Nikola Stojanovic
Department of Computer Science and Engineering, University of Texas at Arlington
Arlington, TX 76019, USA
Transcriptional control signals, DNA motifs, Regulatory modules.
Locating signals for the initiation of gene expression in DNA sequences is an important unsolved problem in
genetics. Over more than two decades researchers have applied a large variety of sophisticated computational
techniques in order to address it, but only with moderate success. In this paper we investigate the reasons for
the relatively poor performance of the current models, and outline some possible directions for future work in
this field.
Eukaryotic gene expression is regulated by a complex
network of protein–DNA and protein–protein interac-
tions. The prevailing opinion, corroborated by many
studies, is that most of these interactions take place
within a few hundred bases upstream from the tran-
scription start site, although this is still somewhat con-
troversial (Nelson et al., 2004). In addition, sites im-
portant for the regulation of genes have been found
in introns and in downstream sequences, as well as
at distant loci, such as the β–globin LCR (Hardison
et al., 1997b). Promoter regions in yeast are charac-
terized by multiple occurrences of the same binding
motif (van Helden et al., 1998), and this is also the
case with many genes from other species. At present,
relatively little is known about genetic pathways and
the mechanisms of gene co-expression, but this situa-
tion is rapidly changing, especially with the advances
in microarray technology and protein–protein interac-
tion studies. However, while these advances provide
an insight into expression patterns and associations,
they do not tell anything about the mechanisms driv-
ing them, nor about the sites in DNA responsible for
their regulation.
Despite of significant efforts over the last twenty
years to computationally predict transcription factor
binding signals in promoter and other regions of the
genome, this remains an elusive goal. While early
approaches relied on a rather naive assumption that
the target sites for protein binding must feature in-
formation content sufficient for them to be uniquely
recognized among all non–sites (Schneider et al.,
1986), disillusionment soon followed, as any attempt
to isolate functional elements in DNA resulted in an
enormous number of false positives. Learning from
that experience, and further experimental evidence,
the bioinformatics community has widely adopted a
view that the motifs for transcription factor bind-
ing in functional regions are grouped in regulatory
modules, sometimes featuring multiple copies of in-
dividual sites. This idea is not new (Ackers et al.,
1982; Mehldau and Myers, 1993; Kel et al., 1995),
however in the recent years there has been an ex-
plosion of computational algorithms designed in an
attempt to identify such modules (Hu et al., 2000;
GuhaThakurta and Stormo, 2001; Rebeiz et al., 2002;
Eskin and Pevzner, 2002; Jegga et al., 2002; Johans-
son et al., 2003; Sharan et al., 2003; Sinha et al., 2003;
Aerts et al., 2004; Donaldson et al., 2005; Kundaje
et al., 2005; Pierstorff et al., 2006; Papatsenko, 2007;
Schones et al., 2007), to list just a few. Some of the
methods also relied on the assumption that multiple
copies of the same motif should be a component of
these modules (Qin et al., 2003; van Helden, 2004). If
a particular motif is over-represented, i.e. if it occurs
in a genomic segment or a group of segments more of-
ten than expected by chance, it was anticipated that it
should indicate a functional signal. Moreover, if mul-
tiple motifs in close proximity satisfy this condition,
it was presumed to be a strong indication of function.
Software developed for the location of such modules
generally relied on previous information about the in-
dividual binding sites forming the modules. The ap-
Stojanovic N. (2008).
In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 464-471
DOI: 10.5220/0001061804640471
proaches were based on the phylogenetic conserva-
tion (Jegga et al., 2002; Sharan et al., 2003; Sinha
et al., 2004; Dieterich et al., 2004; Donaldson et al.,
2005; Pierstorff et al., 2006) of homologous regions
or promoters, approximate matching to known motif
sequences acquired from databases such as TRANS-
FAC (Matys et al., 2006) or some combination of
both. The search was performed for the statistically
significant clusters of motifs (Johansson et al., 2003;
Alkema et al., 2004; Kundaje et al., 2005; Schones
et al., 2007), and it was often combined with match-
ing them to conserved regions in alignments. This
was necessary in order to reduce the search space, but
it proved inaccurate. A regulatory module can con-
tain elements that have not been included in the origi-
nal set, but the elements which did get included were
sometimes spurious, at least when binding in vivo is
concerned. Indeed, over years evaluation studies have
been consistently demonstrating that these tools have
not been very effective (Fickett and Hatzigeorgiou,
1997; Tompa et al., 2005), despite of the progress in
our understanding of the genome, advances in tech-
nology and sophistication of the models.
Many approaches were based on gene expres-
sion study results, and the postulated co-regulation.
