Comparative co-expression analysis in plant biology

Article (PDF Available)inPlant Cell and Environment 35(10):1787-98 · April 2012with32 Reads
DOI: 10.1111/j.1365-3040.2012.02517.x · Source: PubMed
The analysis of gene expression data generated by high-throughput microarray transcript profiling experiments has shown that transcriptionally coordinated genes are often functionally related. Based on large-scale expression compendia grouping multiple experiments, this guilt-by-association principle has been applied to study modular gene programmes, identify cis-regulatory elements or predict functions for unknown genes in different model plants. Recently, several studies have demonstrated how, through the integration of gene homology and expression information, correlated gene expression patterns can be compared between species. The incorporation of detailed functional annotations as well as experimental data describing protein-protein interactions, phenotypes or tissue specific expression, provides an invaluable source of information to identify conserved gene modules and translate biological knowledge from model organisms to crops. In this review, we describe the different steps required to systematically compare expression data across species. Apart from the technical challenges to compute and display expression networks from multiple species, some future applications of plant comparative transcriptomics are highlighted.
Comparative co-expression analysis in plant biologypce_2517 1787..1798
Department of Plant Systems Biology, VIB, 9052 Gent, Belgium and Department of Plant Biotechnology and Bioinformatics,
Ghent University, 9052 Gent, Belgium
The analysis of gene expression data generated by high-
throughput microarray transcript profiling experiments has
shown that transcriptionally coordinated genes are often
functionally related. Based on large-scale expression
compendia grouping multiple experiments, this guilt-by-
association principle has been applied to study modular
gene programmes, identify cis-regulatory elements or
predict functions for unknown genes in different model
plants. Recently, several studies have demonstrated how,
through the integration of gene homology and expression
information, correlated gene expression patterns can be
compared between species. The incorporation of detailed
functional annotations as well as experimental data describ-
ing protein–protein interactions, phenotypes or tissue
specific expression, provides an invaluable source of infor-
mation to identify conserved gene modules and translate
biological knowledge from model organisms to crops. In
this review, we describe the different steps required to sys-
tematically compare expression data across species. Apart
from the technical challenges to compute and display
expression networks from multiple species, some future
applications of plant comparative transcriptomics are
Key-words: bioinformatics; comparative genomics; expres-
sion analysis; orthology.
Comparative sequence analysis is a successful tool to study
homologous gene families (genes sharing common ances-
try), define conserved gene functions between orthologs
(homologs separated by a speciation event) and identify
lineage- and species-specific genes. Most annotations of
newly sequenced genomes are based on similarity with
sequences for which functional information is available.
Apart from conserved sequences, inter-species differences
provide important clues about evolutionary history and
species-specific adaptations (Hardison 2003). Accelerated
by technological innovations, genome-wide data describing
functional properties including gene expression, protein–
protein interactions and protein–DNA interactions are
becoming available for an increasing number of model
organisms. Consequently, the integration of functional
genomics information provides, apart from gene sequence
data, an additional layer of information to study gene func-
tion and regulation across species (Tirosh, Bilu & Barkai
Depending on the availability of expression profiling
technologies and the evolutionary distances between
the species under investigation, a number of different
approaches can be applied to study expression profiles
between organisms (Lu, Huggins & Bar-Joseph 2009). The
hybridization of samples from closely related species to the
same microarray requires compatible experimental condi-
tions and has been first used in studies comparing different
Brassicaceae species (Taji et al. 2004; Weber et al. 2004;
Gong et al., 2005, Hammond et al. 2005).To monitor specific
responses between more distantly related species, multiple
microarray experiments are combined to first identify dif-
ferentially expressed (DE) genes in each species indepen-
dently, and then compare these genes among different
species. Downstream comparative sequence analysis of DE
genes between different species or kingdoms makes it pos-
sible to identify evolutionary conserved responsive gene
families as well as species-specific components. In addition,
unknown genes showing a conserved response shared
between multiple species are interesting targets for detailed
molecular characterization (Vandenbroucke et al. 2008).
Similarly, Mustroph and co-workers successfully applied a
comparative meta-analysis of low-oxygen stress responses
to identify several unknown plant-specific hypoxia respon-
sive genes (Mustroph et al. 2010). More recently, microarray
datasets were integrated to study orthologs and specific
biological processes between more distantly related plant
species, including Arabidopsis thaliana (Arabidopsis),
Oryza sativa (rice) and Populus (poplar). Two pioneering
studies, comparing microarray expression profiles between
Arabidopsis and rice, focused on conservation and diver-
gence of light regulation during seedling development and
the analysis of global transcriptomes from representative
organ types between both plant model systems (Jiao et al.
2005; Ma et al. 2005). Similarly, Street and co-workers iden-
tified several transcription factors involved in leaf develop-
ment based on cross-species expression analysis of
orthologous genes between Arabidopsis and poplar (Street
et al. 2008).
Although comparative expression analysis is most
straightforward when compatible expression datasets are
Corresponding author: K. Vandepoele. Fax: +32 9 3313809; e-mail:
Plant, Cell and Environment (2012) 35, 1787–1798 doi: 10.1111/j.1365-3040.2012.02517.x
© 2012 Blackwell Publishing Ltd 1787
used that cover equivalent conditions for all species, only a
small fraction of all available data in different species can
be utilized in this approach (Tirosh et al. 2007).To overcome
these limitations, pioneering comparative transcriptomics
studies have shown that comparing co-expression, instead
of the raw expression values, provides a valid alternative to
identify gene modules (set of co-expressed genes poten-
tially sharing similar function and regulation) and study
their evolution (Stuart et al. 2003; Bergmann, Ihmels &
Barkai 2004). Stuart and colleagues developed a computa-
tional approach to identify conserved biological functions
in different species by looking for correlated patterns of
gene expression in microarrays from humans, fruit flies,
worms and yeast (Stuart et al. 2003). Similarly, the integra-
tion of genome-wide expression data was used to study the
modular architecture of regulatory programmes in six evo-
lutionary distant organisms (Bergmann et al. 2004).
In this manuscript, we give an overview of the different
steps to systematically compare microarray expression data
across species based on recent comparative transcriptomics
studies in plants. Apart from the retrieval, normalization
and annotation of microarray expression information, chal-
lenges related to the detection of co-expressed genes, the
accurate delineation of gene orthology and the integration
of expression networks and homology data are highlighted.
Two case studies are presented demonstrating how con-
served co-expression can be used to functionally annotate
genes and to discriminate between co-orthologs with
varying levels of expression conservation. Finally, we
discuss some properties of conserved expression modules in
plants and highlight some future applications.
Gene expression profiling of different samples reveals
whether genes are transcriptionally induced or repressed
as a reaction to a certain treatment, disease or at different
developmental stages. Consequently, it is a powerful tool
for target discovery, disease classification, pathway analy-
sis, and monitoring of biotic or abiotic responses. Among
different available microarray technologies, such as
Affymetrix, Agilent and Roche/NimbleGen, the Affyme-
trix GeneChip is one of the most popular platforms to
quantify steady-state transcript abundances (shortly, gene
expression). On Affymetrix oligonucleotide microarrays,
tens of thousands of probes, typically covering 25nt, are
attached to a solid surface. Other microarray platforms,
like Agilent, use only a few but longer probes to measure
expression of a specific gene (Hardiman 2004). After
sample preparation, the outcome of the probe-target
hybridization is quantified and intensity values of each cell
(feature) are saved in a CEL file for a specific experiment.
