10.1101/gr.4140006 Access the most recent version at doi:
2006 16: 1455-1464; originally published online Oct 19, 2006; Genome Res.
Laura Elnitski, Victor X. Jin, Peggy J. Farnham and Steven J.M. Jones
computational and experimental techniques
Locating mammalian transcription factor binding sites: A survey of
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Locating mammalian transcription factor
binding sites: A survey of computational
and experimental techniques
Laura Elnitski,1,4Victor X. Jin,2Peggy J. Farnham,2and Steven J.M. Jones3
1Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Rockville,
Maryland 20878, USA;2Genome and Biomedical Sciences Facility, University of California–Davis, Davis, California 95616-8645,
USA;3Genome Sciences Centre, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada V5Z-4E6
Fields such as genomics and systems biology are built on the synergism between computational and experimental
techniques. This type of synergism is especially important in accomplishing goals like identifying all functional
transcription factor binding sites in vertebrate genomes. Precise detection of these elements is a prerequisite to
deciphering the complex regulatory networks that direct tissue specific and lineage specific patterns of gene
expression. This review summarizes approaches for in silico, in vitro, and in vivo identification of transcription factor
binding sites. A variety of techniques useful for localized- and high-throughput analyses are discussed here, with
emphasis on aspects of data generation and verification.
[Supplemental material is available online at www.genome.org.]
One documented goal of the National Human Genome Research
Institute (NHGRI) is the identification of all functional noncod-
ing elements in the human genome (ENCODE Project Consor-
tium 2004). Studies by ENCODE Consortium members and other
investigators in the field have demonstrated that a mixture of
computational and experimental approaches is required for the
genome-wide elucidation of cis-acting transcriptional regulatory
elements. These include promoters, enhancers, and repressor el-
ements, along with structural components like origins of repli-
cation and boundary elements. For instance, experimentally
based oligo-capping methods represent technical advances to-
ward defining the precise 5? ends of mRNA transcripts (Suzuki et
al. 2002; Shiraki et al. 2003; for review, see Harbers and Carninci
2005), enabling the robust prediction of proximal promoter re-
gions and their components (Trinklein et al. 2003; Cooper et al.
2006). In addition, sensitive and comprehensive microarray-
based analyses of human RNAs are providing a detailed map of
the transcribed regions of the human genome (ENCODE Consor-
tium, in prep.). Methods to characterize replication origins on a
genome-wide scale are also in development. Techniques like mi-
croarrays are providing details on the coordinated timing of rep-
lication by detecting twofold increases in DNA copy number, or
heavy isotope incorporation into newly synthesized DNA (for
review, see Schwob 2004; MacAlpine and Bell 2005; ENCODE
Consortium, in prep.). Along with newly emerging techniques, a
few historically proven approaches still provide reliable indica-
tors of functional regions. These include the detection of altered
chromatin structure using DNaseI hypersensitivity (Weisbrod
and Weintraub 1979; ENCODE Consortium, in prep.) and se-
quence conservation as found in pairwise- or multi-species com-
parisons (for review, see Miller et al. 2004; ENCODE Consortium,
The function of promoters, enhancers, replication origins,
and other regulatory elements is mediated by DNA/protein in-
teractions. Thus, one major step in the characterization of the
functional elements of the human genome is the identification
of all the protein binding sites, which serve as the atomic units of
functional activity (Collins 2003). Recent studies focused on the
analysis of transcription factor binding sites in one percent of the
human genome (ENCODE Consortium, in prep.) have revealed
the need for integrated computational and experimental ap-
proaches in the identification of genome-scale sets of transcrip-
tional regulatory elements. Given the amount of noncoding se-
quence that is under selective constraint (∼3.5% of the human
genome; Waterston et al. 2002; Chiaromonte et al. 2003), the
anticipated number of DNA binding factors (∼1962; Messina et
al. 2004), the complexity of finding a suitable biological assay to
detect a given functional activity, and the cost of pursuing such
efforts, synergistic collaborations between computational predic-
tion and high-throughput experimental validation remain a
The techniques summarized herein are meant to provide a
comprehensive overview of the complementary aspects of com-
putational prediction and experimental validation of functional
sites. These described methods are often considered fundamental
to investigators working within that specific field but may be
unfamiliar to an outside investigator, such as a biologist wanting
to predict the identity of transcription factor binding sites
(TFBSs), or a computer programmer trying to validate the predic-
tion of a biological feature. In that spirit, we include techniques
from categories that address locus-specific and high-throughput
methodologies. Supplemental tables list the Web servers avail-
able for computational predictions and provide URLs for proto-
cols of experimental techniques. Additionally, readers are en-
couraged to examine the journal Nucleic Acids Research for its
Annual Review of Bioinformatics Web Sites (2006, http://
Although broad in scope, this review of computational and
experimental techniques is intended to elucidate aspects of their
interdependence. Computational techniques, by definition, are
predictive and vary in performance quality. Experimental results
E-mail email@example.com; fax (301) 435-6170.
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provide a spectrum of information, ranging from implied func-
tional relevance to validation of protein identity. In this review,
we will begin with a description of computational approaches
used to identify transcription factor binding sites, define a need
for additional experimental data sets, and introduce several ex-
perimental methodologies for identifying regulatory elements.
We will then end with a description of how computational analy-
ses of these experimental data sets can provide new insights into
transcriptional regulation. Figure 1 illustrates this interplay be-
tween computational and experimental strategies. Whether ini-
tiating from a locus-specific or high-throughput perspective, all
indicated pathways lead to the ultimate goal of validation of a
A computational approach to studying transcriptional regulatory
networks requires the analysis of large and complex data sets.
These data sets often include such diverse yet interdependent
data as (1) gene expression profiles, (2) locations of promoters
and computationally predicted transcription factor binding sites,
(3) experimentally identified target genes of specific transcrip-
tion factor families, and (4) sequence conservation (for review,
see Qiu 2003). Using such data sets, investigators have produced,
for example, a computational catalog of high-quality putative
regulatory elements from vertebrates (Prakash and Tompa 2005).
Also, ab initio approaches using the techniques of conservation,
overrepresentation, and coregulation are successfully being ap-
plied to determine cohorts of expression groups within the ge-
nome (Cora et al. 2005). Methods to identify tissue-specific fac-
tors have evolved from detecting single factors that regulate ex-
pression in tissues such as liver (Krivan and Wasserman 2001) or
muscle (Wasserman and Fickett 1998) to comprehensively iden-
tifying novel motifs that confer tissue-specific expression pat-
terns (Qian et al. 2005; Blanchette et al. 2006; Huber and Bulyk
2006). As transcription factors often work cooperatively, binding
in close physical proximity, recent computational approaches
have used the presence of co-occurring motifs to identify puta-
tive regulatory modules (Kreiman 2004; Zhou and Wong 2004;
Zhu et al. 2005). The recent analysis by Blanchette and colleagues
(2006) predicted more than 118,000 such regulatory modules in
the human genome. Clearly, these (and other) computational
approaches used to identify transcription networks are providing
new insights into transcriptional regulation. However, in this
review, we will focus only on the various computational meth-
odologies used to predict transcription factor binding sites. An
in-depth discussion of all the computational techniques used to
predict binding sites is beyond the scope of this review; however,
a survey of tools available as Web-based resources is documented
in Supplemental Table 1. Also, evaluations of some computa-
tional tools are available in other publications (Roulet et al. 1998;
Tompa et al. 2005).
The myriad approaches to the computational prediction of
functional binding sites are all based on either pattern matching
or pattern detection (Supplemental Table 1). Pattern matching
utilizes prior knowledge of all characterized DNA binding sites
for a given protein. Finding these patterns within the genome
allows one to identify putative protein binding sites that might
represent uncharacterized regulatory elements (van Helden
2003). Pattern matching requires that the known binding sites
for a given protein be represented as a consensus of the collection
or as a matrix of acceptable nucleotides at each position. The use
Biological Mechanisms” depicts the ultimate goal for researchers studying a biological pathway. Approaches illustrated from the top of the image
downward depict high-throughput analyses used to predict transcription factor binding sites and to determine functional activity of those elements.
Approaches illustrated from the bottom of the image upward signify more conventional “locus-specific” analyses that start from a narrowly defined
hypothesis of biological function and can include the use of animal models.
The interplay and codependence of experimental and computational approaches. The centrally located yellow box labeled “Validation of
Elnitski et al.
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of consensus patterns is convenient but may exclude a significant
subset of a binding site repertoire because of omission of impor-
tant variable regions (Roulet et al. 1998). Some of the missing
information can be captured through IUPAC strings (an alphabet
of characters other than ACGT) that provide a means to indicate
alternative choices at each position (e.g., Y = C or T). However,
IUPAC strings, while conveying more information than a core
consensus, do not provide information as to the relative fre-
quency of the alternative nucleotides. The information content
of regulatory sites can be more accurately represented by position
weight matrices (PWMs) (Stormo et al. 1982; Harr et al. 1983) or
position-specific scoring matrices (PSSMs), which incorporate
pattern variability by recording the frequencies of nucleotides at
each site or by assigning penalties to nucleotides that should not
appear within a factor binding site. Although PWM pattern
matching represents an improvement over consensus mapping
in sensitivity (i.e., it has a lower false negative rate), when used as
the sole means of identifying protein-binding sites it still suffers
from the limited amount of training data available (Roulet et al.
