On the detection and refinement of transcription
factor binding sites using ChIP-Seq data
Ming Hu1,2, Jindan Yu3,4,5,6, Jeremy M. G. Taylor2,5, Arul M. Chinnaiyan3,4,5,7,8and
Zhaohui S. Qin1,2,*
1Center for Statistical Genetics,2Department of Biostatistics,3Michigan Center of Translational Pathology,
4Department of Pathology,5University of Michigan Comprehensive Cancer Center, University of Michigan, Ann
Arbor, Michigan 48109,6Division of Hematology/Oncology, Department of Medicine, Northwestern University,
Chicago, Illinois 60660,7Department of Urology, University of Michigan Medical School and8Howard Hughes
Medical Institute, Ann Arbor, Michigan 48109, USA
Received October 26, 2009; Revised and Accepted December 2, 2009
sequencing technologies has enabled genome-wide
detection of protein–DNA interactions with unprec-
edented sensitivity and specificity. This new tech-
in-depth analysis of transcription regulation. In this
study, we explore the value of using ChIP-Seq data
to better detect and refine transcription factor
binding sites (TFBS). We introduce a novel com-
putational algorithm named Hybrid Motif Sampler
(HMS), specifically designed for TFBS motif discov-
ery in ChIP-Seq data. We propose a Bayesian model
that incorporates sequencing depth information to
aid motif identification. Our model also allows
intra-motif dependency to describe more accurately
combines stochastic sampling and deterministic
‘greedy’ search steps into a novel hybrid iterative
scheme. This combination accelerates the compu-
tation process. Simulation studies demonstrate
favorable performance of HMS compared to other
existing methods. When applying HMS to real
ChIP-Seq datasets, we find that (i) the accuracy of
existing TFBS motif patterns can be significantly
improved; and (ii) there is significant intra-motif
dependency inside all the TFBS motifs we tested;
modeling these dependencies further improves
These findings may offer new biological insights
Accurately locating the transcription factor (TF)–DNA
interaction sites provides key insights into the delineation
of the underlying mechanisms of transcriptional regula-
tion. By exploiting the fact that binding sites for a
specific TF often show sequence specificity, computational
prediction of TF binding sites, or motif finding, has
become an indispensible tool for functional genomics
research. A variety of different software programs have
been developed for motif-finding (1–7) [see Tompa et al.
(8) for a review of this topic].
The input data for computational motif-finding algo-
rithms are DNA sequences believed to be enriched by
the TF binding sites, or motifs. Typical sources of the
input data are known co-regulated genes (7), phylogenetic
conservation (9) or results from functional genomics
experimental assays (1,10–12). For the latter, continually
ChIP-Seq (17–20), offer rapidly improving opportunities
for motif finding.
ChIP-Seq, or chromatin immunoprecipitation (ChIP)
(21,22) followed by ultra-high-throughput sequencing,
genome-wide mapping of protein–DNA interactions and
histone modifications (17–20). Through direct sequencing
of all DNA fragments from ChIP assays, ChIP-Seq can
reveal protein–DNA interaction sites across the entire
*To whom correspondence should be addressed. Tel: +1 734 763 5965; Fax: +734 615 8322; Email: firstname.lastname@example.org
Nucleic Acids Research, 2010, Vol. 38, No. 7Published online 6 January 2010
? The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
genome, thus building a comprehensive and high-
resolution interactome map for DNA-binding proteins
From past experience, exploiting the quantitative infor-
mation provided by high-throughput genomic assays
allows scientists to develop more effective motif-finding
algorithms. Improvements in motif detection have been
ChIP-chip (1,12) data. The newly emerged ChIP-Seq tech-
nology has demonstrated remarkable sensitivity and
specificity in identifying protein–DNA binding loci
across the entire genome with high resolution and few
constraints. In excess of 10000 DNA sequences are
routinely being identified as candidates that potentially
harbor protein–DNA interaction sites of interest. Such
information provides an exciting new venue for motif dis-
covery and refinement.
A de novo motif search is a natural follow-up to the
identification of ChIP-enriched regions. Not only it is
required when the TF binding motif pattern is unknown,
but it is also important in cases where TF and its canon-
ical binding motif pattern have been established. After
all, it is reassuring to be able to rediscover the known
TFBS motif pattern from the input sequences. More
importantly, most of the known TF binding motif
patterns stored in the various TF binding motif databases
or reported in the literature are defined based on limited
numbers of experimentally verified TF–DNA interaction
sites. Many of these motif patterns could be inaccurate
due to limited experimental data. Performing a de novo
motif search on a large number of ChIP-Seq binding
sites has the potential to refine the motif patterns of the
While a variety of methods that attempt to identify
ChIP-enriched genomic regions from ChIP-Seq experi-
ments (also called ‘peak calling’) have been described
(23–31), little has been developed utilizing ChIP-Seq
data for motif finding.
Probability model-based de novo motif finding algo-
rithms such as MEME have demonstrated a high level
of sensitivity and specificity (2–5,32–36). However, since
these methods were developed when only a handful of
motif-enriched sequences were available, they do not
work well when analyzing large sets of sequences identified
by ChIP-Seq. There are at least two limitations that affect
their performance: (i) the requirement for going through
all bases in all sequences using time-consuming iterative
procedures means that these methods do not scale well for
the analysis of large sets of sequences generated from
ChIP-Seq; (ii) existing methods, which only consider
sequence data, are unable to fully utilize the rich informa-
tion produced from ChIP-Seq. Overlooked information
includes the sequencing depth along the ChIP-enriched
regions and the overall significance of ChIP-enrichment
for each sequence. ‘Sequencing depth’ refers to the
number of ChIP DNA fragments that cover each base.
Currently, a common practice for performing motif
finding on ChIP-Seq data is to use existing motif-finding
tools on a subset of all sequences (e.g. the top 500
sequences or top 10% of all such sequences) (25,26).
This is sub-optimal because the small sample size may
lead to an inaccurate motif pattern and the selection of
top sequences tends to result in motif patterns with
inflated information content.