Apart from the expression values, standardized descrip-
tions of experimental conditions and protocols are stored
using the MIAME/Plant standard to facilitate data
sharing (Zimmermann et al. 2006). A detailed description
of various experimental parameters is essential if, in a
later stage, the identification of compatible experimental
conditions across species is required. Repositories like
Gene Expression Omnibus (GEO) (Barrett & Edgar
2006) or ArrayExpress (Parkinson et al. 2011) are public
microarray archives and provide thousands of expression
profiling studies (Fig. 1). All available microarray data for
a specific organism, mostly focusing on an individual plat-
form, are frequently combined to build large-scale expres-
sion compendia [see, e.g. PLEXdb (Wise et al. 2007)]
which summarize expression profiles in tens or hundreds
of different conditions (Fierro et al. 2008). For each experi-
ment, the CEL files are retrieved and subsequently
processed using a chip description file (CDF) in order to
obtain a raw intensity value per gene. A CDF file
describes probe locations and probeset groupings on the
chip. During microarray analysis, mostly performed using
algorithms such as MAS5 (Affymetrix proprietary
method) or RMA/GCRMA (Irizarry et al. 2003), intensity
values of individual probes are summarized for a probeset,
typically representing a specific locus, gene or transcript.
The final expression dataset is a matrix of genes (rows)
and conditions (columns), which is background corrected,
normalized and finally summarized (Quackenbush 2002).
In contrast to gene-based arrays, tiling arrays contain a
large number of probes that cover a complete chromo-
some or genome and can be used, apart from standard
expression profiling, for various applications including the
detection of novel transcripts, chromatin immunoprecipi-
tation of transcription factor protein–DNA interactions,
profiling of epigenetic modifications or the detection of
DNA polymorphisms (Gregory, Yazaki & Ecker 2008).
Although repeat sequences can interfere with the reliable
measurement of genome-wide expression, high-density
tiling arrays are independent of known gene annotations
and therefore provide an unbiased approach for different
profiling studies. This is in contrast with the GeneChip
platform, which measures the expression of a given
sequence (i.e. gene or transcript) using multiple probes
grouped in a probeset (see Supporting Information
Appendix S1).
According to a survey executed on November 2011, there
were 13 Affymetrix GeneChip microarray platforms pub-
licly available in the NCBI GEO database for different
plants (eight dicots and five monocots, see Fig. 1). The
number of CEL files available for these species varies a
lot, from only 20 for sugar cane (Sacharum officinarum)to
more than 7000 for Arabidopsis. Apart from a developmen-
tal plant expression atlas generated for Arabidopsis
(Schmid et al. 2005), large-scale expression compendia
have been constructed, using a variety of platforms, for other
species as well. Examples include barley (Hordeum vulgare)
(Druka et al. 2006), Medicago (Medicago truncatula) (Bene-
dito et al. 2008), rice (Jiao et al. 2009; Wang et al. 2010),
tobacco (Nicotiana tabacum) (Edwards et al. 2010) and
soybean (Glycine max) (Libault et al. 2010).Although many
plant expression studies integrated all available expression
data, in some cases condition-dependent or predefined
expression compendia focusing on specific developmental
1788 S. Movahedi et al.
© 2012 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 1787–1798
stages, tissues or stress conditions have been generated to
study specific gene functions (Usadel et al. 2009a; De Bodt
et al. 2010).Additional procedures can be applied to remove
low-quality samples or to remove samples that could gener-
ate biases within the final compendium (Table 1).The latter
is typically achieved by applying a statistical selection pro-
cedure to only select independent conditions or,reversely,by
first grouping similar conditions and only retaining a single
experiment as a representative for a set of related microar-
ray conditions (Movahedi, Van de Peer & Vandepoele 2011;
Mutwil et al. 2011). Although these selection procedures
allow for the detection of specific conditions providing new
expression information compared with the samples already
included in the compendium, the number of genes that can
be reliably measured through a specific microarray platform
also provides an important parameter when compiling
expression compendia. As for some species, the number of
genes that can be measured using a microarray differs
substantially from the number of annotated genes in
the genome (Mutwil et al. 2011); missing genes provide
an important drawback for many microarray-based
co-expression tools (see, e.g. Fig. 3b).
Figure 1. Overview of publicly available expression data for different plant species. White and black bars indicate for each species the
number of Affymetrix GeneChip microarray experiments (CEL files) in the NCBI Gene Expression Omnibus database and the number
of Transcriptome experiments from the NCBI Short Read Archive (SRA), respectively. Values below the species name indicate the
number of available CEL files and Transcriptome SRA experiments (November 2011), respectively.
Comparative transcriptomics in plants 1789
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Table 1. Overview of cross-species co-expression studies in plants
STARNET2 CoP PLaNet Maize rice ECC
Species H. sapiens (human), R. norvegicus (rat),
M. musculus (mouse), G. gallus
(chicken), D. rerio (zebrafish),
D. melanogaster (fly), C. elegans
(worm), S. cerevisiae (baker’s yeast),
A. thaliana (thale cress), O. sativa (rice)
A. thaliana, O. sativa, P. trichocarpa
(poplar), G. max (soybean), T. aestivum
(wheat), H. vulgare (barley), V. vinifera
(grape), Z. mays (maize)
A. thaliana, O. sativa,
M. truncatula M. sativa
(Medicago), P. trichocarpa,
G. max, T. aestivum, H. vulgare
Z. mays, O. sativa A. thaliana, O. sativa
Source of
microarray data
GEO GEO, ArrayExpress GEO, ArrayExpress GEO GEO
Sample bias filtering No No Yes No Yes
Filtering low-quality
No No Yes (deleted residuals) Yes (R/arrayQualityMetrics) No
Custom-made CDF + RMA MAS5 RMA RMA Custom-made CDF + RMA
PCC Cosine correlation coefficient Highest Reciprocal Rank (based
on PCC)
Clustering algorithm Gene-centric Confeito algorithm extracting highly
interconnected sub-graphs
Graph-based (NVN, HCCA) Graph-based (WGCNA, RMT) Gene-centric
Gene homology
NCBI HomoloGene Best hit orthologous gene (BLASTn) PFAM Reciprocal Best Hits OrthoMCL
Filtering homology links
between co-expression clusters
List of co-expressed genes in other
species based on individual query gene
Filtering and quantification
homology links between
co-expression clusters
Network alignment (mixed
co-expression topology and
homology; IsoRankN)
Filtering and quantification homology
links between co-expression clusters
Statistical model
No No Permutation test No Permutation test
Gene Ontology (GO) (terms linked to
AMIGO), Entrez ID, interaction data
(protein, DNA, RNA)
GO (Biological Process), KEGG
PATHWAYS, KaPPA-View 4, and
biological processes of GO
MapMan, phenotype GO, InterPro, KEGG,
GO, Reactome, MapMan
Hypergeometric distribution + Bonferroni
No Fisher exact
test + Benjamini–Hochberg
Fisher exact test Hypergeometric
distribution + Benjamini–Hochberg
Reference Jupiter et al. (2009) Ogata et al. (2010) Mutwil et al. (2011) Ficklin & Feltus (2011) Movahedi et al. (2011)
Algorithm available
No No Yes No No
http://vanburenlab.medicine. Not available Not available
Visualization Graphviz SVG Graphviz Cytoscape
Comment HeatSeeker cross-species analysis using
color maps
Meta-network of co-expression
Comparison of functional
enrichments between
co-expression clusters using
Integration data about tissue specificity,
protein evolution (Ka) and promoter
cis-regulatory elements
ECC includes the construction of a null model controlling for network connectivity or tissue-specific expression.