1998) and often results in a high rate of false-positive predictions
(Tompa et al. 2005; Jolly et al. 2005).
A number of algorithmic approaches have been developed
for de novo pattern detection (i.e., the discovery of unknown
motifs), many of which search for recurring or overrepresented
patterns in DNA. Examples include Hidden Markov Models (Ped-
ersen and Moult 1996), Gibbs sampling (Lawrence et al. 1993),
greedy alignment algorithms (e.g., CONSENSUS, Hertz and
Stormo 1999), expectation-maximization (MEME, Bailey and El-
kan 1995), probabilistic mixture modeling (NestedMica, Down
and Hubbard 2005) and exhaustive enumeration (i.e., detecting
the set of all nucleotide n-mers, then reporting the most frequent
or overrepresented; e.g., Weeder, Pavesi et al. 2004). Alterna-
tively, variations of a pattern can be modeled using information
theory (Schneider 2000). Using this approach, the frequencies of
nucleotides at each position give insight into whether a protein
binds to the major or minor groove of the DNA helix. Once these
patterns are determined for a particular protein, the range of
variation in target binding sequences can be modeled and
matched to the genome (Gadiraju et al. 2003; Vyhlidal et al.
The use of orthologous sequences, also referred to as phylo-
genetic footprinting, introduces the filtering power of evolution-
ary constraint to identify putative regulatory regions that stand
apart from the background sequence conservation (Tagle et al.
1988). The search for both known binding sites (pattern match-
ing) and overrepresented novel motifs (pattern detection) can be
improved through the analysis of data sets containing ortholo-
gous or coregulated genes (summarized in Frith et al. 2004). In
one case, regions that colocalized as high-scoring PWM matches
and conserved regions in human–mouse–rat genomic align-
ments provided a 44-fold increase in the specificity of the pre-
dictions compared with pattern matching alone (Gibbs et al.
2004). In a study involving pattern detection, Xie et al. (2005)
report the first comprehensive screen for regulatory motifs in
human promoters by identifying motifs that are enriched above
background and are conserved in human, mouse, rat, and dog
genomes. Analysis tools that have been refined by incorporating
cross-species conservation include Gibbs sampling (e.g.,
CompareProspector, Liu et al. 2004; PhyloGibbs, Siddharthan et
al. 2004, 2005); expectation maximization (PhyloME, Sinha et al.
2003; orthoMEME, Sinha et al. 2003; EMnEM, Moses et al. 2004)
and greedy alignment algorithms (PhyloCon, Wang and Stormo
2003). A variation of phylogenetic footprinting known as phy-
logenetic shadowing uses the collective divergence time of a rela-
tively large number of closely related species (Boffelli et al. 2003).
This has the advantage of identifying functional elements that
are specific to a lineage from within an unambiguously aligned
set of sequences. The disadvantage of this approach lies in the
fact that the number of genomic sequences required for such an
analysis is currently prohibitive for most investigators.
The recent refinements of computational techniques for
identifying binding sites have evoked considerable interest from
the field in the development of follow-up or validation analyses.
For example, evolutionary constraint has been used not only to
identify sites but also to distinguish real motifs from false posi-
tives (Blanchette and Sinha 2001) and to discern potentially
functional sites from neutral DNA (King et al. 2005). Other vali-
dation analyses capitalize on properties of regulatory elements
such as the presence of spaced dyads (pairs of short words sepa-
rated by a fixed distance) and the propensity for palindromic
content (van Helden et al. 2000), as well as the interdependence
of bases at specific positions within a motif (Wang et al. 2005).
Also, the information content and binding preferences of known
motifs have been used to identify binding sites of new family
members (Keles et al. 2003). Currently, the limiting factor in
confirmation and refinement of in silico predictions is a lack of
experimental data (Vavouri and Elgar 2005). Despite our best
efforts at predicting functional sites, the cellular environment
dictates which events can and cannot occur by imposing the
selective constraint of higher-order chromatin structure; conse-
quently, experimental confirmation remains the highest form of
validation. Described below are various experimental techniques
that can be used in conjunction with the computational ap-
proaches depicted above.
Experimental approaches to identifying transcription factor
binding sites are necessary to understand their contributions to
biological function, to address the complexity of tissue-specific
and temporal stage-specific effects on gene expression (Levine
and Tjian 2003), and to continue refinement of computational
predictions. Experimental techniques useful for identifying tran-
scription factor binding sites include those that, although not
directly measuring transcription factor/DNA interactions, can
lead to the identification of regulatory elements. Such techniques
include analysis of alterations of chromatin structure and experi-
mental manipulation of defined DNA segments, both of which
are advantageous in helping to locate a functional element when
the exact regulatory protein(s) involved is not known. Other
techniques, which directly measure protein/DNA interactions,
provide more precise information but are only useful after the
identity of the critical transcription factor has been established.
Examples of these two types of approaches, each of which can
range in scope from localized, site-specific analyses to high-
throughput assays that generate broad conclusions about bind-
ing site preferences and regulation of gene expression, are de-
scribed below. Protocols for these experimental assays are avail-
able in the references and in Supplemental Table 2.
DNaseI hypersensitivity provides a method to map changes in
chromatin structure. The degree of response of the DNA se-
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quence to DNase is classified as generalized sensitivity or hyper-
sensitivity. Generalized nuclease sensitivity is a property inher-
ent in all actively expressed genes (Gazit and Cedar 1980), cor-
relating closely with the presence of acetylated histones.
Whether a cause or an effect, the presence of acetylated histones
accompanies the appearance of open chromatin extending over
10s to 100s of kilobases and is documented in the chicken lyso-
zyme, chicken ovalbumin, human apolipoprotein B, c-fos, and
chicken ?-globin loci (Lawson et al. 1982; Goodwin et al. 1985;
Jantzen et al. 1986; Feng and Villeponteau 1992; Hebbes et al.
1994). In contrast, hypersensitivity refers to regions showing ex-
treme sensitivity to the cleavage effects of the enzyme that are
localized to short stretches of DNA ranging from 100 to 400 bp in
length (Gross and Garrard 1988). Extreme sensitivity serves as a
marker for functional regions that fall in noncoding sequences;
these include promoters, enhancers, silencers, origins of replica-
tion, recombination elements, and structural sites of telomeres
and centromeres (Cereghini et al. 1984; Gross and Garrard 1988).
Early observations suggested that hypersensitivity is associated
with the removal of nucleosomes (Almer et al. 1986), whereas
more recent analyses can detect the presence of histones in modi-
fied form (Gui and Dean 2003), such as acetylated histones H3
and H4 and methylated H3 at lysine 4 (K4; Jenuwein and Allis
2001), at the hypersensitive sites (HSs). The modifications on the
histones reduce the affinity of DNA for the nucleosome (Bode et
al. 1980), facilitating the interaction of DNA with trans-acting
factors (Vettese-Dadey et al. 1996). Thus, the presence of hyper-
sensitivity, which originated as a feature of already-characterized
functional sites, has now evolved into a predictive indicator for
the presence of a functional site. Furthermore, the impermanent
nature of the nuclease hypersensitivity provides insight into the
temporal and tissue-specific stages of activity in the underlying
elements when assayed using representative biological samples.
Many studies have focused on a locus-specific analysis of
nuclease hypersensitive sites. In such studies, the resolution with
which one can identify the location of a DNaseI HS varies by
approach, ranging from ?500 bp using the indirect end-labeling
technique (Wu 1980) to nearly nucleotide resolution using PCR
assessment (Yoo et al. 1996) and quantitative PCR (McArthur et
al. 2001). Thus, the interpretation of results is dependent on the
exact method used for analysis. High-throughput approaches to
assess DNase hypersensitivity address the appearance and disap-
pearance of functional sites on a genome-wide scale. Compari-
sons can be made between cells from different tissues, or within
the same type of cell to measure a response to changes in the
cellular environment. Two new experimental techniques that
have emerged as promising technologies in the ENCODE project
(ENCODE Project Consortium 2004) are quantitative chromatin
profiling (Dorschner et al. 2004) and massively parallel signature
sequencing (Crawford et al. 2005). Additionally, Yuan et al.
(2005) have used tiled microarrays to identify translation posi-
tioning of nucleosomes in Saccharomyces cerevisiae, revealing that
69% of nucleosomal DNA contained positioned nucleosomes,
whereas transcription start sites tended to be nucleosome-free
regions. Although the scope and expense of such experiments
could limit the pace at which new cell lines or tissue types are
investigated, comparisons across samples hold the promise of
identifying regions that appear in a specific lineage of cells and
thus could provide a systematic means of profiling functional
sites that describe a cellular phenotype. Importantly, recent com-
putational advances, which use a sequence-based classification
algorithm, have relied on experimental data sets to model hy-
persensitive sites in silico (Noble et al. 2005). In this approach, a
support vector machine is trained to discriminate between ex-
perimentally validated HSs and nonHSs. Experimental validation
of the genome-wide probability scores shows 70% predictive ac-
curacy, providing support for the extension of this application to
additional tissue types. Clearly, additional cycling between ex-
perimental validation and computational predictions will con-
tinue to improve identification of HSs.