Promoter regions of such genes were considered si-
multaneously, and the programs used Gibbs sam-
pling (Lawrence et al., 1993; Thijs et al., 2002),
Bayesian clustering (Qin et al., 2003), Markov Mod-
els (Liu et al., 2001), Expectation Maximization (Bai-
ley and Elkan, 1994), Shannons entropy (Kundaje
et al., 2005), simultaneous dyad motif discovery (Es-
kin and Pevzner, 2002), genetic algorithms (Aerts
et al., 2004) and other techniques in order to isolate
regulatory modules. Despite of the use of very sophis-
ticated algorithms these methods have not achieved
desired accuracy. Why?
The use of motif over-representation for the predic-
tion of transcription factor binding signals can be
roughly divided into three categories:
1. Over-representation of single motifs in groups of
related functional sequences.
2. Over-representation of motifs from a limited set,
such as these recorded in the databases of DNA
regulatory elements, in a single region under con-
3. Over-representation of phylogenetically con-
served blocks in a genomic segment of interest.
Combinations of the above approaches are widely
applied. We shall look at each one individually.
2.1 Single Motifs in Groups of
If the motifs recognized by transcription factor pro-
teins were specific (such as these recognized by re-
striction enzymes, for instance), the search for sys-
tematically present short signals would show promise.
It is commonly accepted that a transcription factor
binding site consists of 5 to 25 nucleotides, and most
experimentally confirmed cores tend to be on the
shorter side of that range. Considering just 5 char-
acters, and assuming that most transcriptional regula-
tory activity indeed happens within about 500 bases
upstream of the gene start, under the simplistic model
of each DNA base being equally likely, the proba-
bility of a chance occurrence of such motif at any
single position would be 1/4
0.00098. Within
a window of 500 bases the expected number would
thus be around 0.49. Using the Poisson distribution
we can estimate the probability of seeing it at least
once in any given window to be 1 e
In consequence, if one would consider a set of just
4 cis–regulatory segments of co-expressed genes (de-
termined by microarray experiments, for instance) in
order to achieve statistical significance (i.e. a pvalue
of less than 0.05) and 5 to be highly significant (p
value < 0.01). This is encouraging, having in mind
that the considered motifs are just 5 characters long,
and that with 6 and more characters one can achieve
statistical significance with regulatory sequences of
just 2 co-expressed genes. If several motifs exhibit
co-occurrence within a single set of regulatory se-
quences, that would almost certainly indicate a real
signal, or at least a part of it (discarding for the mo-
ment the fact that such co-occurrences would also
show up at many random places in the genome).
Even genes which are co-expressed under certain
conditions may not be regulated in the same way.
Their transcription initiation complexes may not be
same, or even similar, or they may exhibit a weak
similarity sufficient to yield co-expression only un-
der certain circumstances. In addition, in any set of
regulatory sequences any given motif may be absent
from some, so the requirement that it should be found
in all should be relaxed. Regardless of this, one can
argue that when a set of regulatory sequences of co-
expressed genes is available, one can determine the
motifs unlikely to be shared by chance, and reliably
identify at least these most common. Further studies
can then be performed to identify proteins bound to
these motifs, and their co-factors.
Unfortunately, nature does not follow simplistic
models. Even as the core promoters lie upstream of
the genes, most of their activity depends on the en-
hancer and other elements, which may be very far
from the genes and regulating several of them si-
multaneously. In some cases, the co-expression pat-
tern may stem from a group of genes affected by the
same enhancer, rather than several enhancers featur-
ing same motifs. Even when there are separate control
elements targeted by same transcription factor pro-
teins, and even if we assume that they would not func-
tion across domains, this expands the 500-base win-
dow to tens of thousands of bases, where only very
long motifs would have a chance of achieving statis-
tical significance.
Another problem is in that we may not even be
able to detect a true binding signal present in all con-
sidered sequences. Transcription factors often feature
a notorious lack of specificity, and within any given
motif only certain positions, which need not be adja-
cent, may be important. The true transcription fac-
tor binding is determined by a very small number of
bases, sometimes as small as 3. The use of position
weight matrices (further referred to as PWMs) may
be helpful in detecting these, but this method is far
from perfect. Their biggest problem is in that they do
not take into account the spatial structure of the mo-
tifs (such as positioning of the bases critical for bind-
ing within the major or minor grove of the DNA he-
lix), which may be crucial in determining whether the
specific nucleotide will interact with a protein or not.
Alas, eventhe most recently published work, while al-
lowing for non–contiguous critical residues, still fails
to take into account anything but raw sequence infor-
mation (Chakravarty et al., 2007).