GEO, Gene Expression Omnibus; RMA, Robust Multichip Average; CDF, Chip Description File; MAS, Affymetrix Micorarray Suite; PCC, Pearson correlation coefficient; NVN, node vicinity network; HCCA, heuristic cluster chiseling
algorithm; WGCNA, weighted correlation network analysis; RMT, random matrix theory; SVG, Scalable Vector Graphics; ECC, expression context conservation.
1790 S. Movahedi et al.
© 2012 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 1787–1798
In order to compare genome-wide expression profiles
between different species, most studies apply a clustering
algorithm to search, based on a large-scale expression
compendium, for groups of highly co-expressed genes per
species (Fig. 2). The idea of clustering is to study groups of
genes, sharing similar expression patterns, instead of indi-
vidual ones. There are many different gene expression clus-
tering tools available and each has its own advantages and
disadvantages. Most clustering methods apply a similarity
or a distance measure together with other parameters such
as the number of clusters, the minimum/maximum cluster
size or a quality measure to construct gene co-expression
clusters (Xu & Wunsch 2005). Overall, it is not easy to do a
fair evaluation of how well an algorithm will perform on
typical expression datasets, and under which circums-
tances one algorithm should be preferred over another
(D’Haeseleer 2005; Usadel et al. 2009a).
Two of the most commonly used similarity measures for
gene expression data are Euclidean distance and Pearson
correlation coefficient (PCC). Other examples of measures
that have been applied in comparative plants’ co-expression
studies are cosine and Spearman’s correlation coefficient
(Table 1). To identify clusters of genes showing expression
similarity, very simple as well as complex graph-based clus-
tering algorithms have been developed. The most simple
methods rank, for a selected gene, all other genes based on
a similarity measure (e.g. descending PCC values) and then
select a predefined number of top best-ranked genes. Alter-
natively, gene selection can also be applied by retaining all
genes with a PCC value above a predefined threshold.
Figure 2. Workflow for cross-species expression network analysis. Asterisks above the gene-experiment matrix indicate potentially
redundant experiments which can cause a sample bias when computing gene expression similarities. In the co-expression, graph circles
denote genes while lines indicate expression similarity. Black co-expression lines indicate the first neighbours of the grey query gene
(gene-centric cluster) while grey co-expression lines indicate the indirect neighbours (extended node vicinity). Blue lines indicate
homologous gene relationships which, when superimposed on the co-expression networks, indicate conserved gene modules.
Comparative transcriptomics in plants 1791
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Mutual ranks, defined as the geometrical average of the
correlation ranks, are frequently applied to keep weak but
significant gene co-expression relationships which would
not be retained when applying a fixed absolute similarity
threshold. A derivative, the highest reciprocal rank (HRR),
considers the maximum rank for a pair of genes (Table 1).
The application of these rank-based gene selection criteria
is frequently used as a simple and fast substitute for more
complex clustering algorithms as they generate a set of
co-expressed genes for each query gene (i.e. gene-centric
clustering, see Fig. 2). In this case, the number of co-
expression clusters is close or equal to the number of genes
available in the expression dataset and clusters are poten-
tially overlapping on a genome-wide scale.
Apart from simple rank-based gene-centric clustering
approaches, more advanced algorithms apply graph theory
to find groups of genes showing similar expression profiles.
In general, a weighted graph of genes (nodes) is constructed
where each pair of genes is connected by an edge and the
edge weight is defined by the expression similarity between
the genes. Graph-based clustering tools try to identify
highly connected nodes (sub-graphs) in this expression
network representing gene expression clusters. Whereas
clique finders isolate fully connected sub-graphs, other tools
apply a variety of heuristic or statistical methods to find
gene clusters. This can be done by considering only the first
neighbours of a query (or seed) gene or all nodes within n
steps away from the query gene [node vicinity network
(NVN)]. Cluster Affinity Search Technique (CAST) (Ben-
Dor, Shamir & Yakhini 1999, Vandepoele et al., 2009), the
Confeito algorithm (Ogata et al. 2009), Weighted Gene
Co-expression Network Analysis (WGCNA) (Langfelder
& Horvath 2008), Random Matrix Theory (RMT) (Luo
et al. 2007) and Heuristic Cluster Chiseling Algorithm
(HCCA) (Mutwil et al. 2010) are examples of graph-based
algorithms which have been applied for defining gene
co-expression clusters in plants (Table 1).
A major objective in comparative expression studies is the
systematic comparison of gene clusters across species using
homologous or orthologous genes. Defining sequence-
based orthologs is a powerful approach to link expression
datasets across species (Table 1) and to identify genes with
conserved gene functions or conserved modules that par-
ticipate in similar biological processes (Stuart et al. 2003;
Bergmann et al. 2004; Lu et al. 2009). Although different
approaches are available to identify homologous and
orthologous genes (Koonin 2005), most of them start from
the output of a global all-against-all sequence similarity
search. Whereas NCBI HomoloGene defines homologous
genes in completely sequenced eukaryotic genomes (Sayers
et al. 2011), the PFAM database provides information about
conserved protein domains and families (Finn et al. 2010).
Although reciprocal best hits (RBHs) provide a practical
solution to identify orthologs between closely related
species, OrthoMCL and Inparanoid (Li, Stoeckert & Roos
2003, Ostlund et al. 2010) are more advanced methods to
construct orthologous groups across genomes because they
model, apart from orthology through RBH, also inparalogy
(gene duplication events post-dating speciation). Conse-
quently, species-specific gene family expansions are cor-
rectly represented in OrthoMCL orthologous groups while
RBH approaches only retain a single gene as ortholog
(excluding other inparalogs). In the latter case, it is possible
that erroneous conclusions about gene family expression
evolution are drawn, especially if the expression profiles of
the inparalogs (or co-orthologs) have diverged. Whereas
Inparanoid identifies orthologs and inparalogs in a pairwise
manner, OrthoMCL can delineate orthologous clusters
between multiple genomes in a single run. A detailed com-
parison of plant orthologs from multiple species revealed
that 70–90% of OrthoMCL families could be confirmed
by phylogenetic tree construction (Proost et al. 2009).
Although phylogeny-based orthology predictions are avail-
able in a number of plant comparative genomics resources
(Martinez 2011), sequence similarity clustering methods are
less computer intensive and more easily applicable.