We believe that a more desirable approach is to
develop algorithms that can utilize all of the sequence
information generated from ChIP-Seq. Not only will
this strategy result in the identification of more accu-
rate motif patterns, but also the dramatically increased
number of in vivo binding sites revealed by ChIP-Seq
permits the use of probability models that are more
multinomial models (34) for characterizing the motif
To address these limitations and fully exploit the infor-
mation provided by ChIP-Seq experiments, we develop a
novel model-based motif-finding algorithm named the
Hybrid Motif Sampler (HMS). It is specifically designed
for ChIP-Seq data and utilizes all ChIP-enriched regions
identified from ChIP-Seq experiments. In this algorithm,
we propose a new probability model that considers both
DNA sequence and sequencing depth information that is
available from ChIP-Seq experiment. It also allows
inter-dependent positions within a motif to be identified.
In addition, we propose a novel hybrid searching scheme
to significantly expedite the iterative procedure. Our algo-
rithm is capable of processing tens of thousands of
sequences and is much faster than the established de
novo motif-finding tools such as MEME.
MATERIAL AND METHODS
The statistical model
Let R ¼ ðR1, ...,RJÞ denote a set of J sequences (e.g.,
DNA sequences in ChIP-enriched regions identified by
ChIP-Seq) of length L1, ...,LJ. We initially assume that
every sequence Rj contains exactly one binding site. In
addition, the vector that is formed by the start locations
is referred to as the alignment variable, denoted as
A ¼ ða1, ...,aJÞ
2, ...,J. Here, w is the motif width and is assumed to
be known. Given A and w, the aligned sequence motif
can be represented by a four by w matrix. Each column
of the matrix stores the frequency counts of the four
types of nucleotides. Liu et al. (34) proposed the
preferences shown in such matrices. The product-
multinomial model has been widely used in EM-based
(4,32) and Gibbs sampler-based (3,33,35) motif find-
ing algorithms. Let H ¼ ðh1, ...,hwÞ, hi represent the
nucleotide preference at the i-th position of the motif
and let the probability vector h0represent the nucleotide
preference for non-motif positions in these sequences.
Each of the hi,i ¼ 0,1,2, ...,w is a probability vector of
length four. For notational simplicity, we use integers 1, 2,
3 and 4 to represent the four types of nucleotides A, C, G
For de novo motif finding, the parameter of main
interest in our model is the alignment variable A.
Lawrence et al. (3) proposed a Gibbs sampler-based
where1 ? aj? Lj? w þ 1,j ¼ 1,
to model thenucleotide
Nucleic Acids Research,2010, Vol.38, No. 72155
approach in which the posterior distribution for alignment
ajcan be expressed as:
pðaj¼ ljh0,H,Rj,A?jÞ /
where A?j¼ ða1, ...,aj?1,ajþ1, ...,aJÞ and the functions
hkðÞ,k ¼ 1,2,3,4, returns the number of nucleotides of
For h0and H, as an alternative to sampling them from
posterior conditional distributions as in a standard Gibbs
sampler, one can use the predictive updating technique
(34) to integrate them out. Alternatively, the posterior
means can be used to approximate the updated param-
eters during iteration. More details of these strategies
can be found in Liu et al. (34).
Allowance for some sequences that do not contain
In the model above, we assume that every sequence Rj
contains exactly one motif. However, this is not the case
in real data. To increase specificity, as most motif-finding
algorithms have done, it is highly desirable that we gener-
alize the method to allow some sequences to be motif-free.
We introduce a binary indicator variable Ij, where Ij¼ 1
indicates that Rj contains at least one motif, and Ij¼ 0
otherwise. In the algorithm, Ijis set to 1 if the average of
likelihood ratios observing the motif in the sequence Rj,
denoted as zj, is greater than 1. i.e.
After updating Ij, we only conduct motif search on the
sequences with Ij¼ 1.
Modeling sequencing depth
Lj? w þ 1
The model described in equation (1) assumes that binding
motifs are equally likely to occur at all positions in each
sequence. This is reasonable when no information beyond
the input DNA sequences is considered. However, such a
model is no longer sufficient for analyzing ChIP-Seq data
since additional information beyond the DNA sequences
is available and should be incorporated. In particular, it
has been shown that the sequencing depth in each
ChIP-enriched region is indicative of the motif location
shows that the majority of motifs are tightly packed
near the peak summit (the location inside each peak
with the highest sequence coverage depth), especially for
the highly significant peaks.
To capitalize on the extra information provided by
informative prior distribution of the motif location
based on the sequencing depth. There are multiple ways
to assign such priors. The simplest strategy is to make the
prior probabilities directly proportional to the sequencing
depth in each sequence. However, since sequencing depth
is affected by many factors, such as local GC content,
using a prior distribution like this may result in ‘over
fit’. Alternatively, a parametric distribution that approxi-
mates the sequencing depth can be used to obtain the prior
probabilities. In this study, we set the prior probabilities to
be proportional to a discretized Student’s t-distribution
with three degrees of freedom and rescaled such that the
prior probabilities form a step function with a fixed
step-size (25bp in this study). The prior probabilities are
symmetric and centered at the peak summit (most
peak-calling software provides the exact location of the
summit). Specifically, the prior probabilities that a motif
starts at position l can be expressed as:
Where t3 is the probability density function of the
Student’s t-distribution with three degrees of freedom, sj
is the location of the peak summit, w is the motif width,
u is the step size (25bp in this study) in the step function
and int ½?? returns the integer part of a real number. Please
see Supplementary Figure S2 for an illustration of the
prior probabilities. The reason that we choose Student’s
t-distribution instead of a normal distribution is because it
better allows for some motif locations to be far from the
peak (the standard deviation of Student’s t-distribution
with three degrees of freedom is 1.73, compared to one
for standard normal distribution).
pðaj¼ lÞ / t3 intjl þ w=2 ? sjj þ u=2
Modeling intra-motif dependency
The classical product-multinomial model assumes that the
positions within the motif are independent of each other
(37). However, recent studies indicate that some positions
of TF binding motifs exert an inter-dependent effect on
the binding affinities of TF’s (38–41). These findings imply
that the commonly used product-multinomial model may
be too simplistic in characterizing the binding sites.
