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Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells

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Eukaryotic genomes contain a variety of structured patterns: repetitive elements, binding sites of DNA and RNA associated proteins, splice sites and so on. Often, these structured patterns can be formalized as motifs and described using a proper mathematical model such as position weight matrix and IUPAC consensus. Two key tasks are typically carried out for motifs in the context of the analysis of genomic sequences. These are: identification in a set of DNA regions of over-represented motifs from a particular motif database, and de novo discovery of over-represented motifs. Here we describe existing methodology to perform these two tasks for motifs characterizing transcription factor binding. When applied to the output of ChIP-seq and ChIP-exo experiments, or to promoter regions of co-modulated genes, motif analysis techniques allow for the prediction of transcription factor binding events and enable identification of transcriptional regulators and co-regulators. The usefulness of motif analysis is further exemplified in this review by how motif discovery improves peak calling in ChIP-seq and ChIP-exo experiments and, when coupled with information on gene expression, allows insights into physical mechanisms of transcriptional modulation.
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REVIEW
published: 23 February 2016
doi: 10.3389/fgene.2016.00024
Frontiers in Genetics | www.frontiersin.org 1February 2016 | Volume 7 | Article 24
Edited by:
Ekaterina Shelest,
Leibniz Institute for Natural Product
Research and Infection
Biology – Hans Knöll Institute,
Germany
Reviewed by:
Vladimir A. Kuznetsov,
Bioinformatics Institute, Singapore
Jan Grau,
Martin Luther University
Halle-Wittenberg, Germany
*Correspondence:
Valentina Boeva
valentina.boeva@inserm.fr
Specialty section:
This article was submitted to
Bioinformatics and Computational
Biology,
a section of the journal
Frontiers in Genetics
Received: 30 October 2015
Accepted: 05 February 2016
Published: 23 February 2016
Citation:
Boeva V (2016) Analysis of Genomic
Sequence Motifs for Deciphering
Transcription Factor Binding and
Transcriptional Regulation in
Eukaryotic Cells. Front. Genet. 7:24.
doi: 10.3389/fgene.2016.00024
Analysis of Genomic Sequence
Motifs for Deciphering Transcription
Factor Binding and Transcriptional
Regulation in Eukaryotic Cells
Valentina Boeva 1, 2, 3, 4, 5, 6, 7, 8*
1Centre de Recherche, Institut Curie, Paris, France, 2INSERM, U900, Paris, France, 3Mines ParisTech, Fontainebleau,
France, 4PSL Research University, Paris, France, 5Department of Development, Reproduction and Cancer, Institut Cochin,
Paris, France, 6INSERM, U1016, Paris, France, 7Centre National de la Recherche Scientifique UMR 8104, Paris, France,
8Université Paris Descartes UMR-S1016, Paris, France
Eukaryotic genomes contain a variety of structured patterns: repetitive elements,
binding sites of DNA and RNA associated proteins, splice sites, and so on. Often,
these structured patterns can be formalized as motifs and described using a proper
mathematical model such as position weight matrix and IUPAC consensus. Two key
tasks are typically carried out for motifs in the context of the analysis of genomic
sequences. These are: identification in a set of DNA regions of over-represented motifs
from a particular motif database, and de novo discovery of over-represented motifs. Here
we describe existing methodology to perform these two tasks for motifs characterizing
transcription factor binding. When applied to the output of ChIP-seq and ChIP-exo
experiments, or to promoter regions of co-modulated genes, motif analysis techniques
allow for the prediction of transcription factor binding events and enable identification of
transcriptional regulators and co-regulators. The usefulness of motif analysis is further
exemplified in this review by how motif discovery improves peak calling in ChIP-seq and
ChIP-exo experiments and, when coupled with information on gene expression, allows
insights into physical mechanisms of transcriptional modulation.
Keywords: motif discovery, transcription factors, binding sites, position-specific scoring matrices, regulation of
gene transcription, ChIP-seq, binding motif models
INTRODUCTION
A eukaryotic genome contains a variety of structured patterns. A far from exhaustive list of genomic
patterns includes (i) tandem repeats and transposable elements, (ii) stretches of GC- or AT-rich
sequences (e.g., CpG islands in mammalian genomes), (iii) binding sites of DNA associated proteins
(e.g., transcription factor binding sites), (iv) splice sites, and (v) DNA and RNA binding sites of non-
coding RNA molecules. Different patterns may overlap each other. Therefore, although this review
is focused on motifs for transcription factor binding sites (TFBSs), we provide a short overview of
other types of genomic patterns.