2.2 Modules of Elements Retrieved
from Databases
Researchers have spent many years meticulously col-
lecting the experimental data concerning the bind-
ing of transcriptional proteins, and compiling the in-
formation about the bound motifs in databases such
as TRANSFAC (Matys et al., 2006), Jaspar (Vlieghe
et al., 2006) or Mapper (Marinescu et al., 2005). The
consistency with which certain sequences are bound
in vitro gives a strong support to the view that the
exact nucleotide sequence is important, and that spa-
tial and epigenetic factors may be more instrumental
in blocking the sequences which are compositionally
similar to the true binding targets, but which should
not be used under the particular circumstances. Al-
though it is still somewhat unclear how much of the
binding effects in vitro would also happen in vivo (Jin
et al., 2007), one can expect a reasonable correlation.
Concentrating on a motif recorded in a database
rather than on any general one that may be repeated
dramatically decreases the complexity of the search.
In the extreme cases of long motifs with a strong con-
sensus one can perform simple pattern matching and
identify the targets uniquely in the genome. However,
such motifs are not common, so the promise of this
approach lies in the search for database motif clus-
ters, i.e. regulatory modules. This still makes sense:
TRANSFAC, the richest of the above mentioned re-
sources, presently contains 7915 transcriptional bind-
ing sites, with consensus motifs organized into 398
position weight matrices. They come from different
species, however one can use this number in rough
calculations. Assuming the average length of a mo-
tif represented in a PWM to be around 9 (and for
the moment discarding the fact that multiple motifs
can match a single consensus) and the same random
model as above, the number of possible motifs of
this length would be 4
= 262144. Consequently,
one could estimate the probability that a motif from
a set of 400 would start at any given position in the
genome as 400/4
0.0015. Within a window of 500
bases, putative regulatory region, the expected num-
ber of chance occurrences of a motif recorded in the
database would roughly be around 0.76. Taking this
number as the Poisson λ, one would need as few as
3 motifs recored in a database within a window of
500 bases (presumably serving as the anchoring for a
regulatory module) in order to achieve statistical sig-
nificance (p–value < 0.05). Such considerations have
given rise to the creation of many software tools.
The first problem with this approach is that in a
large genome such as human, even if we concentrate
only on windows upstream of the known or predicted
genes that would give us around 30 thousand regions,
so with the p–value of 0.05 we would still get around
1500 false positive hits. Of course, for larger mod-
ules the p–values would be much lower, but one can
hardly expect to locate very large clusters of sites,
at least according to the current views on transcrip-
tional regulation. If a module is shared among a few
dozen regulatory sequences, and we would want to
keep the specificity of the search at 0.5 or better, we
would need to have the expected chance groupings at
around, say, 50, which would dictate the pvalue of
0.0017. Even under the above outlined simplified cir-
cumstances this would dictate literally dozens of mo-
tifs to participate in the module, forming a common
core. Consequently, the poor performance of module
searching software comes as no surprise.
The real–world situation is actually much worse:
genomic sequences are not random assemblies of 4
BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing
letters, the regulatory module locations (moreover,lo-
cations that can be taken by individual participating
motifs) are not limited to windows of length 500 im-
mediately upstream of the genes, and many variants
of a motif may match its consensus (as represented by
the PWM). The currently available databases are nei-
ther complete nor accurate, and thresholds for matrix
matching are set in a very ad hoc, heuristic fashion.
In consequence, PWMs tend to match large groups
of motifs, producing hits literally everywhere. Epi-
genetics phenomena may act in such fashion as to
dramatically reduce the numbers of elements partic-
ipating in a regulatory module, by making many in-
stances of chance groupings resembling it inaccessi-
ble to transcriptional proteins, and many interactions
within modules are taking place at the protein, not
DNA level, further reducing the number of motifs in
the genome that would need to be recognized in order
to initiate transcription (and thus the size of the motif
cluster corresponding to the module).
2.3 Phylogenetic Approaches
Another popular approach to identifying functional
signals relies on phylogenetic conservation. Its ba-
sis is a very reasonable assumption that a functional
constraint prevents mutations in DNA from becoming
fixed in population, while sites which are not impor-
tant are free to independently mutate and fix along
separate branches of the evolutionary tree. This hy-
pothesis has been amply confirmed by the study of
coding sequences, and within them of the synony-
mous and non-synonymous substitutions. The en-
couraging results in the study of genes have led to the
assumption that phylogenetic conservation can be ex-
ploited in the search for regulatory signals.
