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Plant Physiology 132:1162-1176 (2003) © 2003 American Society of Plant Biologists Computational Approaches to Identify Promoters and cis-Regulatory Elements in Plant Genomes1Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology, Ghent University, B–9000 Gent, Belgium (S.R., K.F., Y.V.d.P.); Laboratoire de Génétique et Physiologie du Développement, Equipe bioinformatique, Centre National de la Recherche Scientifique, Parc Scientifique de Luminy, F-13288 Marseille Cedex 9, France (M.L.); Department of Electrical Engineering (Electronics, Systems, Automatisation and Technology-Signals, Identification, System Theory, and Automation), Katholieke Universiteit Leuven, B–3001 Heverlee, Belgium (K.M.); and Laboratoire Associé de l'Institut National de la Recherche Agronomique (France), Ghent University, K.L. Ledeganckstraat 35, B–9000 Gent, Belgium (P.R.)
The identification of promoters and their regulatory elements is one of the major challenges in bioinformatics and integrates comparative, structural, and functional genomics. Many different approaches have been developed to detect conserved motifs in a set of genes that are either coregulated or orthologous. However, although recent approaches seem promising, in general, unambiguous identification of regulatory elements is not straightforward. The delineation of promoters is even harder, due to its complex nature, and in silico promoter prediction is still in its infancy. Here, we review the different approaches that have been developed for identifying promoters and their regulatory elements. We discuss the detection of cis-acting regulatory elements using word-counting or probabilistic methods (so-called "search by signal" methods) and the delineation of promoters by considering both sequence content and structural features ("search by content" methods). As an example of search by content, we explored in greater detail the association of promoters with CpG islands. However, due to differences in sequence content, the parameters used to detect CpG islands in humans and other vertebrates cannot be used for plants. Therefore, a preliminary attempt was made to define parameters that could possibly define CpG and CpNpG islands in Arabidopsis, by exploring the compositional landscape around the transcriptional start site. To this end, a data set of more than 5,000 gene sequences was built, including the promoter region, the 5'-untranslated region, and the first introns and coding exons. Preliminary analysis shows that promoter location based on the detection of potential CpG/CpNpG islands in the Arabidopsis genome is not straightforward. Nevertheless, because the landscape of CpG/CpNpG islands differs considerably between promoters and introns on the one side and exons (whether coding or not) on the other, more sophisticated approaches can probably be developed for the successful detection of "putative" CpG and CpNpG islands in plants.
Arabidopsis, and probably most plants, encode an exceptionally large number of DNA-binding proteins, potentially acting as transcription factors (TFs). In fact, more than 3,000 genes have been anticipated to be involved in transcription, more than one-half of which were expected to encode TFs (Arabidopsis Genome Initiative, 2000  ), corresponding to more than 5% of the Arabidopsis genes, and
approximately twice the ratio observed for yeast and animal genomes
(Riechmann et al., 2000).
These TFs bind to the DNA on specific cis-acting regulatory elements (CAREs)
and orchestrate the initiation of transcription, which is one of the most
important control points in the regulation of gene expression. CAREs are short
conserved motifs of five up to 20 nucleotides usually found in the vicinity of
the 5' end of genes in what is called the promoter. The promoter
sequence is usually located upstream from the transcription start site (TSS),
but regulatory elements can also be located downstream, for example, in the
first intron of the gene itself (Zhang et
al., 1994; Gidekel et al.,
1996; de Boer et al.,
1999; Dorsett,
1999). The promoter can roughly be divided in two parts: a
proximal part, referred to as the core, and a distal part. The proximal part
is believed to be responsible for correctly assembling the RNA polymerase II
complex at the right position and for directing a basal level of transcription
(Nikolov et al., 1996;
Nikolov and Burley, 1997;
Berk, 1999). It is mediated by
elements, such as TATA and Initiator boxes through the binding of the TATA
box-binding protein, and other general TFs specific for the RNA polymerase II
(Featherstone, 2002). The
distal part of the promoter is believed to contain those elements that
regulate the spatio-temporal expression
(Tjian and Maniatis, 1994;
Fessele et al., 2002). How far
upstream (or downstream) such a distal part reaches is not defined. In
addition to the proximal and distal parts, somewhat isolated, regulatory
regions have also been described, mainly in animals, that contain enhancer
and/or repressors elements (Barton et al.,
1997; Bagga et al.,
2000). The latter elements can be found from a few kilobase pairs
upstream from the TSS, in the introns, or even at the 3' side of the
genes they regulate (Larkin et al.,
1993; Wasserman et al.,
2000). Lastly, eukaryotic genomes can be organized into domains of
transcriptional activity or transcriptional silencing, encompassing one or
more genes (Oki and Kamakaka,
2002).