Models that allow for dependent positions likely will
provide a better fit of the data. The significantly increased
quantity of motifs identified by ChIP-Seq enables us to
consider a more sophisticated model that can take into
account the intra-motif dependency.
There have been numerous attempts to incorporate into
models the inter-dependency among positions within a
motif. King and Roth (42) introduced a non-parametric
dependencies among positions. Barash et al. (43) sug-
gested multiple Bayesian network models to represent
dependencies among motif positions. Zhou and Liu (44)
proposed a generalized weight matrix model in which a
16-component multinomial model is used to model two
dependent positions jointly.
2156Nucleic Acids Research, 2010,Vol.38, No. 7
Here, we extend the generalized weight matrix model of
Zhou and Liu. To take greater advantage of the abundant
sequence information made available by the ChIP-Seq
technology, our model allows up to three positions to be
Detection of dependent positions
Given a set of aligned putative binding motifs, our goal
is to identify positions that show inter-dependency.
Here, ‘inter-dependency’ implies that the frequency of
certain nucleotide combinations spanning multiple posi-
tions deviates fromthe
assuming an independent motif model. As an exam-
ple, for a pair of positions, if the frequency of a particu-
lar dinucleotide, say AC, is much higher or lower than
the product of frequency of nucleotide A in the first
position and frequency of nucleotide C in the second
position, we concludethat the
A variety of methods have been proposed in the litera-
ture to search for such inter-dependent positions. Barash
et al. (43) applied machine learning approaches to infer the
structure of a Bayesian network that best represents the
underlying motif. Zhou and Liu (44) proposed a
Metropolis-type iterative procedure to identify pairs of
inter-dependent positions. Given the abundant motif
data from ChIP-Seq, we implement a comprehensive
search strategy to go through all pairs of positions
within the motif to determine whether there is evidence
of dependency. To be specific, for any two positions i
and j among wðw ? 1Þ=2 possible pairs, we first obtain
probability estimates of the 16 dinucleotides assuming
either a 16-component multinomial model (dependent)
or the product of two four-component multinomial
models (independent). Let the number of motifs be
represented by M. The term gxðriÞ represents the number
of motifs whose i-th position is occupied by nucleotide
x and the term gxyðri,rjÞ represents the number of
motifs whose i-th and j-th positions are occupied
by nucleotides x and y, respectively. The probability
^ ?xðriÞ ¼ gxðriÞ=M and
tively. We then calculate the Hamming distance between
the two sets of estimates as
two positions are
^ ?xyðri,rjÞ ¼ gxyðri,rjÞ=M, respec-
^ ?xyðri,rjÞ ? ^ ?xðriÞ^ ?yðrjÞ
Under the hypothesis that the two positions are indepen-
dent, we expect that distance dij¼ 0, excluding sampling
variability; larger dijindicates stronger inter-dependency
between positions i and j. In this study, we designate posi-
tions i and j to be dependent if dij>0.2. The threshold is
determined from the empirical null distribution of dijinfer
through simulations. More details can be found in the
We take a Bayesian approach and consider two different
models to describe the motif pattern. In the first one, we
assume all positions within the motif are independent.
There are two sets of parameters in this model: alignment
variable A and multinomial distribution probability
hi,i ¼ 0,1, ...,w.
for A are multinomial with probabilities defined as in
equation (3). Adopting a conjugate prior distribution for
each hi, which is Dirichletð?0,1, ...,?0,4Þ, the posterior
probabilities that a motif starts at position l can be
As suggested in Liu et al. (34), the above conditional dis-
tribution can be closely approximated by replacing ?ikby
its posterior mean given the current alignment vector A?j:
^?ik¼hkðr?j,A?jþi?1Þ þ ?0,k
For background (non-motif) regions, it has been shown
that employing a Markov model to capture weak depen-
dency in background DNA sequences improves the sensi-
tivity and specificity of motif finding compared to an
independent model in equation (1). In this study, we use
a third-order Markov model as in Liu et al. (2) to charac-
terize the background sequences. Under such a model,
the probability of observing DNA sequence fragment
PðBackgrounds,tÞ ¼ Pðrs,tÞPðrs,tþ1jrs,tÞPðrs,tþ2jrs,tþ1,rs,tÞ
In this background model, the 3 ? 43¼ 192 conditional
sequences downloaded from UCSC genome browser
website. The dataset contains 5kb upstream sequences
of annotated transcription starts for all RefSeq genes
with annotated 50-UTRs.
After incorporating these modifications, the complete
posterior distribution for aj¼ l becomes
pðaj¼ ljh0,H,Rj,A?jÞ / Ifzj>1g
In the second model, we consider intra-motif dependency.
Within the motif, we assign positions into two disjoint
groups: groups of independent positions S and groups of
dependent position pairs P where P ¼ fði,jÞ : dij> 0:2g. By
modeling dependent positions jointly, the probability
Nucleic Acids Research,2010, Vol.38, No. 72157
‘matrix’ H becomes an amalgamation of vectors of
length four (modeling single positions) and vectors of
length 16 (modeling pairs of dependent positions).
The prior distributions for the two types of hj ’s are
Dirichletð?0, 1, ...,?0,4Þ and Dirichletð?0, 1, 1,...,?0,1,4,
?0, 2, 1, ...,?0, 4, 4Þ respectively. The complete posterior
distribution for aj=l in the dependent model is
pðaj¼ ljh0,H,Rj,A?jÞ /Ifzj>1g? U ? V ? pðaj¼ lÞ
Here the counting function hk1k2ðÞ, whose argument is a
set of positions, counts the frequency of the 16
dinucleotides for a pair of positions within the motif.
The above model can be extended easily to allow
three-way inter-dependent positions.