For this purpose, many investigators have turned
attention to the identification of phylogenetic foot-
prints, both in pair-wise sequence comparisons and
multiple alignments. Studies have been performed in
order to establish the most informative genetic dis-
tance between compared species, which have to be
far apart so to minimize the noise coming from ran-
dom conservation, but close enough to share simi-
lar regulatory signals (Hardison et al., 1997a; Miller,
2001), as well as the most informative additional
species to place in a multiple alignment (Thomas et
al., 2003). Pairwise, within the mammalian scope, se-
quences which have diverged about 70 million years
ago (such as human and mouse) have shown greatest
promise, although optimal phylogenetic distance for
analysis tends to vary with the genomic locus (Hardi-
son, 2000).
Even under the most favorable circumstances,
when the effectsof non-specificbinding and permissi-
ble divergence in regulatory signal consensus, as well
as these of inter–species differences, would be mini-
mal, any short signal would not be sufficient to war-
rant significance, or it would require a multiple align-
ment of dozens of very close genomic sequences (Sto-
janovic, 2004). This is becoming feasible with bac-
terial, but not yet with eukaryotic genomes. Re-
searchers have thus concentrated on the identification
of clusters of conserved sites, guided by essentially
the same reasoning as outlined in the previous sec-
tions. In relatively short segments of DNA it is un-
likely that rearrangements would be taking place on
a substantial scale, and the positional conservation of
regulatory signals would lead to good alignments with
short phylogenetic footprints clearly visible.
The probabilistic reasoning applied in this case re-
lied on the strength of the signals (i.e. sequence con-
servation), the likelihood of seeing such conserved
motif by chance, given the phylogenetic distance be-
tween the sequences, and, because the later is often
difficult to establish, on the empirical determination
of the background conservation within the alignment,
as its sections which appear to stand out.
The first problem with this approach lies in the
quality of the alignment itself: genetic regulatory sig-
nals are short and non-specific, and thus not very
likely to be precisely positioned, although their rel-
ative offsets would probably be small. This, on one
hand, may lead to an imprecise definition of motif
boundaries, which often shows as only a partial over-
lap between the footprint and the experimentally con-
firmed binding site. On the other, the footprint itself
may be difficult to identify, as its improperly aligned
bases would both lower the signal and increase the
neighboring region noise. In protein sequences one
can at least partially exploit structural characteristics
(such as α–helix signatures) in order to improve the
alignment quality, but in DNA the only relatively re-
liable markers are the exons of genes. If one looks
for their immediate upstream promoter regions this
may be helpful, but unfortunately the 5’ untranslated
regions of variable lengths and weaker conservation
(with some notable exceptions discussed below) tend
to reduce the anchoring strength of the first exon.
Even when the alignment is reliable, the probabil-
ity of random conservation in even distantly related
sequences is too high to lend credibility to any but
extremely large groupings of footprints, too large to
be plausible anchor sites for the transcriptional com-
plexes. Somewhat surprisingly, such large concen-
trations of footprints are not uncommon in higher
eukaryotic genomes. In fact, many of these are so
large that they can hardly be considered as groupings
of individual, discrete elements (Jones and Pevzner,
2006). The most dramatic example are the non-
coding ultra-conserved segments, defined as blocks
of 200 or more bases with absolute identity among all
compared species. Within the human genome there
are about 500 such blocks conserved among all se-
quenced mammals, but sometimes even among all
vertebrates. The role of these elements is currently
unknown, as many knock-out experiments have re-
peatedly failed to produce visible effects in model an-
imals. Consequently, some researchers have postu-
lated that the ultra-conservation (as well as conserva-
tion of other long non-coding blocks) may be a con-
sequence of a regional repair mechanism of excep-
tional strength, but so far nobody was able to charac-
terize what that mechanism might be, as well as why
it would have been put in place at its target loci.
In order to quantify this phenomenon, we have
looked at the patterns of conservation in mammalian
Hox gene clusters (Stojanovic and Dewar, 2005),
which are well preserved, and home to some of the
mentioned ultra-conserved blocks. Interestingly, in
Hox the highest overall conservation has been ob-
served within the 5’ UTR regions of genes, as illus-
trated in Table 1. While good conservationof untrans-
lated regions is not common genome–wide, it has
been observed in several other cases, such as mam-
malian casein genes (Rijnkels et al., 2003). In sum-
mary, this indicates that there is much more to phy-
logenetic conservation than a simple functional con-
straint. Before that mechanism is understood, some
skepticism concerning the use of sequence conserva-
tion as a hallmark of a functional signal is warranted.
The over-representation concept itself is problematic.