A promoter region, as described above, presents a rather linear view of the
promoter. In reality, a supplementary layer of complexity is added by bringing
the TFs together on a promoter, by adopting a three-dimensional configuration,
enabling the interaction with other parts to activate the basal transcription
machinery (Fig. 1;
Buratowski; 1997
The three-way connection between methylation, gene activity, and chromatin
structure has been known for almost two decades. DNA methylation has been
shown to repress transcription initiation by interfering directly with the
binding of transcriptional activators or indirectly by binding proteins with
affinity for methylated DNA (Weber et
al., 1990
Much attention has been paid to investigate the modular structure of
regulatory regions that control the transcription of eukaryotic genes
(Dynan, 1989 All of these different levels of complexity have great repercussions on the in silico identification of binding sites and promoters. Here, we review current approaches (summarized in Fig. 2) to identify promoters and their regulatory elements.
Promoter Prediction
Unlike gene prediction (Mathé
et al., 2002
In 1997, Fickett and Hatzigeorgiou
(1997
On the one hand, sequence-based algorithms aim at identifying regulatory
regions and promoters based on their sequence composition compared with that
of non-promoters. Among others, Scherf et al.
(2000
PromoterInspector (Scherf et al.,
2000
McPromoter (Ohler et al.,
1999
Although the prediction tools hitherto developed can produce acceptable
results for certain species, none of them have been trained and adapted for
plants. For example, McPromoter is trained especially to analyze data of
fruitfly (Drosophila melanogaster) and has been used in the Genome
Annotation Assessment project (Reese et
al., 2000
A structural feature that has proven useful in the detection of promoters
in the human genome are the so-called CpG islands, i.e. regions that are rich
in CpGs, which are important because of their strong link with gene
regulation. In general, CpG-rich regions are methylated and are associated
with inactive DNA often linked to heterochromatin, gene silencing, and
pathogen control (Jeddeloh et al.,
1998
Although the functional significance of methylation appears to be similar
in humans and plants (Hershkovitz et al.,
1990
CpG islands are characterized by a locally increased GC percentage (GC%)
compared with local averages and by the presence of CpGs (and CpNpGs in
plants). The CpG dinucleotide, usually methylated at the fifth position on the
cytosine ring, is counter-selected and found much less frequently than
expected based on mononucleotide frequencies, for example, 5-fold lower in
genomes of vertebrates. This depletion is believed to result from accidental
mutations by deamination of 5-methylcytosine to thymine
(Sved and Bird, 1990
The original pragmatic definition of a CpG island in human sequences
considers a GC% higher than 50 and a ratio between observed and expected (o/e)
occurrence of CG dinucleotides of 0.6 over a window of 200 bp
(Gardiner-Garden and Frommer,
1987 A program in Perl was written that computes the GC content and the o/e ratios of CpG and CpNpG compared with local characteristics over a certain window size. By applying this program to the ARAPROM data set to extract potential CpG/CpNpG islands, we tested the effect of setting the cut-off values for the GC content and the o/e CpG/CpNpG ratios at different levels (39% to 52% with a stepwise increase of 0.5% for the GC content; 0.6% to 2.0% for the o/e CpG and CpNpG ratios with a stepwise increase of 0.1. The results of this analysis with a window size of 200 bp are shown graphically for CpG and CpNpG islands (Figs. 3 and 4, respectively). The first observation is that no CpG island is detected with the cut-off parameters tuned for humans, except for a few in coding exons. Both parameters, CG% and o/e CpG, appear to influence strongly the number of CpG islands detected and, depending on the position in the genome, to affect differently the number of CpGs found. That number found in the "promoter" region sharply increases while the GC% cut-off decreases (Fig. 3). In contrast, for coding exons, the landscape resembles more a plateau, with many CpG islands found already at much higher GC% values. Only at the lowest GC% values, more CpG islands are predicted in the "promoter" region than in the coding exons. In UTR exons, which show a landscape similar to that of coding exons, fewer CpG islands are found and introns, which show a landscape more similar to that of the "promoter" region, show the lowest number of CpG islands.
Regarding the CpNpG landscape (Fig. 4), the major observation is that the overall number of islands lies well below that of the CpG islands. In addition, the same differences in landscape hold, as observed for CpG islands between promoter (and introns), on the one hand, and coding exons (and UTR exons), on the other hand. Nevertheless, a striking difference is that for CpNpGs, the o/e CpNpG threshold has to be very low for those islands to be detected. In terms of number of genes associated with CpG/CpNpG islands, different parameter settings lead to very different figures (Table II).