Gene expression assays measure changes in the production of a
reporter protein in response to cis-acting regulatory signals. For
instance, promoter sequences placed upstream of a firefly-
luciferase reporter gene (de Wet et al. 1987) or green fluorescent
protein (GFP; Tsien 1998) can be introduced into a sample of
cultured cells and subsequently assayed in a 24- to 48-h time
period, generating reproducible results. Promoters and enhancers
can be tested in short-term reactions known as transient trans-
fections, in which the test plasmid remains unintegrated (epi-
somal) in the nucleus. The introduction of an enhancer element
creates a “gain-of-function” result, whereas “loss-of-function” as-
says result from mutations of functional nucleotides in the target
region. Alternatively, long-term assays, or stable transfections,
use a linearized plasmid that integrates into the genomic DNA
and hence is subject to effects conferred by the surrounding chro-
matin environment. Stable transfections are frequently used to
identify sequences that protect against both positive and nega-
tive influences of surrounding chromatin (such as boundary el-
ements) and to provide a biologically relevant view of the func-
tional activity as measured within a living cell. High-throughput
approaches to cell transfection include the use of cationic lipids
or electroporation units that work in a 96-well plate format
(Strauss 1996; Ovcharenko et al. 2005; Siemen et al. 2005). One
assessment of high-throughput gene expression focused on pu-
tative promoters in one percent of the human genome, assayed
in multiple cell lines (Trinklein et al. 2003; Cooper et al. 2006).
Such large-scale promoter/enhancer assays provide insight into
the features commonly found in promoters and serve to verify
the functional capability of computationally predicted elements.
The immortalized cell lines used in most experiments rarely
recapitulate a “normal” cellular environment (Worton et al.
1977). Nevertheless, they provide a suitable environment in
which to initiate studies on the mechanisms of gene regulation
rapidly. In contrast, although more technically difficult, in vivo
expression assays using animal models supply a means of assess-
ing functional elements within a biologically relevant, tissue-
specific context. Analyses using fish, frogs, chickens, and mice
(Khokha and Loots 2005; Poulin et al. 2005; Shin et al. 2005;
Hallikas et al. 2006; Takemoto et al. 2006) have shown that an
element or binding site can act in a defined biological pathway;
such conclusions could not have been made with cultured cells.
For example, Hallikas et al. (2006) used a computational ap-
proach to identify mammalian enhancers and then showed ex-
treme developmental and tissue-specific activity of several of the
identified enhancer elements.
Intraspecies comparative approaches in Ciona intestinalis
highlight the versatility of this model organism. Boffelli et al.
(2004) identified candidate regulatory regions undergoing the
slowest mutation rates relative to the surrounding rates and
tested them for functional activity in transgenic tadpoles. The
work identified a set of noncoding elements that act as tissue-
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specific enhancers in notochord, endoderm, and neurotube. The
availability of genomic sequence from a closely related species,
Ciona savignyi, provides opportunities to identify additional can-
didate regulatory elements through interspecies comparisons. A
summary of Web resources and experimental data available for
Ciona is provided in Shi et al. (2005).
Protein binding assays
EMSAs and DNaseI protection
Historically, the traditional approach to defining protein–DNA
interactions was through the electrophoretic mobility shift assay
(EMSA) (Fried and Crothers 1981; Garner and Revzin 1981). By
utilizing the sieving power of nondenaturing polyacrylamide gels
to separate a protein-bound DNA molecule from one that is un-
bound, the in vitro “gel-shift” assay is ideal for verifying the
ability of an unknown protein to recognize and bind a target
DNA sequence. DNaseI protection (also known as DNaseI foot-
printing) (Galas and Schmitz 1978) is another technique for the
precise localization of protein binding sites that does not require
knowledge of protein identity. The technique combines the
binding reaction of an EMSA with the cleavage reaction of
DNaseI. When the radionuclide-labeled probe is visualized on a
denaturing polyacrylamide gel, sites protected from cleavage cre-
ate a blank image in the otherwise semicontinuous ladder of
nucleotide positions. As an in vitro assessment of protein bind-
ing, DNaseI protection uses a longer probe than EMSA (500 bp vs.
25 bp), elucidating the positions of numerous proteins on the
probe simultaneously. A variation of the in vitro assay uses
chemical cleavage to produce a uniform cleavage pattern to over-
come limitations of the DNaseI enzyme and simplify the inter-
pretation of results (Drouin et al. 2001). With both gel-shift and
DNase footprinting assays, unintended DNA–protein interac-
tions are often detected. This can result from interference of non-
specific DNA binding proteins, such as DNA repair proteins,
which can bind to the ends of DNA probes in a binding reaction
Technical advances related to in vitro binding assays include
replacing the use of radionucleotides with use of fluorescent la-
bels (Onizuka et al. 2002) and scaling up for high-throughput
approaches. For example, SELEX (systematic evolution of ligands
by exponential enrichment) (Tuerk and Gold 1990) and CASTing
(cyclic amplification and selection of targets) (Wright et al. 1991)
screen large pools of short, random oligonucleotide probes for
recognition by a specific protein. The JASPAR database of nonre-
dundant PWMs contains binding site information obtained with
this in vitro approach (Sandelin et al. 2004). Other high-
throughput in vitro approaches include DIP–ChIP (DNA immu-
noprecipitation) (Liu et al. 2005) and double-stranded DNA micro-
Use of gel shift assays and in vitro DNase footprinting is quickly
giving way to use of assays that capture binding as it happens in
the in vivo environment. For instance, the development of in
vivo footprinting now allows the study of DNA/protein events
within a living cell. This assay uses ligation-mediated PCR (Muel-
ler and Wold 1989) to capture the fractured pieces of genomic
DNA that flank the sites protected by protein. Such in vivo assays
are informative and can provide tissue-specific information con-
cerning transcription factor binding, yet they can be technically
challenging (Komura and Riggs 1998). Also, they do not provide
information as to the identity of the involved protein(s). In con-
trast, the in vivo technique of chromatin immunoprecipitation
(ChIP) is especially useful when the protein of interest is known.
Reliable protocols for this procedure are listed in Supplemental
Table 3. ChIP assays represent a modification of “pull-down”
assays in which target proteins are precipitated from solution
using an antibody coupled to a retrievable tag. In contrast with
standard protein immunoprecipitation assays, ChIP assays cap-
ture in vivo protein–DNA interactions by cross-linking proteins
to their DNA recognition sites using formaldehyde. Before pre-
cipitation by a transcription factor-specific antibody, the DNA is
fragmented into small pieces averaging 100–500 bp. After pre-
cipitation, reversal of the cross-linking reaction releases the DNA
for subsequent detection by PCR amplification. Caveats to the
ChIP assay include an inability to detect precise contacts of bind-
ing within the 100–500-bp region of the DNA probe and the
potential for recovering indirect interactions created by protein–
protein contact rather than protein–DNA interactions. Kang et al.
(2002) have proposed a method to combine ChIP with DNase
protection to address the limitations of both assays, thereby iden-
tifying the interacting protein in addition to its interaction site.
Although most ChIP assays are performed using tissue culture
cells, modifications of the assay have been developed to allow
analysis in mammalian tissues (Kirmizis et al. 2003; Chaya and
Zaret 2004). A significant challenge that remains for tissue-ChIP
assays is in gaining enough tissue for use in the assay, especially
if the source tissue is rare (such as for human tumor samples).
High-throughput variations of the ChIP technique use ligation-
mediated PCR to amplify the pool of DNA sequences as uni-
formly as possible, generating many copies of all genomic bind-
ing sites for a given protein. The assortment of DNA binding sites
recovered in a ChIP assay can then be visualized by hybridization
to a microarray of genomic sequences. This approach, called
ChIP–chip, has been used to interrogate protein–DNA interac-
tions in intact cells (Ren et al. 2000) and is well documented in
many comprehensive reviews (see, e.g., Hanlon and Lieb 2004).
Just as in cDNA microarrays, DNA that has undergone ChIP assay
is labeled with the fluorophore Cy5 and its signal, when bound
to an array of target sequences, is compared with signal from an
equal amount of total input DNA labeled with Cy3. The relative
enrichment of immunoprecipitated DNA over total input DNA
(Cy5/Cy3) is used to identify putative binding sites. As the tech-
nology improves, the number of searchable target binding sites
on the microarray chip grows more complex in nature. For in-
stance, early applications using intergenic regions in yeast (Ren
et al. 2000) led the way for analyses of putative promoter regions
in humans (Li et al. 2003; Odom et al. 2004). More complex
targets were developed using CpG islands associated with pro-
moters (Weinmann and Farnham 2002; Mao et al. 2003; Heisler
et al. 2005), finally converging on ever-increasingly refined plat-
forms of nonrepetitive human genomic DNA. A recent study
used a series of arrays that contained ∼14 million 50mer oligo-
nucleotides, designed to represent all the non-repeat DNA in the
human genome at 100-bp resolution, to define a genome-wide
map of active promoters in human fibroblasts (Kim et al. 2005).