It has been well known, and for a long time now,
that genomic sequences, even in large “junk” areas,
are not random assemblies of four letters. In or-
der to quantify the genome-wide over-representation
of short motifs, we have recently undertaken a sys-
tematic study (Singh et al., 2007) in which we have
noted a remarkable over-representation of many short
motifs throughout the presumably unique human ge-
nomic sequences, as well as (to a lesser extent),
Markov model generated sequences trained on human
chromosomes. As an example, the results counting
the average number of repeated occurrences of mo-
tifs of lengths 4 through 9 measured in 6 datasets of
100 sequences of length 500 each are shown in Ta-
ble 2. Our findings clearly indicated that, first, all
genomic sequences feature dramatically higher num-
bers of repeated short motifs than one would expect
by chance, and, second, that the differences in num-
bers of such motifs do not appear to be significant be-
tween random intergenic and presumably regulatory
sequences upstream of the known genes, despite of
the trend that one can notice in the last two columns
of Table 2. Repeatedly, chi-square tests performed on
these columns and other data could show only mild,
but inconclusive, bias. This indicates that something
else in addition to the functional signal is at play, but
it is somewhat unclear what that might be.
In a series of studies started more than forty years
ago (Waring and Britten, 1966) Britten, Davidson and
others demonstrated that the nuclear genome of di-
verse eukaryotes contained a large fraction of repeti-
tive DNA, and recent large–scale genome sequencing
has established the ubiquitous existence of repeats.
Many of them are of tandem nature, relatively eas-
ily recognizable, however the majority are the result
of the repeated interspersed insertion of transposable
elements, often not capable of further activity (Smit,
1999; Feschotte et al., 2002) — once integrated, these
sequences will never transpose again and can be con-
sidered molecular fossils. Regardless of their ori-
gin and of the mechanisms responsible for their in-
activation, it is widely accepted that fossilized trans-
posons, as a whole, do not assume function to the
host. Consequently, these inactive copies are progres-
sively eroded by mutations accumulating at a neutral
rate until they become unrecognizable. While more
recent insertional events can be readily identified due
to the high similarity of the copies, characterization
of more ancient activity remains a challenge. In the
human genome, almost half of the sequence is con-
sidered unique, but only a small fraction (about 5% of
the total) is thought to be significant, whether coding
or not. This leaves an open question about the ori-
gin and role of the presumably unique non-functional
sequence, which is very likely to originate from an-
cient transpositions and duplications. Due to its de-
gree of degeneracy, it would remain in the genomic
segments under consideration after repeat masking,
but it would also introduce a large number of seem-
ingly over-represented motifs.
Therefore, many of the apparent clusters of con-
served elements are likely just remnants of transpo-
son insertions. While phylogeny–based approaches
are less vulnerable to this effect, it can still be an
issue when comparing sequences from species for
which good repeat libraries have not yet been com-
piled. Regardless of the source, the micro-repetitive
BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing
Table 1: Fractions of the total number of Hox (A, B, C and D clusters) alignment columns in 7 distinct genomic environments
contained in the regions of minimal length of 25 bp, of average conservation with p–value < 0.1 measured against the
background conservation of the entire alignment. The intergenic data for HoxD have been parenthesized because of the
Ensembl gene prediction at the location where many of these regions have been found. Overall, HoxD data are not as reliable
because only a relatively small amount of high–quality sequence of this cluster was available in all considered species (human,
baboon, mouse, rat, cow and pig) at the time of the study.
500–1000bp 5’ 200–500bp 5’ 0–200bp 5 Coding Introns 0–1000bp 3 Intergenic
HoxA 0.067 0.315 0.616 0.223 0.066 0.077 0.057
HoxB 0.115 0.342 0.788 0.639 0.071 0.145 0.024
HoxC 0.104 0.202 0.609 0.521 0.089 0.105 0.035
HoxD 0 0 0 0.061 0.026 0.066 (0.027)
Table 2: The mean numbers of repeated patterns of different lengths in different types of nucleotide sequences. Pattern
counting has been done over 100 sequences of length 500 in each category.
Pattern Expected Random 2
Order 3
Order 5
Order Random Upstream
Length Number Synthetic Markov M. Markov M. Markov M. Genomic Regulatory
4 429.06 425.74 437.99 432.84 432.23 438.97 433.92
5 193.16 189.18 237.83 222.98 222.27 261.64 260.11
6 57.46 55.16 84.33 74.58 75.88 106.62 115.31
7 15.03 14.0 24.5 21.82 23.3 38.66 47.54
8 3.8 3.12 7.05 5.75 6.87 15.72 21.3
9 0.95 0.56 1.94 1.47 1.97 8.57 11.33
structure of genomic sequences of higher eukaryotes
makes it very difficult to locate any feature through
over-representation, simply because the background
is highly non-random.