Acceleration via prioritized hybrid Monte Carlo
To streamline this motif-finding algorithm in order to
handle a large number of input sequences, we develop a
prioritized hybrid strategy to increase computation speed
with only minimal if any sacrifice in accuracy. Unlike a
standard Gibbs sampler where motif alignment variables
are sampled stochastically from all sequences, only a small
proportion, ?, of all sequences are subjected to stochastic
sampling. For the remaining sequences, we select the
alignment variable deterministically by identifying the
position that corresponds to the highest probability as
given by equation (8) or (9). Since the deterministic
approach is much faster than the stochastic one and the
proportion ? we use is often quite small ( ? ?10%), this
hybrid strategy is much faster than the standard Gibbs
motif sampler (3).
For each iteration, the proportion of sequences under-
going stochastic search is constant, but a different set of
sequences is selected each time. We have automated the
process of selecting a subset of sequences for stochastic
search. All the sequences identified from the ChIP-Seq
experiment are rank-ordered according to their ChIP-
enrichment. Assume we run N iterations in each Gibbs
sampler. In the i-th iteration, we sample a fixed number
of ??J sequences from a multinomial distribution mult
ðJ,pi1, ...,piJÞ. At the beginning of the iteration, we use a
monotonically decreasing triangle probability distribu-
tion, which assigns higher probability to sequences with
higher ChIP-enrichment. As the iteration proceeds, the
slope of the triangle gradually becomes flatter so that
the oversampling of higher ChIP-enriched sequences
diminishes. In the last iteration, the distribution becomes
uniform. For the i-th iteration, we have
pij/ cij¼ J ? j þ 1 ?J=2 ? j þ 1
N ? 1
i ¼ 1, ...,N;j ¼ 1, ..., J:
? ði ? 1Þ,
We have developed a software program that implements
the algorithms described in this manuscript. The HMS
program is a Gibbs sampler type iterative procedure. To
reduce the possibility that the Markov chain converged to
a local mode, we run multiple Markov chains and choose
the motif pattern that corresponds to the highest likelihood
as the final motif pattern. The number of parallel chains
and the number of complete iterative cycles within each
chain are specified by users. Within each chain, the
iterative procedure can be broken down into three steps.
In the first step, we use a traditional product multinomial
model in which all positions are assumed independent of
each other. We further assume every sequence contain one
motif. In the second step, we again assume all positions
are independent, but we allow some sequences to be
motif-free. In the final step, we adopt the generalized
motif model that allows intra-motif dependency. The
HMS program, including the source code is freely available
Performance evaluation using simulated data
In the simulation study, we are interested in evaluating the
performance of various de novo motif finding algorithms
from two perspectives: first, the number of times a
program successfully detects the motif inserted into each
of the 100 simulated datasets; second, the accuracy of the
inferred motif pattern given that the motif has been found.
For the former, since we know the true location of all
inserted motifs in the simulated datasets, we are able
to directly verify whether each motif site predicted by
the testing software is correct. Within each simulated
dataset, we declare that the inserted motif is found if the
proportion of sequences in which the program correctly
identifies the true motif location is greater than 20%.
For the latter, we measure the accuracy of an inferred
motif pattern by calculating the average Hamming
distance between the true probability matrix H and its
prediction denoted as^H :
Small h indicates close resemblance of the predicted motif
pattern to the truth.
Performance evaluation using real data
Given a set of sequences identified by ChIP-Seq, we want
to discern which de novo motif-finding algorithm produces
a more accurate motif pattern. Since the exact true motif
pattern is unknown, we use motif enrichment as the crite-
rion. We assume that among multiple motif patterns, the
one that is most enriched in the ChIP-Seq-identified
regions relative to random controls is closest to the true
We use a cross-validation scheme to assess motif enrich-
ment. The original dataset is equally divided into halves: a
training set and a testing set. The input sequences are
restricted to within 200bp in length and centered at the
2158 Nucleic Acids Research, 2010,Vol.38, No. 7
peak summit (?100bp toward each side of the peak
summit). For the testing set, we create a control set
composed of randomly selected DNA promoter sequences
(within 5kb upstream of the transcription start site) as in
Zhou and Liu (44) matched by number of sequences and
length of each sequence. We run each motif-finding
program on the training set to identify the motif pattern,
and then utilize this pattern to scan both the testing and
the corresponding control sets to assess how many
sequences contain the motif. We employ a set of signifi-
cance thresholds and calculate the corresponding empiri-
cal false discovery rate (FDR) (45) and motif enrichment,
as measured by chi-squared test statistics for a 2 ? 2 con-
tingency table. The empirical FDR is estimated by
dividing the number of control sequences that contain
the motif by the number of testing sequences that
contain the motif. We repeat the scheme five times for
each dataset and report the average test statistics corre-
sponding to each FDR level.
We plot the curves of the empirical FDR versus the
chi-squared test statistics when the empirical FDR is
between 0 and 0.2. To accomplish this, we equally divide
the empirical FDR into ten consecutive windows and cal-
culate the mean of the chi-squared test statistics from five
cross validations (when the corresponding empirical
FDRs fall into the same window). Since the curve repre-
senting the most enriched motif pattern will be the highest,
we use area under the curve (AUC) as a quantitative
assessment of the overall motif enrichment. Higher AUC
indicates further motif enrichment.