One of the key issues in processing the ChIP–chip raw data
is to identify the “best” binding sites among the collection of
potential DNA targets, pointing to the need for computational
scientists to join experimental teams. Several statistical ap-
proaches have been developed to detect such regions, which are
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summarized eloquently in Buck and Lieb (2004). They include a
median percentile rank (Lieb et al. 2001), single array-error mod-
els (Ren et al. 2000; Li et al. 2003), and a sliding window analysis
(Buck and Lieb 2004). The tool ChiPOTle, which uses a sliding
window approach, is publicly available for the analysis and in-
terpretation of ChIP–chip data (Buck et al. 2005). Bieda et al.
(2006) describe both theoretical and statistical approaches to
ChIP–chip data analysis, bringing new insights into the role of
the protein E2F1, which acts at a large fraction of human pro-
moters without recognizing a consensus motif. Additional meth-
ods are described in a series of recent reports including variance
stabilization (Gibbons et al. 2005), enrichment detection (Caw-
ley et al. 2004), and model-based methods (Kim et al. 2005).
Programs to identify the most significant regions for protein
binding from a ChIP–chip analysis include MPEAK (Kim et al.
2005) and PEAKFinder (Glynn et al. 2004). URLs for these Web
sites are listed in Supplemental Table 1.
Experimental conditions and reagent quality greatly affect
ChIP–chip results. Published work by Oberley and Farnham
(2003) and related papers in the same issue of the journal Methods
in Enzymology (Allis and Wu 2003) provide guidance on these
issues. Antibodies suitable for ChIP–chip applications are sum-
marized in the ChIP-on-chip database (see Supplemental Table
1), as are additional resources for experimental protocols. ChIP–
chip analyses conducted through February 2005 are summarized
in Sikder and Kodadek (2005). Repositories and genomic servers
listed in Supplemental Table 3 contain additional ChIP–chip data
sets, many of which were contributed through the efforts of EN-
CODE project participants (www.genome.gov/10005107).
Computational follow-up experiments
The ChIP–chip assays described above allow the identification of
the genomic region to which a particular protein is bound. How-
ever, because of limitations of the assays, it is difficult to identify
the exact site within the region to which the protein is bound.
Certain computational tools such as MEME (Bailey and Gribskov
1997) and AlignACE (Roth et al. 1998) (see Supplemental Table 1)
have proven useful in the follow-up analysis of ChIP–chip data.
In addition, other computational approaches have been applied
to ChIP–chip data. For example, MDScan (Liu et al. 2002) in-
volves an ab initio motif discovery method and applies a
Bayesian statistical score function to refine the candidate motifs
enumerated from a set of the top ChIP–chip sequences. Other
approaches combine the use of a weight matrix model, which
incorporates prior knowledge of PWMs, with statistical classifi-
cation methods to identify the TFBSs. To illustrate this point, a
CART model (classification and regression tree) has been used to
identify estrogen receptor ? target genes (Jin et al. 2004), and a
MARS model (MARSMotifs)—which uses multivariate adaptive
regression splines—was selected to discover liver target genes
(Smith et al. 2005). Hong et al. (2005) adopted a confidence-rated
boosting algorithm to discriminate positive and negative data by
taking advantage of the ChIP–chip technology, to distinguish a
set of positive data (binding sequences) from a set of negative
data (nonbinding sequences). Several studies have increased the
sensitivity of motif detection by building motif modules (cis-
regulatory modules) based on interacting motifs (Zhou and
Wong 2004; Gupta and Liu 2005; Wang et al. 2005; Li et al.
2006). Furthermore, integrating pattern detection of interacting
transcription factors, phylogenetic footprinting, and statistical
learning methods has provided a substantial increase in the
specificity of detecting estrogen receptor alpha (Cheng et al.
2006) and E2F1 target genes (Jin et al. 2006.).
An example of integrating ChIP–chip data with phyloge-
netic conservation and experimental analyses is shown in Har-
bison et al. (2004). The authors combined binding data from 203
transcriptional regulators in yeast assayed under more than one
growth condition. Six motif discovery methods (including MD-
Scan and MEME) were used to find highly significant motifs for
116 regulatory proteins. The process identified promoter archi-
tectures that give clues to regulatory mechanisms defined by the
presence of single or repetitive motifs, multiple occurrences of
motifs having mixed identities, and co-occurring motifs.
Databases and Web sites for genomic analysis
The databases listed in Supplemental Table 3 serve as repositories
for whole-genome high-throughput ChIP–chip binding data.
Flexible query and output options in these databases allow one to
filter data sets to meet user-specified thresholds (e.g., certain P-
values on ChIP–chip data), to pass data to interconnected data-
bases, and to retrieve the DNA sequences that underlie the re-
gions of interest. The Galaxy2 repository (Giardine et al. 2005;
Blankenberg et al. 2007) provides mathematical tools, known as
set operations, for use on any genomic data sets represented as
coordinate-based intervals. These include operations for intersec-
tion, union, subtraction, and complement. Importantly, the
server can handle extremely large data files. Additional tools in-
clude operations for finding all regions that are proximal to a
feature data set, merging regions that have overlapping coordi-
nates, and clustering regions that are located within a specified
distance. These tools were used to predict MEF2 and MyoD bind-
ing sites based on previous knowledge that these sites are known
to cluster (Fickett 1996). A complementary approach to predict-
ing target genes from genomic data sets aims to identify target
genes using a combination of ChIP–chip and gene expression
arrays (Kirmizis and Farnham 2004). Database repositories spe-
cializing in both of these data types include GEO (Barrett et al.
2005) and ArrayExpress (Brazma et al. 2003) along with others
listed in Supplemental Table 3.
After a set of binding sites and/or target promoters are ob-
tained, further analyses are used to place the information into a
wider context. Conservation information is available as align-
ments of the sequenced mammalian genomes at the UCSC Ge-
nome Browser, Vista, and Ensembl Web sites (Karolchik et al.
2003; Frazer et al. 2004; Birney et al. 2006). Additional align-
ments containing up to 25 mammalian sequences (including
pre-eutherian species) are available in the finished ENCODE and
ZOOSEQ target regions and are viewable at the Genome Browser.
Conversely, stand-alone tools for pairwise or multispecies align-
ments allow users to create statistically robust alignments of their
own target sequences (Brudno et al. 2003; Blanchette et al. 2004;
Cooper et al. 2005; for review, see Dubchak and Frazer 2003;
Frazer et al. 2003). Highly conserved genomic segments are typi-
cally embraced as candidates for experimental and computa-
tional predictions of transcription factor binding sites. Accord-
ingly, tools to detect conserved regions, such as Galaxy2, the
UCSC Table Browser (Karolchik et al. 2004), MCS Browser (Mar-
gulies et al. 2003), and ECR browser (Ovcharenko et al. 2004)
allow a user to define and extract conserved sequences from a
multi-species alignment. In addition to conservation, the Ge-
nome Browser provides predictive measures of regulatory regions
such as 5-way regulatory potential (RP) (Kolbe et al. 2004) and
Elnitski et al.
1460 Genome Research
on February 22, 2007 www.genome.orgDownloaded from
PhastCons scores (Siepel et al. 2005), both of which are useful for
identifying putative functional regions under selective con-
Finally, it is becoming clear that the collection and visual-
ization of data sets comprising both experimental and computa-
tional analyses from multiple independent research groups will
allow broader insights than individually analyzed data sources
(see Supplemental Table 3). Recent studies focused on the experi-
mental and computational analysis of one percent of the human
genome (ENCODE Consortium, in prep.) have led to the identi-
fication of a large number of novel regulatory elements initially
termed GEMMS (genomic elements identified by multiple meth-
ods). Visualization of such collected data can be performed using
the UCSC browser, which allows the display of information con-
cerning known and predicted genes, protein binding sites, pro-
moter activities, transcription factor motifs, sequence conserva-
tion, and DNaseI hypersensitivity sites. Continuing develop-
ments focused on the integration and dissemination of
combined experimental and computational information are
critical for the future.
In this review, we have attempted to demonstrate that the inter-
dependence of experimental and computational approaches al-
lows an iterative refinement process, with each side benefiting
from collaboration with the other. Experimentalists may choose
to begin a project with in silico analyses or to expand an experi-
mental observation into a genome-wide predictive analysis. Pro-
grammers need to verify predictions of binding sites and improve
their prognostic pipeline using experimental data. Although
many tools for predicting binding sites are available worldwide
via the Internet—thereby allowing universal implementation—a
lot of experimentalists are not well trained in the programming
skills needed for insightful application of the analysis tools. Simi-
larly, despite the availability of robust protocols for genome-scale
experimental identification of transcription factor binding sites,
these experiments are technically challenging and time consum-
ing. Because programmers are frequently more familiar with the
intricacies of tools for binding site prediction, and biologists are
better trained in the collection and interpretation of experimen-
tal data sets, collaborative interactions and cross training will
serve both communities well.