However, simple sequence similarity approaches have a
higher risk of missing genes involved in complex many-to-
many orthology relationships between more distantly
related species (Kuzniar et al. 2008; Proost et al. 2009; Van
Bel et al. 2012). Reversely, protein domain-based methods
might assign false orthology relationships between multi-
domain protein coding genes that are only distantly related
based on the presence of single frequently occurring
domain (e.g. ankyrin repeat, WD40, F-box). Tools like
CoGe or PLAZA provide synteny information to delineate
putative orthologs (Lyons et al. 2008; Van Bel et al. 2012),
with the latter applying an ensemble approach to integrate
results from different methods when searching for ortholo-
gous genes (PLAZA Integrative Orthology approach).
So far, most comparative expression analyses have
combined gene expression clusters per species with homol-
ogy information to identify conserved gene expression
(Table 1). Examples in plants include Co-expressed biologi-
cal Processes (CoP) (Ogata et al. 2010), expression context
conservation (ECC) (Movahedi et al. 2011), Plant Network
(PLaNet) (Mutwil et al. 2011) and STARNET2 (Jupiter,
Chen & VanBuren 2009) (Table 1).Although the CoP data-
base simply provides a list of co-expressed genes in
the other species starting from an individual query gene, the
other tools include gene homology information to filter
the co-expression information from the different species
(see blue dashed lines in Fig. 2). Gene expression is typi-
cally compared between species in a pairwise manner and,
optionally, information about conserved genes in multiple
species is combined (Mutwil et al. 2011). Although this
approach provides a first glimpse on the co-expressed genes
that are conserved between different species (Humphry
et al. 2010), recently developed methods also apply statisti-
cal tests to verify if the number of shared orthologs between
two expression clusters is significant (Chikina & Troyan-
skaya 2011; Movahedi et al. 2011; Mutwil et al. 2011;
1792 S. Movahedi et al.
© 2012 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 1787–1798
Zarrineh et al. 2011). As most approaches use gene homol-
ogy or orthology information to connect co-expression
networks between different species, larger co-expression
clusters will logically also yield a higher number of shared
orthologs. Similarly, for genes involved in many-to-many
orthology relationships, the probability to have shared
orthologs between co-expression clusters is also higher
compared with small families with one-to-one orthology
relationships. As shown in Supporting Information Fig. S2,
the application of a statistical significance test can be used
to objectively define if, based on the gene co-expression
cluster sizes and homologous genes or families, the number
of shared orthologs is significantly higher than expected by
chance. In comparative studies where the homologous
genes from the different species can be classified using
one-to-one orthology, the hypergeometric distribution and
Pearson’s chi-square test have been used to estimate if the
number of shared orthologs is significant (Chikina & Troy-
anskaya 2011; Zarrineh et al. 2011). However, for species
with many multi-gene families like plants (Vandepoele &
Van de Peer 2005), the application of empirical significance
testing using a permutation test provides a more reliable
alternative as the probability of finding shared orthologs
between two expression clusters differs for genes belonging
to families with different sizes. To the best of our knowl-
edge, only PLaNet and ECC applied a statistical evalua-
tion taking into consideration different gene family sizes
(Table 1), the latter including different null models to
reliably estimate the significance levels of conserved
co-expression controlling for network properties such as
connectivity (i.e. the degree distribution of co-expressed
genes within the network) or tissue specificity (Movahedi
et al. 2011). As a consequence, these models correct for
specific expression breadth biases that might exist in
co-expression clusters for certain genes when performing
statistical evaluation.
To determine the most optimal conserved co-expression
module, the recently developed COMODO method uses a
cross-species co-clustering approach that simultaneously
evaluates the homology relations and the extension of
co-expression seed modules. Starting from seeds in each
species, these seed modules are gradually expanded (by
addition of co-expressed genes ranked using PCC similarity
information) in each of the species until a pair of modules is
found for which the number of shared orthologs is statisti-
cally optimal (Zarrineh et al. 2011). Although this approach
explores the two-dimensional parameter landscape (Sup-
porting Information Fig. S2) to find the best co-expression
module definition, it is still required to pre-specify a
co-expression stringency value for seed identification.
Complementary to two-step approaches which first
define expression clusters and then filters co-expressed
edges in the networks using gene homology information,
Ficklin & Feltus (2011) used a global network alignment
approach to combine the co-expression topology and
homology information and to delineate conserved
modules. Although this approach successfully identified
several conserved modules between rice and maize, the
applied method did not include a statistical evaluation of
the conserved sub-graphs.
To study the biological processes behind conserved
co-expression modules, different functional annotation
systems as well as experimental data have been used.
Although several studies relied on Gene Ontology (GO)
annotations to identify enriched gene functions within
conserved modules, information from KEGG pathways
(Kanehisa et al. 2010), Reactome (Tsesmetzis et al. 2008)
or MapMan (Usadel et al. 2009b) has also been exploited
(Table 1). Gene annotation enrichment analysis is a high-
throughput strategy that increases the likelihood for inves-
tigators to identify biological processes most pertinent to
their study, based on an underlying enrichment algorithm
(Huang da, Sherman & Lempicki 2009). The integration of
known protein–protein interactions, tissue-specific expres-
sion or phenotypic information from mutant lines provides
an additional level of experimental information that has
been used to characterize conserved modules (Ficklin &
Feltus 2011; Movahedi et al. 2011; Mutwil et al. 2011).
Graphviz and Cytoscape (Smoot et al. 2011) are fre-
quently applied software tools to graphically integrate
expression networks, homology information and functional
annotations (Table 1). Typically, genes are depicted by
nodes while different edge attributes are used to represent
expression similarity and homology information within and
between species (Fig. 3a). Although functional information
about individual genes can be displayed using node
attributes based on colour, shape or outline thickness, the
wealth of GO, KEGG or MapMan functional categories as
well as various experimental properties makes it difficult to
summarize all information in one single view. Although
filtering on specific gene functions or a GO biological
process provides a practical solution to reduce network
complexity, the construction of meta-networks (also
referred to as module or ontology networks) makes it pos-
sible to explore regulatory interactions between groups of
functionally related genes rather than between individual
genes (Table 1). Furthermore, meta-networks are an impor-
tant instrument to identify regulatory interactions and
cross-talk between different processes (Mutwil et al. 2011).
Although both STARNET2 and PLaNet host a website
where users can browse co-expression networks, only the
latter can be used to successfully generate cross-species
networks due to missing rice HomoloGene information in
STARNET2.Although Mohavedi et al. and Ficklin & Feltus
published several examples of conserved co-expression
modules between Arabidopsis–rice and rice–maize (Ficklin
& Feltus 2011; Movahedi et al. 2011), respectively, an
online resource to browse these conserved modules is
currently unavailable. The COP database displays small
co-expression networks for individual genes but reports
conserved orthologs between two co-expression clusters
from different species in a textual manner. Clearly, it
Comparative transcriptomics in plants 1793
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ECC (fraction of shared orthologs)
Arabidopsis - poplar (0.18)
Arabidopsis - rice (0.25)
Poplar - rice (0.27)
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remains an important challenge to provide an inter-
active web-browser application where, apart from the
co-expression networks from multiple species, different
functional annotations, phenotypes, protein–protein inter-
actions and complex orthology gene relationships can also
be displayed.