Estrogen receptor ChIP-Seq experiment on MCF7 cells
To test the algorithms in a real setting, we have conducted
a ChIP-Seq experiment to survey genome-wide binding of
estrogen receptor (ER) on the MCF-7 breast cancer cell
line. ER is a hormonal TF that, when liganded by
estrogen, binds specially to estrogen response elements
(ERE) and plays a critical role in breast cancer develop-
ment. Identifying ER target genes and refining the ERE
motifs are thus of significant interest. A brief description
of the experimental protocol is shown in the next para-
graph. More details can be found in the Supplementary
Briefly, MCF-7 cells were grown in RPMI media sup-
plemented with 10% FBS to 50% confluence. The cells
were then hormone-starved for three days prior to treat-
ment of the vehicle control or 10nM b–estradiol for
45min. The cells were then harvested for ChIP analysis
using an antibody against estrogen receptor (ER)-a
(sc-543x, Santa Cruz) or against IgG. The ChIP-enriched
DNA was evaluated for significant enrichment of positive
control genes and then subjected to ChIP-Seq sample
preparation and short-read sequencing using Illumina
Genome Analyzer (Illumina Inc., San Diego, CA, USA)
sequencing images were analyzed using the Illumina
analysispipeline, and the
subsequently aligned to the human reference genome
(NCBI v36, hg18) using ELAND software (Illumina
sequencing reads that are uniquely mapped to the
human reference genome with up to two mismatches
were included for further analysis as delineated in this
study. We have submitted ER ChIP-Seq data (raw and
processed) into the GEO database; the accession number
of this dataset is GSE19013. We used the HPeak software
program, a HMM-based peak calling program developed
by our group, to define the ChIP-enriched regions. Details
of the HPeak software program can be found in the
Independent motif models. The goal of this simulation
study was to evaluate the ability of HMS to identify the
correct motif patterns. We use the default setting for HMS
which adopts the informative prior and allows intra-motif
dependency. For comparison, we also tested a simpler
version of HMS that assumes all positions are indepen-
dent. In addition, we applied two established motif-finding
software tools, MDscan (1) and MEME (4) on the same
sets of simulated data. Following the simulation scheme of
Liu et al. (1), four motif models were manually created
(Supplementary Table S1A), representing two different
motif widths (8bp and 16bp), and two different degrees
of conservation measured by information content (1.42
and 0.93). The information content is defined as:
where pijis the proportion of base j at the motif position i.
Information content ranges from 0 to 2, reflecting the
weakest to the strongest motifs. Finally, two different
motif abundance schemes (Supplementary Table S1B)
were considered for a total of eight combinations in the
simulation study. The eight simulation settings covered a
wide range of scenarios. The combination of short motif
width, weak motif information content and low motif
abundance was the most challenging.
For each setting, we simulated 100 test datasets. Each
dataset contains 3000 sequences of 200bp in length. To
mimic real human data, all the sequences were generated
from a third-order Markov model with parameters
estimated from the collection of 5kb promoter sequences
of annotated genes in the human genome. Hypothetical
motifs were generated from product multinomial models
with specified length and information content. The pro-
portion of sequences that contained a motif followed one
of the two abundance schemes mentioned in the previous
paragraph. We assumed that each sequence contained at
most one motif.
We next derived the empirical distribution from real
ChIP-Seq data of CTCF and NRSF of the motif start
locations in a 200bp window centered at the peak
summit. We strategically inserted the motifs in these
sequences following this empirical distribution. As a con-
sequence, the motif locations were biased toward the
Nucleic Acids Research,2010, Vol.38, No. 7 2159
center of the sequence, which was assumed to be the
location of the peak summit.
We applied MDscan, MEME and HMS to every
dataset. Two versions of HMS were used in the compar-
ison. One assumed an informative prior (proportional to a
discretized and rescaled Student’s t-distribution with three
degrees of freedom) that favored motif start locations near
the peak. The other, denoted as HMS_uniform, assumed a
uniform prior for the motif start location throughout the
genome. As described in the ‘Materials and Methods’
section, we used the successful motif detection rate and
the accuracy of predicted motif pattern as measurements
For the motif detection rate, both versions of HMS
achieved perfect results in all eight simulation settings.
MEME and MDscan achieved perfect results in six and
four settings, respectively. MEME achieves equal or
higher detection rate than MDscan in all but one setting
(Supplementary Table S2A).
We next compared performance on motif pattern pre-
diction accuracy. The prediction accuracy is defined as the
average Hamming distance between predicted and true H
for each method and each dataset. See equation (11) in the
‘Material and Methods’ section for the expression for the
average Hamming distance. To compare methods, within
each simulation setting, we performed a paired t-test
between the average Hamming distances obtained using
HMS and that of a competing method (HMS_uniform,
MEME and MDscan). Among the 100 datasets, we only
considered the ones in which all methods successfully
detected the right motif. Significantly smaller average
Hamming distance (P-value<0.01) was observed in six
out of eight simulation settings when comparing HMS
to MEME, and in seven out of eight settings when
comparing HMS to MDscan (Figure 1A, 1B and
Supplementary Table S2A). In addition, we found that
adopting the informative prior for the proposed HMS
method results in more accurate motif pattern prediction
Figure 1. Performance comparison on simulated data with independent and dependent motif model. The y-axis represents the difference between two
sets of average Hamming distances resulted from two different motif finding methods. The error bars represent the standard deviation of the
difference between two sets of average Hamming distances across 100 simulated datasets. (A) Independent, motif width=8bp. (B) Independent,
motif width=16bp. (C) Dependent, motif width=8bp. (D) Dependent, motif width=16bp.
2160 Nucleic Acids Research, 2010,Vol.38, No. 7
in all eight simulation settings than when using the
uniform prior (Supplementary Table S2A).
Inter-dependent motif models. We next conducted simula-
tion studies to evaluate the performance of HMS when
some positions within the motif showed inter-dependency.
In our simulation, dependency was added to two pairs of
positions in the 8bp motif model and four pairs of posi-
tions in the 16bp motif model. The joint distribution of
the pairs was taken from the one predicted for position
pair (1,2) in the E2F motif in Zhou and Liu (44) [as shown
in Figure 2(b) in the original paper, reproduced in
Supplementary Table S3].
In terms of motif detection, both versions of HMS
achieved perfect results in five out of the eight simulation
settings. MEME and MDscan achieved perfect results in
four and two settings respectively. Furthermore, HMS
and HMS_uniform reported
compared to MDscan and MEME in all simulation
settings. Our results also suggest that the HMS method
Figure 2. Illustration of the unbiased exhaustive survey of all pairs of positions within the ER motif to assess the strength of their dependency.