We thank the members of our laboratories for productive discus-
sions and anonymous reviewers for helpful comments. We
apologize to colleagues whose original contributions could not
be cited, given the space constraints. The Intramural Program of
the NIH, NHGRI, supports research in the laboratory of L.E.; S.J.
is a scholar of the Michael Smith Foundation for Health Research;
and P.J.F. and V.X.J. are supported by grants from the NIH
(CA45240, HG003129, and DK067889).
Allis, C.D. and Wu, C. 2003. Chromatin and chromatin remodeling
enzymes, Part A, Vol. 375. Academic Press.
Almer, A., Rudolph, H., Hinnen, A., and Horz, W. 1986. Removal of
positioned nucleosomes from the yeast PHO5 promoter upon PHO5
induction releases additional upstream activating DNA elements.
EMBO J. 5: 2689–2696.
Bai, Y., Ge, Q., Liu, Q., Li, T., Wang, J., and Lu, Z. 2005. A free-labeled
method for DNA-binding protein detection using a double-stranded
DNA microarray. J. Nanosci. Nanotechnol. 5: 1216–1219.
Bailey, T.L. and Elkan, C. 1995. The value of prior knowledge in
discovering motifs with MEME. Proc. Int. Conf. Intell. Syst. Mol. Biol.
Bailey, T.L. and Gribskov, M. 1997. Score distributions for simultaneous
matching to multiple motifs. J. Comput. Biol. 4: 45–59.
Barrett, T., Suzek, T.O., Troup, D.B., Wilhite, S.E., Ngau, W.C., Ledoux,
P., Rudnev, D., Lash, A.E., Fujibuchi, W., and Edgar, R. 2005. NCBI
GEO: Mining millions of expression profiles—database and tools.
Nucleic Acids Res. 33: D562–D566.
Bieda, M., Xu, X., Singer, M.A., Green, R., and Farnham, P.J. 2006.
Unbiased location analysis of E3F1-binding sites suggests a
widespread role for E2F1 in the human genome. Genome Res.
Birney, E., Andrews, D., Caccamo, M., Chen, Y., Clarke, L., Coates, G.,
Cox, T., Cunningham, F., Curwen, V., Cutts, T., et al. 2006. Ensembl
2006. Nucleic Acids Res. 34: D556–561.
Blanchette, M. and Sinha, S. 2001. Separating real motifs from their
artifacts. Bioinformatics 17: S30–S38.
Blanchette, M., Kent, W.J., Riemer, C., Elnitski, L., Smit, A.F., Roskin,
K.M., Baertsch, R., Rosenbloom, K., Clawson, H., Green, E.D., et al.
2004. Aligning multiple genomic sequences with the threaded
blockset aligner. Genome Res. 14: 708–715.
Blanchette, M., Bataille, A.R., Chen, X., Poitras, C., Laganiere, J.,
Lefebvre, C., Deblois, G., Giguere, V., Ferretti, V., Bergeron, D., et al.
2006. Genome-wide computational prediction of transcriptional
regulatory modules reveals new insights into human gene
expression. Genome Res. 16: 656–668.
Blankenberg, D., Taylor, J., Schenck, I., He, J., Zhang, Y., Ghent, M.,
Veeraraghavan, N., Albert, I., Miller, W., Makova, K., et al. 2007. A
framework for collaborative analysis of ENCODE data: Making
large-scale analyses biologist-friendly. Genome Res. (in press).
Bode, J., Henco, K., and Wingender, E. 1980. Modulation of the
nucleosome structure by histone acetylation. Eur. J. Biochem.
Boffelli, D., McAuliffe, J., Ovcharenko, D., Lewis, K.D., Ovcharenko, I.,
Pachter, L., and Rubin, E.M. 2003. Phylogenetic shadowing of
primate sequences to find functional regions of the human genome.
Science 299: 1391–1394.
Boffelli, D., Weer, C.V., Weng, L., Lewis, K.D., Shoukry, M.I., Pachter, L.,
Keys, D.N., and Rubin, E.M. 2004. Intraspecies sequence
comparisons for annotating genomes. Genome Res. 14: 2406–2411.
Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., Vilo, J.,
Abeygunawardena, N., Holloway, E., Kapushesky, M., Kemmeren, P.,
Lara, G.G., et al. 2003. ArrayExpress–a public repository for
microarray gene expression data at the EBI. Nucleic Acids Res.
Brudno, M., Do, C., Cooper, G., Kim, M.F., Davydov, E., NISC
Comparative Sequencing Program, Green, E.D., Sidow, A., and
Batzoglou, S.. 2003. LAGAN and Multi-LAGAN: Efficient tools for
large-scale multiple alignment of genomic DNA. Genome Res.
Buck, M.J. and Lieb, J.D. 2004. ChIP–chip: Considerations for the
design, analysis, and application of genome-wide chromatin
immunoprecipitation experiments. Genomics 83: 349–360.
Buck, M.J., Nobel, A., and Lieb, J. 2005. ChIPOTle: A user-friendly tool
for the analysis of ChIP–chip data. Genome Biol. 6: R97.
Bulyk, M.L., Gentalen, E., Lockhart, D.J., and Church, G.M. 1999.
Quantifying DNA-protein interactions by double-stranded DNA
arrays. Nat. Biotechnol. 17: 573–577.
Cawley, S., Bekiranov, S., Ng, H.H., Kapranov, P., Sekinger, E.A., Kampa,
D., Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A.J., et al.
2004. Unbiased mapping of transcription factor binding sites along
human chromosomes 21 and 22 points to widespread regulation of
noncoding RNAs. Cell 116: 499–509.
Cereghini, S., Saragosti, S., Yaniv, M., and Hamer, D.H. 1984.
SV40-?-globulin hybrid minichromosomes. Differences in DNaseI
hypersensitivity of promoter and enhancer sequences. Eur. J.
Biochem. 144: 545–553.
Chaya, D. and Zaret, K.S. 2004. Sequential chromatin
immunoprecipitation from animal tissues. Methods Enzymol.
Cheng, A.S.L., Jin, V.X., Fan, M., Smith, L.T., Liyanarachchi, S., Yan,
P.S., Leu, Y.W., Chan, M.W., Plass, C., Nephew, K.P., et al. 2006.
Combinatorial analysis of transcription factor partners reveals
recruitment of c-MYC to estrogen receptor ?-responsive promoters.
Mol. Cell 21: 393–404.
Chiaromonte, F., Weber, R.J., Roskin, K.M., Diekhans, M., Kent, W.J.,
and Haussler, D. 2003. The share of human genomic DNA under
selection estimated from human-mouse genomic alignments. Cold
Locating transcription factor binding sites
on February 22, 2007 www.genome.orgDownloaded from
Spring Harb. Symp. Quant. Biol. 68: 245–254.
Collins, F.S. 2003. Genome research: The next generation. Cold Spring
Harb. Symp. Quant. Biol. 8: 49–54.
Cooper, G.M., Stone, E.A., Asimenos, G., NISC Comparative Sequencing
Program, Green, E.D., Batzoglou, S., and Sidow, A. 2005.
Distribution and intensity of constraint in mammalian genomic
sequence. Genome Res. 15: 901–913.
Cooper, S.J., Trinklein, N.D., Anton, E.D., Nguyen, L., and Myers, R.M.
2006. Comprehensive analysis of transcriptional promoter structure
and function in 1% of the human genome. Genome Res. 16: 1–10.
Cora, D., Herrmann, C., Dieterich, C., Di Cunto, F., Provero, P., and
Caselle, M. 2005. Ab initio identification of putative human
transcription factor binding sites by comparative genomics. BMC
Bioinformatics 6: 110.
Crawford, G.E., Holt, I.E., Whittle, J., Webb, B.D., Tai, D., Davis, S.,
Margulies, E.H., Chen, Y., Bernat, J.A., Ginsburg, D., et al. 2005.
Genome-wide mapping of DNase hypersensitive sites using
massively parallel signature sequencing (MPSS). Genome Res.
de Wet, J.R., Wood, K.V., DeLuca, M., Helinski, D.R., and Subramani, S.
1987. Firefly luciferase gene: Structure and expression in mammalian
cells. Mol. Cell. Biol. 7: 725–737.
Dorschner, M.O., Hawrylycz, M., Humbert, R., Wallace, J.C., Shafer, A.,
Kawamoto, J., Mack, J., Hall, R., Goldy, J., Sabo, P.J., et al. 2004.
High-throughput localization of functional elements by quantitative
chromatin profiling. Nat. Methods 1: 219–225.
Down, T.A. and Hubbard, T.J. 2005. NestedMICA: Sensitive inference of
over-represented motifs in nucleic acid sequence. Nucleic Acids Res.
Drouin, R., Therrien, J.P., Angers, M., and Ouellet, S. 2001. In vivo DNA
analysis. Methods Mol. Biol. 148: 175–219.
Dubchak, I. and Frazer, K.A. 2003. Multi-species sequence comparison:
The next frontier in genome annotation. Genome Biol. 4: 122.