To demonstrate the power of comparative co-expression
methods to study gene functions across species, Figure 3a
displays the result of a comparative transcriptomics analysis
for the Arabidopsis gene ETG1 (AT2G40550). Whereas
this gene was previously described as a conserved E2F
target gene with unknown function (Vandepoele et al.
2005), recent experimental work revealed that it has an
essential role in sister chromatin cohesion during DNA
replication (Takahashi et al. 2010). To identify the biological
role of ETG1 and verify whether it is part of a conserved
co-expression module in plants, we first characterized the
gene’s co-expression context based on a general Arabidop-
sis expression compendium from CORNET (De Bodt et al.
2010). Retrieval of the 50 most co-expressed genes based on
the PCC yielded a set of genes showing a strong GO enrich-
ment towards ‘cellular DNA replication’ (90-fold enrich-
ment, P-value 1.33e-36). Enrichment analysis for known
plant cis-regulatory elements using ATCOECIS (Vandepo-
ele et al. 2009) yielded enrichment for the E2F binding site
TTTCCCGC (18-fold enrichment, P-value 1.41e-18), con-
firming that ETG1 is a putative E2F target gene. To
explore whether this functional enrichment is evolution-
arily conserved, we first searched for ETG1 orthologs
using the PLAZA 2.0 Integrative Orthology Viewer in
species for which microarray data are publicly available.
Whereas poplar, maize and rice have one ETG1 ortholog
(PT19G07260, ZM03G04050 and OS01G07260, respec-
tively), two copies were found in soybean (GM04G39990
and GM06G14860). Next, for each species a general expres-
sion compendium was compiled using Affymetrix experi-
ments from GEO and the top 50 co-expressed genes were
isolated in these organisms as well. Finally, the number of
shared orthologs between the different co-expression clus-
ters was determined and the resulting conserved modules
were delineated (Fig. 3a). Based on the ETG1 Arabidopsis
co-expression cluster, 9 and 13 orthologous genes were con-
served with the co-expression clusters for poplar and rice,
respectively. Whereas for both species the fraction of con-
served orthologs is much higher than expected by chance
(P-value <1e-5, see inset Fig. 3a), the functions of these
orthologs (MCM2-5, MCM7, RPA70B, RPA70D and
POLA3) as well as the ECC in both monocots and dicots
lend support for the conserved role of ETG1 in DNA rep-
lication. Querying the CoP database for ETG1 reports a
smaller number of co-expressed genes but confirms the
functional enrichment towards DNA replication as well as
the shared orthologs MCM3, MCM6 and POL3A between
Arabidopsis and rice.Whereas the PLaNet platform did not
directly confirm the biological role of ETG1 in DNA repli-
cation based on the Arabidopsis co-expression cluster, the
comparative analysis confirmed that up to 10 known DNA
replication genes showed conserved co-expression in other
plants. Examples included multiple replication factors, two
ribonucleotide reductases, PCNA, ORC2 and different
DNA polymerase subunits.
Based on the frequent nature of many-to-many gene
orthology relationships in plants, mediated by large-scale
duplication events (Van de Peer et al. 2009), comparative
transcriptomics also offers a practical solution to identify
functional homologs in multi-gene families (Chikina &
Troyanskaya 2011). Apart from detecting conserved gene
modules, the ECC method can also be applied to identify
orthologs and inparalogs with conserved co-expression
between different species for which large-scale expression
data are available. For a set of 21 ubiquitin-activating
enzyme homologs from seven species (Fig. 3b), the system-
atic examination of conserved co-expression between all
family members makes it possible to explore whether dupli-
cates show different conservation patterns. Application of
the ECC method using the 50 most co-expressed genes
revealed that, for those orthologs which have expression
data, in poplar, Medicago, soybean, Arabidopsis and maize
ECC patterns with orthologs from other species were dif-
ferent between inparalogs. This result reveals that for at
least five species, both co-orthologs with conserved and
Figure 3. Plant orthologs with conserved co-expression. (a) Co-expression context analysis for the Arabidopsis ETG1 gene and its
orthologs in poplar and rice (based on PLAZA 2.0 annotations). Grey edges represent co-expression links between ETG1 (query gene)
and its top 50 co-expressed genes, weighted by the PCC value. Red dashed edges denote protein–protein interactions, black add-ons are
used to indicate genes with known GO annotations for cell cycle and/or DNA replication, and blue edges depict orthology. The inset
displays a histogram of the ECC background model (expected number of shared orthologs for random clusters with equal sizes as
real co-expression clusters) while the arrows indicate the ECC scores for the different ETG1 co-expression context comparisons.
(b) Systematic evaluation of orthology and conserved co-expression using the ECC method for a set of 21 homologs (encoding
ubiquitin-activating enzyme E1) from Arabidopsis, grape, Medicago, maize, poplar, rice and soybean (AT, VV, MT, ZM, PT, OS and GM
prefixes, respectively). Groups of inparalogous genes are indicated using dashed vertical lines. Upper-left triangles denote the
sequence-based orthologous relationship between the genes, with a darker shade of blue indicating a higher number of evidence types
reported by the PLAZA 2.0 Integrative Orthology approach. The lower-right yellow triangles denote gene pairs with significant ECC
scores (P-value < 0.05), white triangles represent gene pairs lacking a significant number of hared orthologs (P-value 0.05) and darker
shades of yellow indicate a higher fraction of shared orthologs. Arced sections denote missing expression data for at least one of the
genes. ECC scores are only computed between genes from different species. ECC, expression context conservation.
Comparative transcriptomics in plants 1795
© 2012 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 1787–1798
non-conserved co-expression contexts exist, making the
transfer of biological information between different species
Hypothesis-driven gene discovery remains one of the
most promising applications for co-expression networks.
Whereas this principle is not new in plant genomics (Usadel
et al. 2009a), the analysis of expression networks between
more distantly related species exploits the assumption that
predicted gene-function associations that occur by chance
within one organism will not be conserved in a multi-
species dataset. Indeed, several plant studies identified
conserved expression modules related to photosynthesis,
translation, cell cycle and DNA metabolism, both in dicots
and monocots (Ficklin & Feltus 2011; Movahedi et al. 2011;
Mutwil et al. 2011). As a consequence, the analysis of con-
served modules with enriched gene functions and the com-
parison of gene sets with enriched phenotypes provide an
invaluable approach for biological gene discovery in model
species and to translate new gene functions to species with
agricultural or economical value. Reversely, the analysis of
orthologous genes lacking expression conservation might
reveal biological adaptations linking genotype to pheno-
type (Tirosh et al. 2007). Based on the statistical evaluation
of genes lacking shared orthologs between Arabidopsis and
rice genes, Movahedi and co-workers reported that non-
conserved ECC genes involved in stress response and signal
transduction could provide a connection between regula-
tory evolution and environmental adaptations (Movahedi
et al. 2011).