This preliminary in silico analysis shows that prediction of promoter location based on the detection of potential CpG/CpNpG islands in the Arabidopsis genome is not straightforward. Nevertheless, because the landscape of CpG/CpNpG islands differs considerably between promoters and introns on the one side and exons (whether coding or not) on the other, there is some hope that, based on such a classification, more sophisticated approaches can be developed to detect CpG and CpNpG islands in plants.
Regulatory Elements
As stated in the introduction, CAREs are short, conserved motifs of
approximately 5 to 20 nucleotides. Detection of CAREs in the promoter is not
self-evident, because such short motifs are statistically expected to occur at
random every few hundred base pairs. Therefore, the main problem lies in
discriminating "true" from "false" regulatory elements
(Blanchette and Sinha, 2001
Co-expressed genes can be identified through transcript profiling
techniques, such as microarrays (Brown and
Botstein, 1999 Because co-expressed genes tend to behave similarly, they are expected to be coregulated. Under the simplifying assumption that this coregulation occurs at the transcriptional level, co-expressed genes should contain similar cis-regulatory elements in their promoter regions. As a consequence, these yet unknown cis-regulatory elements will be statistically overrepresented in the intergenic regions of the co-expressed genes in comparison with their frequent occurrence in a set of unrelated sequences. This overrepresentation constitutes the general principle on which motif detection algorithms is based.
Usually, genes are part of more extensive gene families that have
originated through both speciation and duplication events. Homologous genes in
distinct species are called orthologs, whereas paralogs refer to homologous
genes that are found in the same genome and have been created through gene
duplication (Mindell and Meyer,
2001
To conceive a general method that can detect regulatory motifs is a great
challenge because of both the complexity and flexibility of the regulatory
mechanisms (see the introduction). An important distinction between the
different approaches used thus far to detect regulatory motifs lies in the
representation of the motif, i.e. the TF-binding site. The simplest
description for a motif is a string of characters (A, C, G, and T), extended
with the 11 IUPAC characters that represent partly unspecified or ambiguous
nucleotides, and is used in the string-based approaches, such as word
counting. A more sophisticated description is to represent a given motif by
describing it in a probabilistic manner in which a certain likelihood is
assessed for each nucleotide at a given position in the motif. An example of a
probabilistic representation is the position-weight matrix, where each column
corresponds to a position in the aligned binding sites and each row to a
nucleotide, as shown in Figure
5. The cells of these matrices contain a number indicating the
probability to find a given nucleotide at that particular position.
Alternatives to describe motifs in a probabilistic manner are the hidden
Markov models (Jarmer et al.,
2001
Counting all of the possible words that may occur across the different
promoter sequences is one of the simplest approaches to find CAREs in a set of
promoters. Among word-counting methods, enumerative and suffix-tree approaches
can be distinguished, the latter being an optimization of the former. Both
methods are string based: The DNA sequence is considered as text in which
oligonucleotides are represented as words or strings. For a given set of
promoter sequences, the frequency of each possible word of a defined length is
computed (Hutchinson, 1996
Once the frequencies of different words are calculated, the words that are
likely to be a "true" regulatory motif have to be differentiated
from those that are not. Therefore, in each of these word-counting methods,
the number of occurrences of a word needs to be compared with the expected
frequency in a set of non-related sequences, represented by a background
model, which is used to obtain an expected probability. The simplest way to
build a background model is by creating a set of randomly generated sequences,
based on the single nucleotide composition of the submitted sequence. More
sophisticated ways to generate a background model are based on Markov chain
statistics (Schbath et al.,
1995
Probabilistic motif detection aims at constructing a multiple alignment by locally aligning small conserved regions in a set of unaligned sequences. Here, we will focus on the matrix-based approaches to illustrate probabilistic motif detection procedures. All methods start from a random motif model, represented as a weight matrix and altered through a series of iterations by machine-learning algorithms that are aimed at finding the optimal score. The process of optimizing the score for a local alignment already tends to converge toward conserved motifs that occur frequently in the data set. The more advanced algorithms incorporate a background model to compensate for given motifs occurring at high frequencies because of compositions similar to those of the non-conserved parts of the sequence (the "background"). A motif in which the average nucleotide composition differs strongly from the background will be assigned a higher score. Implementations differ from each other in the way the background is represented, in how the score is calculated, and in how the optimization is performed. For motif detection algorithms that describe the motif by a weight matrix, expectation maximization and its stochastic variant, Gibbs sampling, are often used for optimization strategies.