ENCODE Project Consortium. 2004. The ENCODE (ENCyclopedia Of
DNA Elements) Project. Science 306: 636–640.
Feng, J. and Villeponteau, B. 1992. High-resolution analysis of c-fos
chromatin accessibility using a novel DNase I-PCR assay. Biochim.
Biophys. Acta 1130: 253–258.
Fickett, J.W. 1996. Coordinate positioning of MEF2 and myogenin
binding sites. Gene 172: 19–32.
Frazer, K.A., Elnitski, L., Church, D.M., Dubchak, I., and Hardison, R.C.
2003. Cross-species sequence comparisons: A review of methods and
available resources. Genome Res. 13: 1–12.
Frazer, K.A., Pachter, L., Poliakov, A., Rubin, E.M., and Dubchak, I.
2004. VISTA: Computational tools for comparative genomics. Nucleic
Acids Res. 32: W273–W279.
Fried, M. and Crothers, D.M. 1981. Equilibria and kinetics of lac
repressor-operator interactions by polyacrylamide gel electrophoresis.
Nucleic Acids Res. 9: 6505–6525.
Frith, M.C., Fu, Y., Yu, L., Chen, J.F., Hansen, U., and Weng, Z. 2004.
Detection of functional DNA motifs via statistical
over-representation. Nucleic Acids Res. 32: 1372–1381.
Gadiraju, S., Vyhlidal, C.A., Leeder, J.S., and Rogan, P.K. 2003.
Genome-wide prediction, display and refinement of binding sites
with information theory-based models. BMC Bioinformatics 4: 38.
Galas, D.J. and Schmitz, A. 1978. DNase footprinting: A simple method
for the detection of protein-DNA binding specificity. Nucleic Acids
Res. 5: 3157–3170.
Garner, M.M. and Revzin, A. 1981. A gel electrophoresis method for
quantifying the binding of proteins to specific DNA regions:
Application to components of the Escherichia coli lactose operon
regulatory system. Nucleic Acids Res. 9: 3047–3060.
Gazit, B. and Cedar, H. 1980. Nuclease sensitivity of active chromatin.
Nucleic Acids Res. 8: 5143–5155.
Giardine, B., Riemer, C., Hardison, R.C., Burhans, R., Elnitski, L., Shah,
P., Zhang, Y., Blankenberg, D., Albert, I., Taylor, J., et al. 2005.
Galaxy: A platform for interactive large-scale genome analysis.
Genome Res. 15: 1451–1455.
Gibbons, F.D., Proft, M., Struhl, K., and Roth, F.P. 2005. Chipper:
Discovering transcription-factor targets from chromatin
immunoprecipitation microarrays using variance stabilization.
Genome Biol. 6: R96.
Gibbs, R.A., Weinstock, G.M., Metzker, M.L., Muzny, D.M., Sodergren,
E.J., Scherer, S., Scott, G., Steffen, D., Worley, K.C., Burch, P.E., et al.
2004. Genome sequence of the Brown Norway rat yields insights
into mammalian evolution. Nature 428: 493–521.
Glynn, E.F., Megee, P.C., Yu, H.G., Mistrot, C., Unal, E., Koshland, D.E.,
and Gerton, J.L. 2004. Genome-wide mapping of the cohesin
complex in the yeast Saccharomyces cerevisiae. PLoS Biol. 2: e259.
Goodwin, G.H., Nicolas, R.H., Cockerill, P.N., Zavou, S., and Wright,
C.A. 1985. The effect of salt extraction on the structure of
transcriptionally active genes; evidence for a DNaseI-sensitive
structure which could be dependent on chromatin structure at levels
higher than the 30 nm fibre. Nucleic Acids Res. 13: 3561–3579.
Gross, D.S. and Garrard, W.T. 1988. Nuclease hypersensitive sites in
chromatin. Annu. Rev. Biochem. 57: 159–197.
Gui, C.Y. and Dean, A. 2003. A major role for the TATA box in
recruitment of chromatin modifying complexes to a globin gene
promoter. Proc. Natl. Acad. Sci. 100: 7009–7014.
Gupta, M. and Liu, J.S. 2005. De novo cis-regulatory module elicitation
for eukaryotic genomes. Proc. Natl. Acad. Sci. 102: 7079–7084.
Hallikas, O., Palin, K., Sinjushina, N., Rautiainen, R., Partanen, J.,
Ukkonen, E., and Taipale, J. 2006. Genome-wide prediction of
mammalian enhancers based on analysis of transcription-factor
binding affinity. Cell 124: 47–59.
Hanlon, S.E. and Lieb, J.D. 2004. Progress and challenges in profiling
the dynamics of chromatin and transcription factor binding with
DNA microarrays. Curr. Opin. Genet. Dev. 14: 697–705.
Harbers, M. and Carninci, P. 2005. Tag-based approaches for
transcriptome research and genome annotation. Nat. Methods
Harbison, C.T., Gordon, D.B., Lee, T.I., Rinaldi, N.J., Macisaac, K.D.,
Danford, T.W., Hannett, N.M., Tagne, J.B., Reynolds, D.B., Yoo, J., et
al. 2004. Transcriptional regulatory code of a eukaryotic genome.
Nature 431: 99–104.
Harr, R., Haggstrom, M., and Gustafsson, P. 1983. Search algorithm for
pattern match analysis of nucleic acid sequences. Nucleic Acids Res.
Hebbes, T.R., Clayton, A.L., Thorne, A.W., and Crane-Robinson, C.
1994. Core histone hyperacetylation co-maps with generalized
DNaseI sensitivity in the chicken ?-globin chromosomal domain.
EMBO J. 13: 1823–1830.
Heisler, L.E., Torti, D., Boutros, P.C., Watson, J., Chan, C., Winegarden,
N., Takahashi, M., Yau, P., Huang, H., Farnham, P.J., et al. 2005.
CpG Island microarray probe sequences derived from a physical
library are representative of CpG Islands annotated on the human
genome. Nucleic Acids Res. 33: 2952–2961.
Hertz, G.Z. and Stormo, G.D. 1999. Identifying DNA and protein
patterns with statistically significant alignments of multiple
sequences. Bioinformatics 15: 563–577.
Hong, P., Liu, X.S., Zhou, Q., Lu, X., Liu, J.S., and Wong, W.H. 2005. A
boosting approach for motif modeling using ChIP–chip data.
Bioinformatics 21: 2636–2643.
Huber, B.R. and Bulyk, M.L. 2006. Meta-analysis discovery of
tissue-specific DNA sequence motifs from mammalian gene
expression data. BMC Bioinformatics 7: 229.
Jantzen, K., Fritton, H.P., and Igo-Kemenes, T. 1986. The DNaseI
sensitive domain of the chicken lysozyme gene spans 24 kb. Nucleic
Acids Res. 14: 6085–6099.
Jenuwein, T. and Allis, C.D. 2001. Translating the histone code. Science
Jin, V.X., Leu, Y.-W., Liyanarachchi, S., Sun, H., Huang, T.H.-M., and
Davuluri, R.V. 2004. Identifying estrogen receptor ? target genes
using integrated computational genomics and chromatin
immunoprecipitation microarray. Nucleic Acids Res. 32: 6627–6635.
Jin, V.X., Rabinovich, A., Squazzo, S.L., Green, R., and Farnham, P.J.
2006. A computational genomics approach to identify cis-regulatory
modules from chromatin immunoprecipitation microarray data—A
case study using E2F1. Genome Res. (this issue).
Jolly, E., Chin, C.S., Herskowitz, I., and Li, H. 2005. Genome-wide
identification of the regulatory targets of a transcription factor using
biochemical characterization and computational genomic analysis.
BMC Bioinformatics 6: 275.
Kang, S.H., Vieira, K., and Bungert, J. 2002. Combining chromatin
immunoprecipitation and DNA footprinting: A novel method to
analyze protein-DNA interactions in vivo. Nucleic Acids Res. 30: e44.
Karolchik, D., Baertsch, R., Diekhans, M., Furey, T., Hinrichs, A., Lu, Y.,
Roskin, K.M., Schwartz, M., Sugnet, C.W., Thomas, D.J., et al. 2003.
The UCSC Genome Browser Database. Nucleic Acids Res. 31: 51–54.
Karolchik, D., Hinrichs, A., Furey, T., Roskin, K., Sugnet, C., Haussler,
D., and Kent, W.J. 2004. The UCSC Table Browser data retrieval tool.
Nucleic Acids Res. 32: D493–D496.
Keles, S., van der Laan, M.J., Dudoit, S., Xing, B., and Eisen, M.B. 2003.
Supervised detection of regulatory motifs in DNA sequences. Stat.
Appl. Genet. Mol. Biol. 2: Article5.
Khokha, M.K. and Loots, G.G. 2005. Strategies for characterising
cis-regulatory elements in Xenopus. Brief Funct. Genomic Proteomic.
Kim, T., Barrera, L., Zheng, M., Qu, C., Singer, M., Richmond, T., Wu,
Y., Green, R.D., and Ren, B. 2005. A high-resolution map of active
promoters in the human genome. Nature 436: 876–878.