The differences in Hamming distance between the independent and dependent models are plotted in a heatmap. Larger differences (in dark red color)
indicate higher dependency. Dependent triples: position 2, 3 and 4, position 10, 11 and 12. Dependent pairs: position 18 and 19. Dependent positions
are illustrated in the box on the logo plot and the heatmap. The logo plots are generated using R package ‘seqLogo’. The subgraphs of
multi-nucleotide logo plots were generated using a program that we modified from SeqLogo (please see Section 5 in the Supplementary Data
for more details). To make the logo plots more readable, we changed the range for y-axis from 0–2 to 0–1 in the subfigures for multi-nucleotide
Nucleic Acids Research,2010, Vol.38, No. 72161
assuming informative prior performed better than the
(Supplementary Table S2B).
When comparing motif pattern prediction accuracy,
paired t-tests showed that the average Hamming distances
between the true and predicted probability matrix H were
significantly smaller for HMS than MEME and MDscan
in all testable simulation settings (MEME did not identify
the correct motif in any dataset under two simulation
settings; MDscan only identifies the correct motif in two
out of 100 datasets under one simulation settings.
Therefore no paired t-test is performed for those simula-
tion settings). The performance was similar between the
two versions of HMS (Figure 1C, 1D and Supplementary
To further evaluate the performance of HMS, we tested it
along with MDscan and MEME on four real ChIP-Seq
(neuron-restrictive silencer factor) (18), STAT1 (signal
transducer and activator of transcription protein 1) (19),
and CTCF (CCCTC-binding factor) (17), are publically
available. The ER dataset, however, is newly generated
for this study. The details of these four datasets can be
found in Table S4A and the Supplementary Data.
It is well known that some positions of TF binding motifs
exert an inter-dependent effect on the binding affinities of
TFs (38–41). However, due to the scarcity of the motifs
identified for each TF, it is difficult to detect those depen-
dent positions based solely on the limited motif sequence
data. With the introduction of the ChIP-Seq technology,
significantly more motif sequences can now be identified,
which gives us unprecedented opportunity to identify
strategy we outlined in the ‘Material and Methods’
section, we surveyed the four ChIP-Seq datasets used in
this study: NRSF, STAT1, CTCF and ER. The Hamming
f?i,i ¼ 1, ...,16g and f?i?j,i ¼ 1, ...,4,j ¼ 1, ...,4g were
presented in heatmaps (Figure 2 and Supplementary
Figure S3). The two sets of probabilities of the 16
dinucleotides were estimated under the independent and
dependent models respectively. Larger distance indicated
higher dependency. Using the Hamming distance of 0.2 as
the threshold, the number of dependent position pairs in
the motif ranged from three to five in the four real datasets
we studied (Supplementary Table S5). These pairs formed
two triplets in NRSF and CTCF motifs, one triplet and
one pair in the STAT1 motif and two triplets and one pair
in the ER motif. In particular, we found that positions 14
and 15 in the CTCF motif show exceptionally strong
dependency. The frequency of dinucleotides AC and GG
in these positions were below what would be expected if
they were independent. Similarly, the frequency for
dinucleotides AG and GC exceeded expectations. The dif-
ference in dinucleotide frequencies between independent
and dependent motif models exceeded 0.1 in all four
relevant cells in the four by four table (Supplementary
Table S6E). For other dependent position pairs we
identified, their dinucleotide frequencies were summarized
in Supplementary Table S6.
An interesting question is that, at position pairs that
show significant inter-dependency, whether any particular
dinucleotide displays significant enrichment or depletion.
To address this, in the 16 dependent position pairs
identified from the four motifs, the observed dinucleotide
frequencies were compared with the expected frequencies
under the assumption that the two positions are indepen-
dent. We noticed that some dinucleotides, such as TG,
CA andAG areover-represented,
dinucleotides, such as CG and TA, are under-represented
(Supplementary Figure S4). We found that the overall
dinucleotide preference pattern observed is consistent
with what has been reported in the literature (46).
Although our search strategy considers all pairs equally,
we found that the strongest intra-motif dependency
occurred at pairs of adjacent positions (Figure 2 and
Supplementary Figure S3). All 16 dependent position
pairs we identified in the four motifs were adjacent. This
is not surprising given the strong dependency in neighbor-
ing positions of DNA sequences. We also found that
strong intra-motif dependency often occurred in the
appeared to be ‘weak’ according to single-column motif
model (e.g. positions 10, 11 and 11 and 12 in the ER
TFBS motif profile comparison
Since both HMS and MDscan were able to rapidly process
tens of thousands of DNA sequences without sacrificing
much computation time, we fed the entire set of
ChIP-enriched regions into these two programs. In this
comparison, we only used the top 500 sequences as
input for MEME, since this program was not optimized
to analyze large numbers of DNA sequences. Next, we
applied MAST (47), a motif scanning software that is a
companion to MEME, to scan the remaining sequences
using the motif pattern identified by MEME. This is a
commonly used strategy in motif analysis (26). We also
included motif patterns either from the literature [CTCF
motif from Kim et al. (48)] or from MatBase (Genomatix,
Software GmbH, Munich, Germany) for comparison. We
used two different versions of HMS in our analysis: the
default setting allowing dependency among positions in
the motif and HMS_ind assumed all positions are inde-
pendent. Informative prior for alignment variable A is
used in both versions of HMS.
Although the four TFs and their binding motifs were
quite diverse, the motif pattern identification results were
remarkably consistent. The results from the ER dataset
are presented in Figure 3. Results from the three
publicly available ChIP-Seq datasets can be found in
Figure S5–7 in the Supplementary Data. Inspired by the
logo plot (49), we have developed a new plot that can be
used to visualize the dinucleotide and trinucleotide motif
pattern (Figure 2 and Supplementary Figure S3). This is
2162Nucleic Acids Research, 2010,Vol.38, No. 7
achieved by modifying the SeqLogo package found in the
BioConductor open source software package. More
details can be found in the Supplementary Data.