The integration of new experiments describing specific
transcriptional responses or tissue-specific expression will
provide, apart from GO annotations, an important comple-
mentary source of functional information to annotate
homologs and to transfer biological knowledge between
species based on conserved gene modules. Nevertheless,
this would require that, for example using ontology-based
experimental annotations (Jaiswal et al. 2005; De Bodt et al.
2010), similar conditions in different species could easily be
identified within public databases covering thousands of
profiling experiments. The recently developed Expressolog
Tree Viewer, part of the Bio-Array Resource for Plant
Biology website (, demonstrates
how in several cases equivalent conditions between differ-
ent plants can be identified and how direct comparisons
of expression profiles between homologous genes can be
used to identify (co-)orthologs showing conserved spatial–
temporal expression. Nevertheless, as divergence time and
morphological differences between species increase (e.g.
between monocotyledonous and eudicotyledonous plants),
finding equivalent tissues becomes challenging. Conse-
quently, and in contrast to co-expression comparisons
(Fig. 3b), this set-up only allows for a limited number of
conditions that can directly be compared across homologs
of different species.
The application of next-generation sequencing to quan-
tify plant transcriptomes (RNA-Seq) will generate new
opportunities to study and compare expression profiles
between species (Fig. 1). For example, detailed comparisons
of different alternative transcripts within a co-expression
network context will provide important information about
the biological processes different splicing variants are
involved in. Furthermore, studying alternative transcript
expression levels within a comparative framework will
generate new insights into the evolution and functional
significance of alternative splicing in plants. However, the
development and application of robust data processing and
normalization methods will be essential in order to combine
RNA-Seq experiments with varying sequencing depths into
uniform and comparable expression compendia (Tarazona
et al. 2011).
In conclusion, the rapid accumulation of genome-wide
data describing both plant genome sequences and a variety
of functional properties will require the continuous devel-
opment of systems biology approaches as well as user-
friendly databases to extract biological knowledge and
exchange information between experimental and computa-
tional plant biologists.
We thank Annick Bleys for help in preparing the manu-
script and Yves Van de Peer for general support. K.S.H. is
indebted to the Agency for Innovation by Science and Tech-
nology (IWT) in Flanders for a pre-doctoral fellowship.
K.V. acknowledges the support of Ghent University (Mul-
tidisciplinary Research Partnership ‘Bioinformatics: from
nucleotides to networks’). This project is funded by the
Research Foundation–Flanders and the Belgian Federal
Science Policy Office: IUAP P6/25 (BioMaGNet).
Barrett T. & Edgar R. (2006) Gene expression omnibus: microar-
ray data storage, submission, retrieval, and analysis. Methods in
Enzymology 411, 352–369.
Ben-Dor A., Shamir R. & Yakhini Z. (1999) Clustering gene
expression patterns. Journal of Computational Biology 6, 281–
Benedito V.A., Torres-Jerez I., Murray J.D., et al. (2008) A gene
expression atlas of the model legume Medicago truncatula. The
Plant Journal 55, 504–513.
Bergmann S., Ihmels J. & Barkai N. (2004) Similarities and differ-
ences in genome-wide expression data of six organisms. PLoS
Biology 2, E9.
Chikina M.D. & Troyanskaya O.G. (2011) Accurate quantification
of functional analogy among close homologs. PLoS Computa-
tional Biology 7, e1001074.
D’Haeseleer P. (2005) How does gene expression clustering work?
Nature Biotechnology 23, 1499–1501.
De Bodt S., Carvajal D., Hollunder J., Van den Cruyce J., Movahedi
S. & Inze D. (2010) CORNET: a user-friendly tool for data
mining and integration. Plant Physiology 152, 1167–1179.
Druka A., Muehlbauer G., Druka I., et al. (2006) An atlas of gene
expression from seed to seed through barley development. Func-
tional and Integrative Genomics 6, 202–211.
1796 S. Movahedi et al.
© 2012 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 1787–1798
Edwards K.D., Bombarely A., Story G.W., Allen F., Mueller
L.A., Coates S.A. & Jones L. (2010) TobEA: an atlas of tobacco
gene expression from seed to senescence. BMC Genomics 11,
Ficklin S.P. & Feltus F.A. (2011) Gene coexpression network align-
ment and conservation of gene modules between two grass
species: maize and rice. Plant Physiology 156, 1244–1256.
Fierro A.C., Vandenbussche F., Engelen K., Van de Peer Y. &
Marchal K. (2008) Meta analysis of gene expression data within
and across species. Current Genomics 9, 525–534.
Finn R.D., Mistry J., Tate J., et al. (2010) The Pfam protein families
database. Nucleic Acids Research 38, D211–D222.
Gong Q., Li P., Ma S., Indu Rupassara S. & Bohnert H.J. (2005)
Salinity stress adaptation competence in the extremophile Thel-
lungiella halophila in comparison with its relative Arabidopsis
thaliana. The Plant Journal 44, 826–839.
Gregory B.D., Yazaki J. & Ecker J.R. (2008) Utilizing tiling
microarrays for whole-genome analysis in plants. The Plant
Journal 53, 636–644.
Hammond J.P., Broadley M.R., Craigon D.J., Higgins J., Emmerson
Z.F., Townsend H.J., White P.J. & May S.T. (2005) Using genomic
DNA-based probe-selection to improve the sensitivity of high-
density oligonucleotide arrays when applied to heterologous
species. Plant Methods 1, 10.
Hardiman G. (2004) Microarray platforms comparisons and con-
trasts. Pharmacogenomics 5, 487–502.
Hardison R.C. (2003) Comparative genomics. PLoS Biology 1,
Huang da W., Sherman B.T. & Lempicki R.A. (2009) Bioinformat-
ics enrichment tools: paths toward the comprehensive functional
analysis of large gene lists. Nucleic Acids Research 37, 1–13.
Humphry M., Bednarek P., Kemmerling B., et al. (2010) A regulon
conserved in monocot and dicot plants defines a functional
module in antifungal plant immunity. Proceedings of the
National Academy of Sciences of the United States of America
107, 21896–21901.
Irizarry R.A., Hobbs B., Collin F., Beazer-Barclay Y.D., Antonellis
K.J., Scherf U. & Speed T.P. (2003) Exploration, normalization,
and summaries of high density oligonucleotide array probe level
data. Biostatistics 4, 249–264.
Jaiswal P., Avraham S., Ilic K., et al. (2005) Plant Ontology (PO): a
controlled vocabulary of plant structures and growth stages.
Comparative and Functional Genomics 6, 388–397.
Jiao Y., Ma L., Strickland E. & Deng X.W. (2005) Conservation and
divergence of light-regulated genome expression patterns during
seedling development in rice and Arabidopsis. The Plant Cell 17,
Jiao Y., Tausta S.L., Gandotra N., et al. (2009) A transcriptome atlas
of rice cell types uncovers cellular, functional and developmental
hierarchies. Nature Genetics 41, 258–263.
Jupiter D., Chen H. & VanBuren V. (2009) STARNET 2: a web-
based tool for accelerating discovery of gene regulatory net-
works using microarray co-expression data. BMC Bioinformatics
10, 332.
Kanehisa M., Goto S., Furumichi M., Tanabe M. & Hirakawa M.