So far much of the computational search for genomic
regulatory signals have been done using sequence in-
formation only, just because it was the most readily
available. In the context of sequence analysis look-
ing for statistical over–representation was indeed the
most sensible approach. However, while the resulting
combinatorial and probabilistic problems are chal-
lenging and mathematically interesting, biologically
they are questionable. That does not mean that they
are of no value whatsoever, only that they are cur-
rently not being used in the right way.
Much of the genome study is still in the data col-
lecting phase. We are not yet in a position to build an-
alytical models, and without them the quantification
of their effects makes little sense. Over the last few
years the scientific community has been increasingly
turning attention to epigenetics, and there has recently
been a significant increase in the accumulated knowl-
edge about these phenomena. However, the compu-
tational community have so far mostly ignored these
developments. At this time the study of binding sig-
nals in DNA should probably rely more on data min-
ing approaches than on analytical models, although
statistical analysis of the data will remain important.
When studying a potential regulatory role of a ge-
nomic sequence (or a group of sequences, in cases
when co–regulation pattern of a group of genes is sus-
pected), one should take into account, first of all, the
specific experimentally confirmed knowledge about
the region which can be mined from the literature us-
ing currently available technologies. Next, the spe-
cific biochemical information about methylation pat-
terns and domain structure should be applied, be-
fore raw nucleotide information is considered. At
the later stage the prediction and statistical evalua-
tion should be incorporated, but structural data should
still be taken into account, when available. Recent
studies (Segal et al., 2006; Ioshikhes et al., 2006)
have indicated that there may be specific histone pro-
teins positioning codes in DNA, and if further evi-
dence confirms this it would greatly help in the char-
acterization of binding signals for other types of pro-
teins, transcription factors in particular (through eas-
ier identification of potentially open chromatin do-
mains). Only at this point one can concentrate on
the motif–related considerations, looking for these
recorded in databases and these that might be phy-
logenetically conserved. The over–representation per
se may not be sufficient to provide useful informa-
tion, but the appearance of similar motifs in areas
otherwise postulated to share functionality (based on
stronger evidence than just a correlation of expression
in microarray experiments) may be indicative enough
to warrant confidence.
The true discovery has always been through
a well–coordinated combination of computational
and experimental approaches. This takes time, al-
though modern technologies are dramatically facili-
tating such efforts (Jin et al., 2007), so purely compu-
tational methods for genome–wide prediction of tran-
scriptional regulatory signals will remain to be of in-
terest. It is only that the methods will have to change
in order to be really useful, and not just interesting.
The author would like to thank Cedric Feschotte of
UTA Biology for useful discussions about the nature
of DNA repeats, and Subhrangsu Mandal of UTA Bio-
chemistry for the insights concerning epigenetic phe-
nomena. Abanish Singh and David Levine of UTA
Computer Science have provided computational in-
frastructure which generated data leading to our con-
clusions. This work has been partially supported by
NIH grant 1R03LM009033-01A1.
Ackers, G., A.D.Johnson, and M.A.Shea (1982). Quanti-
tative model for gene regulation by lambda phage re-
pressor. Proc. Natl. Acad. Sci. USA, 79:11291133.
Aerts, S., Van Loo, P., Moreau, Y., and De Moor, B.
(2004). A genetic algorithm for the detection of new
cis–regulatory modules in sets of co-regulated genes.
Bioinformatics., 20:1974–1976.
Alkema, W., Johansson, O., Lagergren, J., and Wasserman,
W. (2004). MSCAN: identification of functional clus-
ters of transcription factor binding sites. Nucleic Acids
Res., 32:W195–W198.
Bailey, T. and Elkan, C. (1994). Fitting a mixture model
by expectation maximization to discover motifs in
biopolymers. In Proceedings of the Second Interna-
tional Conference on Intelligent Systems for Molecu-
lar Biology, pages 28–36. AAAI Press.
Chakravarty, A., Carlson, J. M., Khetani, R. S., DeZiel,
C. E., and Gross, R. H. (2007). SPACER: identifica-
tion of cis–regulatory elements with non–contiguous
critical residues. Bioinformatics, 23:1029–1031.
Dieterich, C., Rahmann, S., and Vingron, M. (2004). Func-
tional inference from non-random distributions of
conserved predicted transcription factor binding sites.
Bioinformatics., 20:i109–i115.
Donaldson, I. J., Chapman, M., and Gottgens, B. (2005).