Figure 3A showed that de novo motif patterns identified
by MEME and HMS from the ER ChIP-Seq dataset.
Both patterns were similar to the ER motif stored in
MatBase. However, the motif pattern identified by HMS
was relativelyless conserved
content: HMS: 0.64, MEME: 0.71, Genomatix V$ER01:
1.00, Genomatix V$ER02: 1.03, Genomatix V$ER03:
0.89) but more palindromic (reverse compliment) than
the other motif patterns (Hamming distance between the
two 6-mer half sites after one half site was converted to its
Genomatix V$ER01: 4.00, Genomatix V$ER02: 2.18,
Genomatix V$ER03: 2.53). The results are encouraging
since it is well known that ER binds as a homo-dimer so
a palindromic pattern is expected in its TFBS motif.
An intriguing question is if dinucleotides also exhibit
the palindromic attribute. Among the five dependent
position pairs that HMS identified in the ER motif,
positionpairs 3–4 and
well-positioned to serve as a test case for the presence of
this palindromic attribute. This is because they are located
at the two ends of the ER half sites and do not overlap.
We found that the 16 dinucleotide frequencies for posi-
tions 3–4 matched almost perfectly with the corresponding
dinucleotide frequency at positions 18–19 after reverse
compliment transformation (Supplementary Table S7).
That is, we did observe dinucleotide dependency at the
two ends of the ER motif that exhibited palindromic attri-
bute. This led us to hypothesize that the palindromic
property, a hallmark of homer-dimer TF binding motifs,
can also be found in the dinucleotide level.
We did not include MDscan in our comparison since
MDscan was unable to consistently identify the consensus
ER motif pattern. In Figure 3B, we plotted the
chi-squared test statistics that measured the motif enrich-
ment at different levels of the empirical FDR. Comparing
AUC, we found that the motif patterns identified by
MEME and HMS showed much higher AUC than the
known motif patterns stored in MatBase. We believe
that the dramatically increased number of binding sites
identified by ChIP-Seq contributed to the refinement of
the motif pattern. MEME and a simplified version of
HMS (which used an independent mono-nucleotide
model, referred as HMS_ind) exhibited a similar result.
AUC for HMS, which allowed up to three-way inter-
dependency, was 16.7%
(Supplementary Table S8). The improvement is statisti-
cally significant when we repeated the cross-validation
steps 100 times and compared the AUCs from HMS and
MEME using a paired t-test (P-value<1.0e-5). We also
compared the proportions of ChIP-enriched sequences
that contain each of the ER motif patterns shown in
Figure 3A. We found that, under the two empirical
FDR levels (0.05 and 0.1), the proportion of motif
pattern defined by HMS is higher than that from
HMS_ind (by12.95% and
Comparing HMS to MEME under these empirical FDR
levels, the proportion of motif pattern defined by HMS
again is higher (by 19.52% and 9.20%, respectively).
These differences are again significant (P-value<1.0e-5)
when verifying with paired t-test comparing results from
100 cross-validations. In addition, we found that propor-
tions of motifs reported by HMS, HMS_ind and MEME
are much higher than those found in the MatBase
(Supplementary Table S9).
Among the other datasets (NRSF, STAT1 and CTCF),
HMS and MEME consistently identified the consensus
motif patterns in all trials. MDscan was able to
consistently identify only the NRSF motif, but not the
ones for the other two datasets. Again, we found that
the motif patterns identified by these de novo motif-finding
tools were more enriched than known motif patterns
found in the literature or MatBase. Motif patterns
defined by HMS consistently showed higher enrichment
andresulted in higher
(Supplementary Figures S4–6, Table S8). Motif patterns
HMS_ind and MEME at the same empirical FDR levels
(Supplementary Table S9). The performance differences
are significant except for the STAT1 motif.
Comparison to ChIP-chip data
In order to confirm that the higher enrichment of the
motif identified by HMS on ChIP-Seq data was not
platform-dependent, we compared an independent set of
testing and control sequences using ChIP-chip. Not only
the technology is different, but also the cells and
datasets can be found
Despite all the differences, we found that the ER motif
pattern identified by HMS from ChIP-Seq data once again
exhibited significantly higher enrichment than those of
HMS_ind and MEME (Figure 3C): the improvements of
AUCwere 17.5%, and
(Supplementary Table S8). These differences are statistical
significant (P-value<1.0e-5). Similar plots and AUC
comparisons performed on the other three datasets—
patterns (Supplementary Figures S4–6, Table S8). These
findings support that the motif pattern identified by HMS
has a higher accuracy.
All computation was performed on Dell PowerEdge 1950
compute nodes with 2.83GHz CPU processors and 8 GB
RAM. To compare the computation time required for
each algorithm, we selected the top 500, 1000, 1500,
2000, up to 5000 sequences identified from the NRSF
ChIP-Seq data and fed them into the three motif-finding
programs—MDscan, MEME and HMS. We found
MDscan to be the fastest, with HMS a close second.
Computation time increased linearly with the number of
sequences for MDscan and HMS; and both were much
faster than MEME. The differences are quite dramatic.
For real data, computation times for HMS ranged from
0.4h (NRSF data) to about 2.5h (CTCF data). However,
Nucleic Acids Research,2010, Vol.38, No. 72163
Figure 3. Comparison of ER motif patterns identified by different de novo motif-finding tools, as well as known motif patterns stored in the MatBase
(Genomatix Software GmBH, Munich, Germany). (A) Logo plots (49) of motifs identified by various motif-finding programs as well as the ones
stored in the MatBase. The logo plots are generated using R package ‘seqLogo’. (B) Comparison of motif enrichment in ChIP-Seq for six different
motif finding strategies using cross validation. Training sets, testing sets and control sets were generated following the scheme described in the
‘Materials and Methods’ section (see ‘Performance evaluation using real data’). (C) Comparison of motif enrichment in ChIP-chip data using motif
patterns identified in ChIP-Seq. In order to obtain a smooth curve when plotting empirical FDR versus chi-squared test statistics, we applied kernel
smoothing using an R function smooth.spline().