The program CONSENSUS was one of the first algorithms that represented a
motif by a weight matrix (Hertz et al.,
1990
The expectation-maximization (EM) method (Stormo,
1988
Gibbs sampling-based strategies have originally been developed to detect
protein motifs but have been adapted later on to handle DNA sequences
(Neuwald et al., 1995
Adaptative quality-based clustering (De
Smet et al., 2002
The procedure that identifies regulatory elements based on a set of
orthologous sequences is named phylogenetic footprinting
(Koop, 1995
A promising novel algorithm has recently been published that identifies the
most conserved motifs among the input sequences as measured by a parsimony
score on the underlying phylogenetic tree
(Blanchette et al., 2002
The most obvious reason why motif detection algorithms fail is because of
their sensitivity to noise. All parts of a sequence that do not contain the
motif constitute noise in the context of motif detection. Moreover, because
sets of related sequences are usually based on other predictive tools, for
instance clustering, they are expected to contain sequences without any shared
motif. A decreasing signal-to-noise ratio exacerbates the identification of
statistically overrepresented motifs and increases the chance of finding false
positives. Probabilistic motif detection methods have been improved
considerably to cope with a large noise level. Current implementations, such
as AlignACE (Hughes et al.,
2000
Because regulatory motifs, in particular in higher eukaryotes, are
concentrated in modules, current research is focusing toward adapting motif
detection algorithms to retrieve dyads, i.e. motifs spaced by a fixed or
variable gap. Within the enumerative statistical methods, Sinha and Tompa
(2000
Vanet et al. (2000
The need for extensive parameter fine tuning complicates nonexpert use of
most of the motif detection approaches described above. Novel implementations
of motif detection algorithms tackle this problem by estimating the optimal
parameter settings themselves, hence, minimizing the number of user-defined
parameters. An example of such a user-defined parameter is the motif length.
Because the motif length is generally unknown in advance, it is not obvious to
choose the parameter setting that results in the true motif. Some algorithms
compute the optimal motif length; for instance, Pattern assembly
(van Helden et al., 2000b
Promoters are very complex structures, defined by many different structural features. The actual regulatory elements are usually very short, which highly complicates their unambiguous identification. As a consequence, the in silico prediction of promoters and regulatory motifs is not straightforward. In addition, our knowledge of transcription regulation in general and organism-specific expression regulation in particular, is still very limited. Especially for plants, solid "intrinsic" genomic data are still needed that can be integrated into existing prediction tools. In this respect, we have started with the analysis of CpG and CpNpG islands, known to be often associated with promoters. Although several implementations for the detection of such "islands" in vertebrates have been described (Ioshikhes and Zhang, 2000 ),
parameter settings used to detect these islands in animals cannot be used to
find similar islands in the Arabidopsis genome. Software and parameters have
to be adapted to the species under investigation. Moreover, even if a reliable
tool were available for the detection of CpG and CpNpG islands associated with
plant promoters, it remains to be proven whether these islands would be
biologically functional and relevant. In addition to a lack of
"intrinsic" genomic data, experimental data on promoters are also
scarce, because in general, thorough analysis of even one single promoter is
very time consuming. Furthermore, the technology is still missing for
exhaustive knowledge of gene expression or for understanding the mechanisms
behind it. Therefore, for now and despite its many shortcomings, in silico
analysis seems to be a privileged alternative to analyze simultaneously a
great number of regulatory elements or promoter regions. Experimental testing
of these in silico predictions may be a manner to increase knowledge on
promoters more quickly and at a lower cost, especially for plants.
We thank two anonymous reviewers for helpful suggestions. Received November 14, 2002; returned for revision January 10, 2003; accepted March 17, 2003.
Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.102.017715.
1 This work was supported by the Vlaams Instituut voor de Bevordering van het
Wetenschappelijk-Technologisch Onderzoek (grant no. STWW–980396). K.F.
is indebted to the Instituut voor de aanmoediging van Innovatie door
Wetenschap en Technologie in Vlaanderen for a predoctoral fellowship, K.M. is
Research Fellow of the Fund for Scientific Research (Flanders), and P.R. is a
Research Director of the Institut National de la Recherche Agronomique
(France).
2 These authors contributed equally to the paper. * Corresponding author; e-mail pierre.rouze{at}gengenp.rug.ac.be; fax 32–9–264–5349.
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TOP
ABSTRACT
CONTENT-BASED FEATURES
zma et al., 1998
2 test
(Vanet et al., 1999
contains an enhancer-like element.
Gene 170:
201–206
80 family of promoter signals.
J Mol Biol 297:
335–353