(2010) KEGG for representation and analysis of molecular net-
works involving diseases and drugs. Nucleic Acids Research 38,
Koonin E.V. (2005) Orthologs, paralogs, and evolutionary genom-
ics. Annual Review of Genetics 39, 309–338.
Kuzniar A., van Ham R.C., Pongor S. & Leunissen J.A. (2008) The
quest for orthologs: finding the corresponding gene across
genomes. Trends in Genetics 24, 539–551.
Langfelder P. & Horvath S. (2008) WGCNA: an R package for
weighted correlation network analysis. BMC Bioinformatics 9,
Li L., Stoeckert C.J., Jr & Roos D.S. (2003) OrthoMCL: identifica-
tion of ortholog groups for eukaryotic genomes. Genome
Research 13, 2178–2189.
Libault M., Farmer A., Joshi T., Takahashi K., Langley R.J., Frank-
lin L.D., He J., Xu D., May G. & Stacey G. (2010) An integrated
transcriptome atlas of the crop model Glycine max, and its use in
comparative analyses in plants. The Plant Journal 63, 86–99.
Lu Y., Huggins P. & Bar-Joseph Z. (2009) Cross species analysis of
microarray expression data. Bioinformatics 25, 1476–1483.
Luo F., Yang Y., Zhong J., Gao H., Khan L., Thompson D.K. &
Zhou J. (2007) Constructing gene co-expression networks and
predicting functions of unknown genes by random matrix
theory. BMC Bioinformatics 8, 299.
Lyons E., Pedersen B., Kane J., et al. (2008) Finding and comparing
syntenic regions among Arabidopsis and the outgroups papaya,
poplar, and grape: CoGe with rosids. Plant Physiology 148, 1772–
Ma L., Chen C., Liu X., et al. (2005) A microarray analysis of the
rice transcriptome and its comparison to Arabidopsis. Genome
Research 15, 1274–1283.
Martinez M. (2011) Plant protein-coding gene families: emerging
bioinformatics approaches. Trends in Plant Science 16, 558–
Movahedi S., Van de Peer Y. & Vandepoele K. (2011) Compara-
tive network analysis reveals that tissue specificity and gene
function are important factors influencing the mode of expres-
sion evolution in Arabidopsis and rice. Plant Physiology 156,
Mustroph A., Lee S.C., Oosumi T., Zanetti M.E., Yang H., Ma K.,
Yaghoubi-Masihi A., Fukao T. & Bailey-Serres J. (2010) Cross-
kingdom comparison of transcriptomic adjustments to low-
oxygen stress highlights conserved and plant-specific responses.
Plant Physiology 152, 1484–1500.
Mutwil M., Usadel B., Schutte M., Loraine A., Ebenhoh O. &
Persson S. (2010) Assembly of an interactive correlation
network for the Arabidopsis genome using a novel heuristic
clustering algorithm. Plant Physiology 152, 29–43.
Mutwil M., Klie S., Tohge T., Giorgi F.M., Wilkins O., Campbell
M.M., Fernie A.R., Usadel B., Nikoloski Z. & Persson S. (2011)
PlaNet: combined sequence and expression comparisons across
plant networks derived from seven species. The Plant Cell 23,
Ogata Y., Sakurai N., Suzuki H., Aoki K., Saito K. & Shibata D.
(2009) The prediction of local modular structures in a
co-expression network based on gene expression datasets.
Genome Inform 23, 117–127.
Ogata Y., Suzuki H., Sakurai N. & Shibata D. (2010) CoP: a data-
base for characterizing co-expressed gene modules with biologi-
cal information in plants. Bioinformatics 26, 1267–1268.
Ostlund G., Schmitt T., Forslund K., Kostler T., Messina D.N.,
Roopra S., Frings O. & Sonnhammer E.L. (2010) InParanoid 7:
new algorithms and tools for eukaryotic orthology analysis.
Nucleic Acids Research 38,
Parkinson H., Sarkans U., Kolesnikov N., et al. (2011) ArrayEx-
press update an archive of microarray and high-throughput
sequencing-based functional genomics experiments. Nucleic
Acids Research 39, D1002–D1004.
Proost S., Van Bel M., Sterck L., Billiau K., Van Parys T., Van de
Peer Y. & Vandepoele K. (2009) PLAZA: a comparative genom-
ics resource to study gene and genome evolution in plants. The
Plant Cell 21, 3718–3731.
Quackenbush J. (2002) Microarray data normalization and trans-
formation. Nature Genetics 32 (Suppl), 496–501.
Sayers E.W., Barrett T., Benson D.A., et al. (2011) Database
resources of the National Center for Biotechnology Information.
Nucleic Acids Research 39, D38–D51.
Comparative transcriptomics in plants 1797
© 2012 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 1787–1798
Schmid M., Davison T.S., Henz S.R., Pape U.J., Demar M., Vingron
M., Scholkopf B., Weigel D. & Lohmann J.U. (2005) A gene
expression map of Arabidopsis thaliana development. Nature
Genetics 37, 501–506.
Smoot M.E., Ono K., Ruscheinski J., Wang P.L. & Ideker T. (2011)
Cytoscape 2.8: new features for data integration and network
visualization. Bioinformatics 27, 431–432.
Street N.R., Sjodin A., Bylesjo M., Gustafsson P., Trygg J. &
Jansson S. (2008) A cross-species transcriptomics approach to
identify genes involved in leaf development. BMC Genomics
9, 589.
Stuart J.M., Segal E., Koller D. & Kim S.K. (2003) A gene-
coexpression network for global discovery of conserved genetic
modules. Science 302, 249–255.
Taji T., Seki M., Satou M., Sakurai T., Kobayashi M., Ishiyama K.,
Narusaka Y., Narusaka M., Zhu J.K. & Shinozaki K. (2004)
Comparative genomics in salt tolerance between Arabidopsis
and aRabidopsis-related halophyte salt cress using Arabidopsis
microarray. Plant Physiology 135, 1697–1709.
Takahashi N., Quimbaya M., Schubert V., Lammens T., Vandepoele
K., Schubert I., Matsui M., Inze D., Berx G. & De Veylder L.
(2010) The MCM-binding protein ETG1 aids sister chromatid
cohesion required for postreplicative homologous recombina-
tion repair. PLoS Genetics 6, e1000817.
Tarazona S., Garcia-Alcalde F., Dopazo J., Ferrer A. & Conesa A.
(2011) Differential expression in RNA-seq: a matter of depth.
Genome Research 21, 2213–2223.
Tirosh I., Bilu Y. & Barkai N. (2007) Comparative biology: beyond
sequence analysis. Current Opinion in Biotechnology 18, 371–
Tsesmetzis N., Couchman M., Higgins J., et al. (2008) Arabidopsis
reactome: a foundation knowledgebase for plant systems
biology. The Plant Cell 20, 1426–1436.
Usadel B., Obayashi T., Mutwil M., Giorgi F.M., Bassel G.W., Tan-
imoto M., Chow A., Steinhauser D., Persson S. & Provart N.J.