TFBScluster: a resource for the characterization of
transcriptional regulatory networks. Bioinformatics,
Eskin, E. and Pevzner, P. A. (2002). Finding composite reg-
ulatory patterns in DNA sequences. Bioinformatics,
Feschotte, C., Jiang, N., and Wessler, S. (2002). Plant trans-
posable elements: where genetics meets genomics.
Nat. Rev. Genet., 3:329–341.
Fickett, J. and Hatzigeorgiou, A. (1997). Eukaryotic pro-
moter recognition. Genome Res., 7:861–878.
GuhaThakurta, D. and Stormo, G. (2001). Identifying tar-
get sites for cooperatively binding factors. Bioinfor-
matics, 17:608–621.
Hardison, R., Oeltjen, J., and Miller, W. (1997a). Long
human–mouse sequence alignments reveal novel reg-
ulatory elements: a reason to sequence the mouse
genome. Genome Res., 7:959–966.
Hardison, R., Slightom, J., Gumucio, D., Goodman, M.,
Stojanovic, N., and Miller, W. (1997b). Locus control
regions of mamalian β–globin gene clusters: combin-
ing phylogenetic analyses and experimental results to
gain functional insights. Gene, 205:73–94.
Hardison, R. C. (2000). Conserved noncoding sequences
are reliable guides to regulatory elements. Trends
Genet., 16:369–372.
Hu, Y., Sandmeyer, S., McLaughlin, C., and Kibler, D.
(2000). Combinatorial motif analysis and hypothe-
sis generation on a genomic scale. Bioinformatics,
Ioshikhes, I. P., Albert, I., Zanton, S. J., and Pugh, B. F.
(2006). Nucleosome positions predicted through com-
parative genomicsenetic. Nature Genetics, 38:1210–
Jegga, A., Sherwood, S., Carman, J., Pinski, A., Phillips, J.,
Pestian, J., and Aronow, B. (2002). Detection and vi-
sualization of compositionally similar cis–regulatory
element clusters in orthologous and coordinatelly con-
trolled genes. Genome Res., 12:1408–1417.
Jin, V. X., O’Geen, H., Iyengar, S., Green, R., and Farn-
ham, P. J. (2007). Identification of an OCT4 and SRY
regulatory module using integrated computational and
experimental genomics approaches. Genome Res.,
Johansson, O., Alkema, W., Wasserman, W., and Lager-
gren, J. (2003). Identification of functional clusters
of transcription factor binding motifs in genome se-
quences: the MSCAN algorithm. Bioinformatics,
Jones, N. C. and Pevzner, P. A. (2006). Comparative ge-
nomics reveals unusually long motifs in mammalian
genomes. Bioinformatics, 22:e236–e242.
Kel, O., Romaschenko, A., Kel, A., Wingender, E., and
Kolchanov, N. (1995). A compilation of compos-
ite regulatory elements affecting gene transcription in
vertebrates. Nucleic Acids Res., 23:4097–4103.
BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing
Kundaje, A., Middendorf, M., Gao, F., Wiggins, C., and
Leslie, C. (2005). Combining sequence and time se-
ries expression data to learn transcriptional modules.
IEEE/ACM Trans. Comput. Biol. Bioinform., 2:194–
Lawrence, C. E., Altschul, S. F., Boguski, M. S., Liu, J. S.,
Neuwald, A. F., and Wootton, J. C. (1993). Detecting
subtle sequence signals: a Gibbs Sampling strategy
for multiple alignment. Science, 262:208–214.
Liu, X., Brutlag, D., and Liu, J. (2001). Bioprospector:
discovering conserved DNA motifs in upstream regu-
latory regions of co-expressed genes. In Pac. Symp.
Biocomput., pages 127–138.
Marinescu, V., Kohane, I., and Riva, A. (2005). The MAP-
PER database: a multi-genome catalog of putative
transcription factor binding sites. Nucleic Acids Res.,
Matys, V., Kel–Margoulis, O.V., Fricke, E. et al. (2006).
and its module TRANSCompel
transcriptional gene regulation in eukaryotes. Nucleic
Acids Res., 34:D108–D110.
Mehldau, G. and Myers, G. (1993). A system for pat-
tern matching applications on biosequences. Comput.
Appl. Biosci., 9:299–314.
Miller, W. (2001). Comparison of genomic DNA se-
quences: solved and unsolved problems. Bioinformat-
ics, 17:391–397.
Nelson, C., Hersh, B., and Carroll, S. B. (2004). The reg-
ulatory content of intergenic DNA shapes genome ar-
chitecture. Genome Biol., 5:R25.
Papatsenko, D. (2007). ClusterDraw web server: a tool to
identify and visualize clusters of binding motifs for
transcription factors. Bioinformatics, 23:1032–1034.
