unit sphere). In a second step, the resulting clusters,
represented by mean profiles, were clustered using average linkage
hierarchical clustering with Pearson correlation distance measure,
and visualized in a heatmap representation [52]. No filtering
besides removing genes with low variation was applied to these
data sets.
Expression Waves
We developed a method to identify all genes that have
characteristic expression patterns during ES cell differentiation.
In brief, transcripts were included into a particular expression
wave represented by a single artificial template, if its correlation
with the specific pattern was higher than a certain threshold and
also highest among all other patterns. This analysis was done in
two different stringent conditions with correlation thresholds of 0.8
and 0.85. The results are presented in a series of graphs, with the
list of genes that belong in the depicted pattern identified below.
Each graph is juxtaposed to its ‘‘mirror image’’, i.e., the graph
representing genes that behave exactly the opposite way.
Pathway Animations
We designed animations of pathways in the Kyoto Encyclope-
dia of Genes and Genomes (KEGG) [44]. The animations use
diagrams available at the KEGG webpage, which portray
connections between pathway components. The expression levels
of relevant genes are shown in the diagrams by the standard red
(high) – green (low) color codes. In case a gene family represents a
particular pathway step, the corresponding box displays the
expression patterns of all individual members of the family in
adjacent vertical stripes. Each stripe may be further divided
horizontally depicting the expression patterns of different probe
sets corresponding to the same transcript.
‘‘Study your Gene(s) of Interest’’
This feature has been designed to allow investigators to search
and display the expression of any probe set during ES cell
differentiation based on the FunGenES data sets. The program
draws clustered heatmaps with the columns annotated with
relevant sample information. The search engine recognizes
common gene identifiers; the conversion to Affymetrix probe set
IDs is done using Ensembl BioMart [77] mappings. The heatmap
representation is based on the ExressView tool, which is linked to
the URLMAP, to provide further analysis options for selected
genes. The organization of the various FunGenES tools is depicted
in Supplemental File S7.
Supporting Information
Supplemental File S1
experimental designs & Contact Information
Found at: doi:10.1371/journal.pone.0006804.s001 (0.24 MB
PDF)
Detailed overview of the microarray
Supplemental File S2
clustering. Histogram of the number of clusters (y-axis) for each
cluster size (x-axis). Clusters with more than 100 genes are listed
separately on the top right corner.
Found at: doi:10.1371/journal.pone.0006804.s002 (0.58 MB TIF)
Yield of the unsupervised hierarchical
Supplemental File S3
Found at: doi:10.1371/journal.pone.0006804.s003 (0.40 MB
XLS)
Gene content of the 115 Global Clusters
Supplemental File S4
obtained by Q-PCR (left panels) and microarray analysis (right
panels). The gene name is depicted on top of the Q-PCR graph;
the Affymetrix ID of the same gene marks the corresponding
adjacent graph. CT: Cycle Threshold values of the Q-PCR
analysis. Signal: normalized log2 signal values from the microarray
Comparison of gene expression profiles
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analysis. Genes are organized according to the Global Cluster they
belong as indicated. The results show comparable gene expression
profiles between microarray and Q-PCR data.
Found at: doi:10.1371/journal.pone.0006804.s004 (0.97 MB TIF)
Supplemental File S5
Series
Found at: doi:10.1371/journal.pone.0006804.s005 (0.05 MB
PDF)
Experimental data sets used in Time
Supplemental File S6
Ranked plot of the best similarity scores (y-axis) of 100 permutated
and clustered datasets (x-axis) and the evaluated 95th percentile of
significant clusters (blue line). The results are given for cluster
nodes with more than two (black line) or three (red line) cluster
members.
Found at: doi:10.1371/journal.pone.0006804.s006 (0.55 MB TIF)
Evaluation of significant correlations.
Supplemental File S7
GenES Database depicting tools to view expression data sets and
links to external resources and databases. Tools in boldface have
been developed specifically for the FunGenES Database.
Found at: doi:10.1371/journal.pone.0006804.s007 (0.79 MB TIF)
Schematic representation of the Fun-
Author Contributions
Conceived and designed the experiments: KA MB NB HB FB CD LF DH
JH HH NH AK LP AS PS FS JV MJW AMW AKH. Performed the
experiments: HS IA KA NB PYB MXD MG NH EK SL MR AR MPS
MT. Analyzed the data: HS RK PA IA HB LF NH AK LP MR JR PS JV
MJW JW AKH. Wrote the paper: HS RK HB PS JV MJW JW AKH.
Coordinated the FunGenES consortium. Coordinator of the FunGenES
consortium: JH. Designed and supervised the global analysis of the
FunGenES gene expression profiling data and wrote the manuscript draft:
AKH. Did the global data clustering analysis and compiled the graphs: HS.
Developed most of the public FunGenES Database features: RK. Directed
the Affymetrix GeneChip processing facility: NH. Managed the bioinfor-
matics team that built the public FunGenES Database: JV. Contributed to
the bioinformatics analysis and the construction of the FunGenES
Database: PA JR JW. FunGenES principal investigators designed, took
part and oversaw the individual experiments: MB HB FB CD LF AKH
DH HH JH AK LP AS PS FS MJW AMW. Researchers performed ES cell
cultures, RNA preparations and validated microarray data: IA KA NB
PYB MXD MG EK SL MR AR MPS MT. Results were discussed among
all Consortium partners. Authors are included in alphabetical order except
the first and senior authors.
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