(2009a) Co-expression tools for plant biology: opportunities for
hypothesis generation and caveats. Plant, Cell & Environment
32, 1633–1651.
Usadel B., Poree F., Nagel A., Lohse M., Czedik-Eysenberg A. &
Stitt M. (2009b) A guide to using MapMan to visualize and
compare Omics data in plants: a case study in the crop species,
Maize. Plant, Cell & Environment 32, 1211–1229.
Van Bel M., Proost S., Wischnitzki E., Movahedi S., Scheerlinck C.,
Van de Peer Y. & Vandepoele K. (2012) Dissecting plant
genomes with the PLAZA comparative genomics platform.
Plant Physiology 158, 590–600.
Van de Peer Y., Fawcett J.A., Proost S., Sterck L. & Vandepoele K.
(2009) The flowering world: a tale of duplications. Trends in
Plant Science 14, 680–688.
Vandenbroucke K., Robbens S., Vandepoele K., Inze D., Van de
Peer Y. & Van Breusegem F. (2008) Hydrogen peroxide-induced
gene expression across kingdoms: a comparative analysis.
Molecular Biology and Evolution 25, 507–516.
Vandepoele K. & Van de Peer Y. (2005) Exploring the plant tran-
scriptome through phylogenetic profiling. Plant Physiology 137,
Vandepoele K., Vlieghe K., Florquin K., Hennig L., Beemster G.T.,
Gruissem W., Van de Peer Y., Inze D. & De Veylder L. (2005)
Genome-wide identification of potential plant E2F target genes.
Plant Physiology 139, 316–328.
Vandepoele K., Quimbaya M., Casneuf T., De Veylder L. & Van de
Peer Y. (2009) Unraveling transcriptional control in Arabidopsis
using cis-regulatory elements and coexpression networks. Plant
Physiology 150, 535–546.
Wang L., Xie W., Chen Y., et al. (2010) A dynamic gene expression
atlas covering the entire life cycle of rice. The Plant Journal 61,
Weber M., Harada E., Vess C., Roepenack-Lahaye E. & Clemens S.
(2004) Comparative microarray analysis of Arabidopsis thaliana
and Arabidopsis halleri roots identifies nicotianamine synthase, a
ZIP transporter and other genes as potential metal hyperaccu-
mulation factors. The Plant Journal 37, 269–281.
Wise R.P., Caldo R.A., Hong L., Shen L., Cannon E. & Dickerson
J.A. (2007) BarleyBase/PLEXdb. Methods in Molecular Biology
406, 347–363.
Xu R. & Wunsch D., 2nd (2005) Survey of clustering algorithms.
IEEE Transactions on Neural Networks 16, 645–678.
Zarrineh P., Fierro A.C., Sanchez-Rodriguez A., De Moor B.,
Engelen K. & Marchal K. (2011) COMODO: an adaptive coclus-
tering strategy to identify conserved coexpression modules
between organisms. Nucleic Acids Research 39, e41.
Zimmermann P., Schildknecht B., Craigon D., et al. (2006)
MIAME/plant adding value to plant microarrray experiments.
Plant Methods 2, 1.
Received 27 December 2011; accepted for publication 5 April 2012
Additional Supporting Information may be found in the
online version of this article:
Figure S1. Probeset definitions at the gene and transcript
Figure S2. Significance testing of the number of shared
orthologs during expression context conservation analysis.
Appendix S1. Note on the mapping of Affymetrix probes
to gene models.
Please note: Wiley-Blackwell are not responsible for the
content or functionality of any supporting materials sup-
plied by the authors. Any queries (other than missing mate-
rial) should be directed to the corresponding author for the
1798 S. Movahedi et al.
© 2012 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 1787–1798
    • "These analyses mostly rely on gene and protein sequence information; however the increasing number of gene expression data in many different species is opening up new perspectives. Cross-species comparison of co-expression networks is a promising approach to understand the interplay between regulatory function and evolution (Movahedi et al., 2012; Hansen et al., 2014). There are several advantages of cross-species network comparisons. "
    [Show abstract] [Hide abstract] ABSTRACT: Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavour that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.
    Article · Apr 2016
    • "So far, most reports on functional gene discovery via coexpression analysis in plants described the use of transcriptome data for a single species. Finding conserved co-expression patterns between orthologs across related plant species can provide a highly relevant list of candidate genes that potentially share similar functions and act in the same pathways (Hirai et al., 2005; Obayashi et al., 2007; Usadel et al., 2009; Mutwil et al., 2010 Mutwil et al., , 2011 Movahedi et al., 2012; Hansen et al., 2014; Tohge et al., 2014). An example of using such a strategy was recently described for tomato and potato, where comparative co-expression information was utilized for constructing a coexpression network, leading to the discovery of a metabolic gene cluster related to the steroidal glycoalkaloids (SGAs) pathway (Itkin et al., 2013). "
    [Show abstract] [Hide abstract] ABSTRACT: Motivation: Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Several efficient co-expression-based tools have been commonly used in plant research but most of these pipelines are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks. Results: We introduce CoExpNetViz, a computational tool that uses a set of query or "bait" genes as an input (chosen by the user) and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i) for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii) non-bait (or target) genes are grouped based on cross-species orthology, and (iii) output files are generated and results can be visualized as network graphs in Cytoscape. Availability: The CoExpNetViz tool is freely available both as a PHP web server (link: (implemented in C++) and as a Cytoscape plugin (implemented in Java). Both versions of the CoExpNetViz tool support LINUX and Windows platforms.
    Full-text · Article · Jan 2016
    • "In recent decades, microarray-based datasets have been massively produced to monitor gene expression levels in parallel with numerous experimental treatments [11]. This high-throughput detection of transcript quantity facilitates the comparative expression analysis by combining multiple microarray expression data among different samples and even different species [12] . Due to the abundance of expression datasets generated by microarray platforms for plants, a plenty of databases and resources have promptly collected gene expression data that are publicly accessible. "
    [Show abstract] [Hide abstract] ABSTRACT: In general, the expression of gene alters conditionally to catalyze a specific metabolic pathway. Microarray-based datasets have been massively produced to monitor gene expression levels in parallel with numerous experimental treatments. Although several studies facilitated the linkage of gene expression data and metabolic pathways, none of them are amassed for plants. Moreover, advanced analysis such as pathways enrichment or how genes express under different conditions is not rendered. Therefore, EXPath was developed to not only comprehensively congregate the public microarray expression data from over 1000 samples in biotic stress, abiotic stress, and hormone secretion but also allow the usage of this abundant resource for coexpression analysis and differentially expression genes (DEGs) identification, finally inferring the enriched KEGG pathways and gene ontology (GO) terms of three model plants: Arabidopsis thaliana, Oryza sativa, and Zea mays. Users can access the gene expression patterns of interest under various conditions via five main functions (Gene Search, Pathway Search, DEGs Search, Pathways/GO Enrichment, and Coexpression analysis) in EXPath, which are presented by a user-friendly interface and valuable for further research. In conclusion, EXPath, freely available at, is a database resource that collects and utilizes gene expression profiles derived from microarray platforms under various conditions to infer metabolic pathways for plants.
    Full-text · Article · Feb 2015
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