King, D.C., Taylor, J., Elnitski, L., Chiaromonte, F., Miller, W., and
Elnitski et al.
on February 22, 2007 www.genome.org Downloaded from
Hardison, R.C. 2005. Evaluation of regulatory potential and
conservation scores for detecting cis-regulatory modules in aligned
mammalian genome sequences. Genome Res. 15: 1051–1060.
Kirmizis, A. and Farnham, P.J. 2004. Genomic approaches that aid in
the identification of transcription factor target genes. Exp. Biol. Med.
Kirmizis, A., Bartley, S.M., and Farnham, P.J. 2003. Identification of the
polycomb group protein SU(Z)12 as a potential molecular target for
human cancer therapy. Mol. Cancer Ther. 2: 113–121.
Klug, J. 1997. Ku autoantigen is a potential major cause of nonspecific
bands in electrophoretic mobility shift assays. Biotechniques
Kolbe, D., Taylor, J., Elnitsk, I.L., Eswara, P., Li, J., Miller, W., Hardison,
R., and Chiaromonte, F. 2004. Regulatory potential scores from
genome-wide three-way alignments of human, mouse, and rat.
Genome Res. 14: 700–707.
Komura, J. and Riggs, A.D. 1998. Terminal transferase-dependent PCR: A
versatile and sensitive method for in vivo footprinting and detection
of DNA adducts. Nucleic Acids Res. 26: 1807–1811.
Kreiman, G. 2004. Identification of sparsely distributed clusters of
cis-regulatory elements in sets of co-expressed genes. Nucleic Acids
Res. 32: 2889–2900.
Krivan, W. and Wasserman, W.W. 2001. A predictive model for
regulatory sequences directing liver-specific transcription. Genome
Res. 11: 1559–1566.
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.
Lawson, G.M., Knoll, B.J., March, C.J., Woo, S.L., Tsai, M.J., and
O’Malley, B.W. 1982. Definition of 5? and 3? structural boundaries of
the chromatin domain containing the ovalbumin multigene family.
J. Biol. Chem. 257: 1501–1507.
Levine, M. and Tjian, R. 2003. Transcription regulation and animal
diversity. Nature 424: 147–151.
Li, Z., Van Calcar, S., Qu, C., Cavenee, W.K., Zhang, M.Q., and Ren, B.
2003. A global transcriptional regulatory role for c-Myc in Burkitt’s
lymphoma cells. Proc. Natl. Acad. Sci. 100: 8164–8169.
Li, H., Chen, H., Bao, L., Manly, K.F., Chesler, E.J., Lu, L., Wang, J.,
Zhou, M., Williams, R.W., and Cui, Y. 2006. Integrative genetic
analysis of transcription modules: Towards filling the gap between
genetic loci and inherited traits. Hum. Mol. Genet. 15: 481–492.
Lieb, J.D., Liu, X., Botstein, D., and Brown, P.O. 2001. Promoter-specific
binding of Rap1 revealed by genome-wide maps of protein-DNA
association. Nat. Genet. 28: 327–334.
Liu, X.S., Brutlag, D.L., and Liu, J.S. 2002. An algorithm for finding
protein-DNA binding sites with applications to
chromatin-immunoprecipation microarray experiments. Nat.
Biotechnol. 20: 835–839.
Liu, Y., Liu, X.S., Wei, L., Altman, R.B., and Batzoglou, S. 2004.
Eukaryotic regulatory element conservation analysis and
identification using comparative genomics. Genome Res.
Liu, X., Noll, D.M., Lieb, J.D., and Clarke, N.D. 2005. DIP-chip: Rapid
and accurate determination of DNA-binding specificity. Genome Res.
MacAlpine, D.M. and Bell, S.P. 2005. A genomic view of eukaryotic DNA
replication. Chromosome Res. 13: 309–326.
Mao, D.Y., Watson, J.D., Yan, P.S., Barsyte-Lovejoy, D., Khosravi, F.,
Wong, W.W., Farnham, P.J., Huang, T.H., and Penn, L.Z. 2003.
Analysis of Myc bound loci identified by CpG island arrays shows
that Max is essential for Myc-dependent repression. Curr. Biol.
Margulies, E.H., Blanchette, M., NISC Comparative Sequencing Program,
Haussler, D., and Green, E.D. 2003. Identification and
characterization of multi-species conserved sequences. Genome Res.
McArthur, M., Gerum, S., and Stamatoyannopoulos, G. 2001.
Quantification of DNaseI-sensitivity by real-time PCR: Quantitative
analysis of DNaseII-hypersensitivity of the mouse ?-globin LCR. Mol.
Biol. 313: 27–34.
Messina, D.N., Glasscock, J., Gish, W., and Lovett, M. 2004. An
ORFeome-based analysis of human transcription factor genes and
the construction of a microarray to interrogate their expression.
Genome Res. 14: 2041–2047.
Miller, W., Makova, K.D., Nekrutenko, A., and Hardison, R.C. 2004.
Comparative genomics. Annu. Rev. Genomics Hum. Genet. 5:
Moses, A., Chiang, D., and Eisen, M.B. 2004. Phylogenetic motif
detection by expectation-maximization on evolutionary mixtures. In
Pacific Symposium on Biocomputing, pp. 324-335, Hawaii.
Mueller, P.R. and Wold, B. 1989. In vivo footprinting of a muscle
specific enhancer by ligation mediated PCR. Science 246: 780–786.
Mukherjee, S., Berger, M.F., Jona, G., Wang, X.S., Muzzey, D., Snyder,
M., Young, R.A., and Bulyk, M.L. 2004. Rapid analysis of the
DNA-binding specificities of transcription factors with DNA
microarrays. Nat. Genet. 36: 1331–1339.
Noble, W.S., Kuehn, S., Thurman, R., Yu, M., and Stamatoyannopoulos,
J. 2005. Predicting the in vivo signature of human gene regulatory
sequences. Bioinformatics 21: i338–i343.
Oberley, M.J. and Farnham, P.J. 2003. Probing chromatin
immunoprecipitates with CpG-island microarrays to identify
genomic sites occupied by DNA-binding proteins. Methods Enzymol.
Odom, D.T., Zizlsperger, N., Gordon, D.B., Bell, G.W., Rinaldi, N.J.,
Murray, H.L., Volkert, T.L., Schreiber, J., Rolfe, P.A., Gifford, D.K., et
al. 2004. Control of pancreas and liver gene expression by HNF
transcription factors. Science 303: 1378–1381.
Onizuka, T., Endo, S., Hirano, M., Kanai, S., and Akiyama, H. 2002.
Design of a fluorescent electrophoretic mobility shift assay improved
for the quantitative and multiple analysis of protein-DNA
complexes. Biosci. Biotechnol. Biochem. 66: 2732–2734.
Ovcharenko, I., Nobrega, M.A., Loots, G.G., and Stubbs, L. 2004. ECR
Browser: A tool for visualizing and accessing data from comparisons
of multiple vertebrate genomes. Nucleic Acids Res. 32: W280–W286.
Ovcharenko, D., Jarvis, R., Hunicke-Smith, S., Kelnar, K., and Brown, D.
2005. High-throughput RNAi screening in vitro: From cell lines to
primary cells. RNA 11: 985–989.
Pavesi, G., Mereghetti, P., Mauri, G., and Pesole, G. 2004. Weeder Web:
Discovery of transcription factor binding sites in a set of sequences
from co-regulated genes. Nucleic Acids Res. 32: W199–W203.
Pedersen, J.T. and Moult, J. 1996. Genetic algorithms for protein
structure prediction. Curr. Opin. Struct. Biol. 6: 227–231.
Poulin, F., Nobrega, M.A., Plajzer-Frick, I., Holt, A., Afzal, V., Rubin,
E.M., and Pennacchio, L.A. 2005. In vivo characterization of a
vertebrate ultraconserved enhancer. Genomics 85: 774–781.
Prakash, A. and Tompa, M. 2005. Discovery of regulatory elements in
vertebrates through comparative genomics. Nat. Biotechnol.
Qian, J., Esumi, N., Chen, Y., Wang, Q., Chowers, I., and Zack, D.J.
2005. Identification of regulatory targets of tissue-specific
transcription factors: Application to retina specific gene regulation.
Nucleic Acids Res. 33: 3479–3491.
Qiu, P. 2003. Recent advances in computational promoter analysis in
understanding the transcriptional regulatory network. Biochem.
Biophys. Res. Commun. 309: 495–501.
Ren, B., Robert, F., Wyrick, J.J., Aparicio, O., Jennings, E.G., Simon, I.,
Zeitlinger, J., Schreiber, J., Hannett, N., Kanin, E., et al. 2000.
Genome-wide location and function of DNA binding proteins.
Science 290: 2306–2309.
Roth, F.P., Hughes, J.D., Estep, P.W., and Church, G.M. 1998. Finding
DNA regulatory motifs within unaligned noncoding sequences
clustered by whole-genome mRNA quantitation. Nat. Biotechnol.
Roulet, E., Fisch, I., Junier, T., Bucher, P., and Mermod, N. 1998.
Evaluation of computer tools for the prediction of transcription
factor binding sites on genomic DNA. In Silico Biol. 1: 21–28.
Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W.W., and
Lenhard, B. 2004. JASPAR: An open-access database for eukaryotic
transcription factor binding profiles. Nucleic Acids Res. 32: D91–D94.
Schneider, T.D. 2000. Evolution of biological information. Nucleic Acids
Res. 28: 2794–2799.
Schwob, E. 2004. Flexibility and governance in eukaryotic DNA
replication. Curr. Opin. Microbiol. 7: 680–690.
Shi, W., Levine, M., and Davidson, B. 2005. Unraveling genomic
regulatory networks in the simple chordate, Ciona intestinalis.
Genome Res. 15: 1668–1674.
Shin, J.T., Priest, J.R., Ovcharenko, I., Ronco, A., Moore, R.K., Burns,
C.G., and MacRae, C.A. 2005. Human–zebrafish non-coding
conserved elements act in vivo to regulate transcription. Nucleic
Acids Res. 33: 5437–5445.
Shiraki, T., Kondo, S., Katayama, S., Waki, K., Kasukawa, T., Kawaji, H.,
Kodzius, R., Watahiki, A., Nakamura, M., Arakawa, T., et al. 2003.
Cap analysis gene expression for high-throughput analysis of
transcriptional starting point and identification of promoter usage.
Proc. Natl. Acad. Sci. 100: 15776–15781.
Siddharthan, R., van Nimwegen, E., and Siggia, E. 2004. PhyloGibbs:
Incorporating phylogeny and tracking-based significance assessment
in a Gibbs sampler. In RECOMB Satellite Workshop on Regulatory
Siddharthan, R., Siggia, E.D., and van Nimwegen, E. 2005. PhyloGibbs:
A Gibbs sampling motif finder that incorporates phylogeny. PLoS
Comput. Biol. 1: e67.
Locating transcription factor binding sites
on February 22, 2007 www.genome.orgDownloaded from
Siemen, H., Nix, M., Endl, E., Koch, P., Itskovitz-Eldor, J., and Brustle,
O. 2005. Nucleofection of human embryonic stem cells. Stem Cells
Dev. 14: 378–383.
Siepel, A., Bejerano, G., Pedersen, J.S., Hinrichs, A.S., Hou, M.,
Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L.W., Richards, S., et
al. 2005. Evolutionarily conserved elements in vertebrate, insect,
worm, and yeast genomes. Genome Res. 15: 1034–1050.
Sikder, D. and Kodadek, T. 2005. Genomic studies of transcription
factor-DNA interactions. Curr. Opin. Chem. Biol. 9: 38–45.
Sinha, S., van Nimwegen, E., and Siggia, E. 2003. A probabilistic method
to detect regulatory modules. In Proceedings of the Eleventh
International Conference on Intelligent Systems for Molecular Biology, pp.
292–301, Brisbane, Australia.
Smith, A.D., Sumazin, P., Das, D., and Zhang, M.Q. 2005. Mining
ChIP–chip data for transcription factor and cofactor binding sites.
Bioinformatics 21: i403–i412.
Stormo, G.D., Schneider, T.D., Gold, L., and Ehrenfeucht, A. 1982. Use
of the ‘Perceptron’ algorithm to distinguish translational initiation
sites in E. coli. Nucleic Acids Res. 10: 2997–3011.
Strauss, W.M. 1996. Transfection of mammalian cells via lipofection.
Methods Mol. Biol. 54: 307–327.
Suzuki, Y., Yamashita, R., Nakai, K., and Sugano, S. 2002. DBTSS:
DataBase of human Transcriptional Start Sites and full-length
cDNAs. Nucleic Acids Res. 30: 328–331.
Tagle, D.A., Koop, B.F., Goodman, M., Slightom, J.L., Hess, D.L., and
Jones, R.T. 1988. Embryonic ? and ? globin genes of a prosimian
primate (Galago crassicaudatus). J. Mol. Biol. 203: 439–455.
Takemoto, T., Uchikawa, M., Kamachi, Y., and Kondoh, H. 2006.
Convergence of Wnt and FGF signals in the genesis of posterior
neural plate through activation of the Sox2 enhancer N-1.
Development 133: 297–306.
Tompa, M., Li, N., Bailey, T.L., Church, G.M., De Moor, B., Eskin, E.,
Favorov, A.V., Frith, M.C., Fu, Y., Kent, W.J., et al. 2005. Assessing
computational tools for the discovery of transcription factor binding
sites. Nat. Biotechnol. 23: 137–144.
Trinklein, N.D., Aldred, S.J., Saldanha, A.J., and Myers, R.M. 2003.
Identification and functional analysis of human transcriptional
promoters. Genome Res. 13: 308–312.
Tsien, R.Y. 1998. The green fluorescent protein. Annu. Rev. Biochem.
Tuerk, C. and Gold, L. 1990. Systematic evolution of ligands by
exponential enrichment: RNA ligands to bacteriophage T4 DNA
polymerase. Science 249: 505–510.
van Helden, J. 2003. Regulatory sequence analysis tools. Nucleic Acids
Res. 31: 3593–3596.
van Helden, J., Rios, A.F., and Collado-Vides, J. 2000. Discovering
regulatory elements in non-coding sequences by analysis of spaced
dyads. Nucleic Acids Res. 28: 1808–1818.
Vavouri, T. and Elgar, G. 2005. Prediction of cis-regulatory elements
using binding site matrices—the successes, the failures and the
reasons for both. Curr. Opin. Genet. Dev. 15: 395–402.
Vettese-Dadey, M., Grant, P.A., Hebbes, T.R., Crane-Robinson, C., Allis,
C.D., and Workman, J.L. 1996. Acetylation of histone H4 plays a
primary role in enhancing transcription factor binding to
nucleosomal DNA in vitro. EMBO J. 15: 2508–2518.
Vyhlidal, C.A., Rogan, P.K., and Leeder, J.S. 2004. Development and
refinement of pregnane X receptor (PXR) DNA binding site model
using information theory: Insights into PXR-mediated gene
regulation. J. Biol. Chem. 279: 46779–46786.
Wang, T. and Stormo, G.D. 2003. Combining phylogenetic data with
co-regulated genes to identify regulatory motifs. Bioinformatics
Wang, W., Cherry, J.M., Nochomovitz, Y., Jolly, E., Botstein, D., and Li,
H. 2005. Inference of combinatorial regulation in yeast
transcriptional networks: A case study of sporulation. Proc. Natl.
Acad. Sci. 102: 1998–2003.
Wasserman, W.W. and Fickett, J.W. 1998. Identification of regulatory
regions which confer muscle-specific gene expression. J. Mol. Biol.
Waterston, R.H., Lindblad-Toh, K., Birney, E., Rogers, J., Abril, J.F.,
Agarwal, P., Agarwala, R., Ainscough, R., Alexandersson, M., An, P.,
et al. 2002. Initial sequencing and comparative analysis of the
mouse genome. Nature 420: 520–562.
Weinmann, A.S. and Farnham, P.J. 2002. Identification of unknown
target genes of human transcription factors using chromatin
immunoprecipitation. Methods 26: 37–47.
Weisbrod, S. and Weintraub, H. 1979. Isolation of a subclass of nuclear
proteins responsible for conferring a DNaseI-sensitive structure on
globin chromatin. Proc. Natl. Acad. Sci. 76: 630–634.
Worton, R.G., Ho, C.C., and Duff, C. 1977. Chromosome stability in
CHO cells. Somatic Cell Genet. 3: 27–45.
Wright, W.E., Binder, M., and Funk, W. 1991. Cyclic amplification and
selection of targets (CASTing) for the myogenin consensus binding
site. Mol. Cell. Biol. 11: 4104–4110.
Wu, C. 1980. The 5? ends of Drosophila heat shock genes in chromatin
are hypersensitive to DNaseI. Nature 286: 854–860.
Xie, X., Lu, J., Kulbokas, E.J., Golub, T.R., Mootha, V., Lindblad-Toh, K.,
Lander, E.S., and Kellis, M. 2005. Systematic discovery of regulatory
motifs in human promoters and 3? UTRs by comparison of several
mammals. Nature 434: 338–345.
Yoo, J., Herman, L.E., Li, C., Krantz, S.B., and Tuan, D. 1996. Dynamic
changes in the locus control region of erythroid progenitor cells
demonstrated by polymerase chain reaction. Blood 87: 2558–2567.
Yuan, G.C., Liu, Y.J., Dion, M.F., Slack, M.D., Wu, L.F., Altschuler, S.J.,
and Rando, O.J. 2005. Genome-scale identification of nucleosome
positions in S. cerevisiae. Science 309: 626–630.
Zhou, Q. and Wong, W.H. 2004. CisModule: De novo discovery of
cis-regulatory modules by hierarchical mixture modeling. Proc. Natl.
Acad. Sci. 101: 12114–12119.
Zhu, Z., Shendure, J., and Church, G.M. 2005. Discovering functional
transcription-factor combinations in the human cell cycle. Genome
Res. 15: 848–855.
Elnitski et al.
on February 22, 2007 www.genome.orgDownloaded from