Pierstorff, N., Bergman, C. M., and Wiehe, T. (2006). Iden-
tifying cis–regulatory modules by combining compar-
ative and compositional analysis of DNA. Bioinfor-
matics, 22:2858–2864.
Qin, Z., McCue, L., Thompson, W., Mayerhofer, L.,
Lawrence, C., and Liu, J. (2003). Identification of co-
regulated genes through Bayesian clustering of pre-
dicted regulatory binding sites. Nature Biotechnology,
Rebeiz, M., Reeves, N. L., and Posakony, J. W. (2002).
SCORE: A computational approach to the identifi-
cation of cis–regulatory modules and target genes in
whole–genome sequence data. Proc. Natl. Acad. Sci.
USA, 99(15):9888–9893.
Rijnkels, M., Elnitski, L., Miller, W., and Rosen, J. M.
(2003). Multispecies comparative analysis of a
mammalian–specific genomic domain encoding se-
cretory proteins. Genomics, 82:417–432.
Schneider, T. D., Stormo, G. D., Gold, L., and Ehrenfeucht,
A. (1986). Information content of binding sites on
nucleotide sequences. J. Mol. Biol., 188:415–431.
Schones, D. E., Smith, A. D., and Zhang, M. Q. (2007). Sta-
tistical significance of cis-regulatory modules. BMC
Bioinformatics, 8:19.
Segal, E., Fondufe–Mittendorf, Y., Chen, L., Thastrom, A.,
Field, Y., Moore, I. K., Wang, J.-P. Z., and Widom, J.
(2006). A genomic code for nucleosome positioning.
Nature, 442:772–778.
Sharan, R., Ovcharenko, I., Ben-Hur, A., and Karp, R.
(2003). CREME: a framework for identifying cis
regulatory modules in human–mouse conserved seg-
ments. Bioinformatics, 19:i283–i291.
Singh, A., Feschotte, C., and Stojanovic, N. (2007). A study
of the repetitive structure and distribution of short mo-
tifs in human genomic sequences. Int. J. Bioinformat-
ics Research and Applications, 3:523–535.
Sinha, S., Schroeder, M., Unnerstall, U., Gaul, U., and
Siggia, E. (2004). Cross–species comparison sig-
nificantly improves genome–wide prediction of cis
regulatory modules in drosophila. BMC Bioinformat-
ics., 5:129.
Sinha, S., vanNimwegen, E., and Siggia, E. (2003). A prob-
abilistic method to detect regulatory modules. Bioin-
formatics, 19:i292–i301.
Smit, A. (1999). Interspersed repeats and other memen-
tos of transposable elements in mammalian genomes.
Curr. Opin. Genet. Dev., 9:657–663.
Stojanovic, N. (2004). Computational methods for the anal-
ysis of differential conservation in groups of similar
DNA sequences. Genome Informatics, 15:21–30.
Stojanovic, N. and Dewar, K. (2005). A probabilistic ap-
proach to the assessment of phylogenetic conservation
in mammalian Hox gene clusters. In Proceedings of
the BIOINFO 2005, International Joint Conference of
InCoB, AASBi and KSBI, pages 118–123.
Thijs, G., Marchal, K., Lescot, M., Rombauts, S., De Moor,
B., Rouze, P., and Moreau, Y. (2002). A Gibbs sam-
pling method to detect overrepresented motifs in the
upstream regions of coexpressed genes. J. Comput.
Biol., 9(2):447–464.
Thomas, J.W., Touchman, J.W., Blakesley, R.W. et al.
(2003). Comparative analysis of multi-species se-
quences from targeted genomic regions. Nature,
Tompa, M., Li, N., and Bailey, T.L. et al. (2005). As-
sessing computational tools for the discovery of tran-
scription factor binding sites. Nature Biotechnology,
van Helden, J. (2004). Metrics for comparing regulatory se-
quences on the basis of pattern counts. Bioinformatics,
van Helden, J., Andre, B., and Collado-Vides, J. (1998).
Extracting regulatory sites from the upstream region
of yeast genes by computational analysis of oligonu-
cleotide frequencies. J. Mol. Biol., 281:827–842.
Vlieghe, D., Sandelin, A., De Bleser, P. J., Vleminckx,
K., Wasserman, W. W., van Roy, F., and Lenhard,
B. (2006). A new generation of JASPAR, the open–
access repository for transcription factor binding site
profiles. Nucleic Acids Res., 34:D95–D97.
Waring, M. and Britten, R. (1966). Nucleotide sequence
repetition: a rapidly reassociating fraction of mouse
DNA. Science, 154:791–794.