2164 Nucleic Acids Research, 2010,Vol.38, No. 7
since all parallel chains are independent, computation time
can be reduced to one tenth if using a multi-processor
computing cluster. In contrast, MEME takes much
longer; from 13h (NRSF data) to more than 23 days
(CTCF data, job aborted after 23 days of running).
The newly emerged ChIP-Seq technology is capable of
comprehensively revealing protein–DNA interacting sites
across the entire genome with high resolution, which
presents both opportunities and challenges for the identi-
fication of TFBS motif patterns. Increasing the number of
input sequences allowed us to define TFBS motif patterns
motif-finding programs such as MEME are not optimized
to analyze the large number of input sequences that are
generated from ChIP-Seq experiments. In this manuscript,
we introduce HMS, a novel computational algorithm, spe-
ChIP-Seq data. It combines stochastic sampling with
deterministic optimization in an iterative procedure. The
assignment of sequences to these two treatments was
dependent on the ranks of the ChIP-enrichment of those
regions. This prioritized hybrid Monte Carlo strategy
allows us to rapidly analyze tens of thousands of input
sequences and produces an accurate estimate of the
motif pattern. Our algorithm has the additional advantage
of leveraging sequencing depth within each region to aid
motif identification. Since the shape of sequencing depth is
indicative of likely loci of the motif, using an informative
prior gives HMS greater capability to identify weaker
motifs than it could otherwise, a clear advancement.
In addition, using HMS we found that there is substan-
tial intra-motif dependency among selected pairs of posi-
tions. We identified 16 highly significant position pairs
within the NRSF, STAT1, CTCF and ER motifs. All of
these position pairs are adjacent to each other, some form
triplets. In particular, we noticed a position pair (14 and
15) in the CTCF motif that displays exceptionally strong
dependency in which dinucleotides AG and GC are far
more frequent than AC and GG at these two positions.
Interestingly, we found that dinucleotides at dependent
position pairs in the ER motif also exhibit palindromic
property, a hallmark for binding motifs of homer-dimer
TFs. Using both simulated data and real data, we showed
that incorporating dependent positions in a motif model
offers further improvement in detecting and characterizing
the underlying TF binding motif patterns.
Currently, most de novo motif searches on sequences
identified by ChIP-Seq are conducted on a subset of all
available sequences. This is because searching through the
full set of thousands, or even tens of thousands, of input
sequences using existing motif-finding tools is extremely
time-consuming. Our simulation study showed that this
strategy, while convenient, has increased the likelihood
of missing the true motif patterns. Further, the probability
matrix H inferred with this strategy are often less accurate.
In contrast, HMS allows us to analyze the full set of input
sequences within only a fraction of the computational time
required for existing de novo motif-finding tools like
MEME. In this study, stochastic search was performed
on the top 10% of all sequences. This proportion is adjust-
able by users. We have experimented increasing or
decreasing the 10% cutoff and found that these changes
made little difference in the performance of HMS. When
applied to multiple real ChIP-Seq datasets, we found that
the motif patterns identified by HMS tend to be more
enriched than motifs identified by other methods.
Remarkably, when comparing the same motif patterns
identified from ChIP-Seq data to enriched regions
identified from independent ChIP-chip experiments for
the same TF, even with different cell types or different
antibodies or both, we still found that motif patterns
identified by HMS showed higher enrichment in the
sequences. This finding suggests that the motif patterns
identified by HMS are closer to the underlying motif
pattern recognized by the TF.
In this study, we utilized ChIP-enrichment of the peaks
ChIP-enrichment is positively correlated with the motif
abundance. However, there are many potential reasons,
both biological and technical, that a particular region is
sequenced more deeply. These include the availability of
the antibody’s epitope during the immunoprecipitation
step, conformational changes on the TF, abnormality in
the cell line such as aneuploidy, bias introduced during the
sequencibility bias (such as GC content) and bias related
to alignment (repeat regions, various polymorphisms).
These complications will reduce the correlation between
ChIP-enrichment and sequencing depth. We believe
advanced models that consider these factors will further
improve the performance of HMS. Another potential
enhancement would be to model the protein–DNA
thermodynamic models (50).
In this study, if the motif width is unknown, we run
HMS with every possible width within the range specified
by the user and report all significant motif patterns. One
possible improvement to this step would be to allow motif
width w to vary during iterations (51). For example, we
may add a Metropolis step, with equal probability of
adding or removing one base at one end of the motif,
and test whether the new motif pattern provides a better
fit with the data. Another possible area for improvement
concerns multiple binding sites. Currently, HMS is only
designed to search for the primary binding site (i.e. the
binding motif of the regulatory protein being ChIP’ed).
However, we can also use HMS to identify secondary
binding sites by masking the first motif identified and
re-running HMS on the masked sequences.
In summary, we showed that ChIP-Seq data can
significantly increase our ability to discover and refine
TFBS motif patterns. However, new computational tools
are needed in order to efficiently and thoroughly handle
the ChIP-Seq data, as well as to exploit the various advan-
tages of ChIP-Seq technology. The development of the
highly scalable HMS algorithm represents an early
attempt. With significant improvement in both accuracy
Nucleic Acids Research,2010, Vol.38, No. 72165
and computation speed, we believe that HMS will be of
broad interest to researchers conducting ChIP-Seq exper-
iments and has the potential to accelerate discovery in
Supplementary Data are available at NAR Online.
The authors would like to thank the executive editor and
two anonymous reviewers for constructive comments and
suggestions. We would like to thank Christopher Maher in
the Chinnaiyan Lab for helpful discussion, Lisa Henn and
Jill Granger for proofreading the article.
Burroughs Welcome Foundation; Doris Duke Charitable
Foundation; Prostate Cancer Foundation; American
W81XWH-09-2-0014) and the Howard Hughes Medical
Institute. Funding for open access charge: R01HG005119.
Conflict of interest statement. None declared.
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