Transcription Factor Binding Sites (TFBSs)
Transcription factors (TFs) are proteins with DNA binding activity that are involved in
the regulation of transcription. Generally, TFs modulate gene expression by binding to
Boeva Motif Analysis for TFBSs
FIGURE 1 | Direct and indirect binding of TF PU.1 to DNA. (A) Direct
binding of PU.1 to DNA to the consensus motif sAGGAAs, which may lead to
transcriptional activation. (B) Indirect binding of PU.1 to DNA, which may lead
to transcriptional repression.
gene promoter regions or to distal regions called enhancers. The
distance between a TFBS and a transcription start site (TSS)
of a gene regulated by the TF can be up to several megabases,
and depends on the chromatin structure of the region (Dekker
and Heard, 2015). Although TFs possess by definition DNA
binding domains, they may occasionally bind DNA indirectly,
by interacting with another TF. For instance, PU.1 and GATA-1
(TFs playing a critical role in the differentiation of hematopoietic
lineages) interact through the ETS domain of PU.1 and the C-
terminal finger region of TF GATA-1; as a result, PU.1 can
bind to DNA both directly and indirectly, through the assistance
of GATA-1 (Figure 1;Burda et al., 2010). A TF has binding
preferences to a specific set of DNA sequences referred to
as a “binding motif.” TFs have different binding affinities for
sequences forming their binding motif set. Several mathematical
models have been developed to represent a binding motif and
take into account its properties. One of the most commonly used
models is the positional weight matrix (PWM), also called the
position-specific scoring matrix (PSSM), containing the log-odds
or log-probability weights for computing the binding affinity
score. Construction and use of the PWM model is discussed in
detail in the next section. In some cases, the same TF is able to
bind quite dissimilar motifs; the motif choice may predefine the
action of this TF on gene expression (Guillon et al., 2009).
TFs often interact with each other or compete for DNA
binding. Consequently, their binding sites may co-localize or
overlap (Wang et al., 2012). Co-localization of TFBSs can be
also due to the combined action of a set of TFs: First, TFs
capable of binding inactive chromatin bind to DNA and create an
open chromatin environment through the recruitment of histone
acetyltransferases (pioneer TFs). Then, other TFs (lacking the
above capability) become able to bind DNA and activate gene
transcription by interacting with the RNA polymerase machinery
(Farnham, 2009). Analysis of the distance and orientation
preferences between the sites of co-binding TFs helps to predict
possible protein-protein interactions, and enables insights into
the mechanisms of transcriptional regulation by TFs when
coupled with information on gene expression modulation.
Repeats
Repeats constitute a large part of eukaryotic genomes. For
instance, more than 45% of the human genome corresponds
to repetitive sequences (Derrien et al., 2012). Among them,
one distinguishes tandem repeats (DNA is repeated in head-
to-tail fashion: microsatellites, minisatellites, and satellite
sequences) and interspersed repeats (similar sequences are
located throughout the genome). The latter correspond to
transposable elements such as SINEs and LINEs, accounting
for 12.5 and 20% of the human genome, respectively. Tandem
repeats themselves account for 10–15% of the human genome.
While short tandem repeats can serve as binding sites for specific
transcription factors (TFs; Shi et al., 2000; Guillon et al., 2009),
long satellite repeats can play a role in the 3D structure shaping of
the genome. For instance, the α-satellite family of repeats (171
bp tandem repeats) are bound by the fundamental component
of the centromere CENP-C, and are essential for centromere
function by ensuring proper chromosome segregation in
mitosis and meiosis (Politi et al., 2002). The TandemSWAN
software (http://favorov.bioinfolab.net/swan/tool.html) allows
the annotation of exact and fuzzy tandem repeats in genomic
sequences (Boeva et al., 2006). It is usual to mask such repeats in
order to avoid artifact discovery, for example, during analysis of
next-generation sequencing data.
AT- or GC- Rich Sequences
AT- or GC- rich sequences are often located in gene promoters
and play a role in transcription initiation. Approximately 24%
of human genes contain an AT-rich sequence within the core
promoter, with 10% containing a canonical TATA-box motif
(TATAWAWR, W =A/T, R =A/G; Yang et al., 2007). The
TATA-box recruits the TATA binding protein (TBP), which
unwinds the DNA; also, due to weaker base-stacking interactions
among A and T (than G and C), AT-rich sequences facilitate
unwinding. The remaining 76% of human promoters are GC-
rich and contain multiple binding sites of the transcriptional
activator SP1 (Yang et al., 2007). As much as 56% of human
genes, including most of the housekeeping genes, possess CpG
islands, i.e., 300–3000 bp GC-rich sequences around gene TSS
with a high density of CpG dinucleotides. The high methylation
level of CpG sites in CpG islands has been shown to be associated
with transcriptional repression. Polycomb group (PcG) repressor
proteins recognize CpG islands that are unmethylated and
unprotected by TFs (Klose et al., 2013). PcG proteins associate
with DNA methyltransferases responsible for methylation of
CpG islands (Viré et al., 2006). Also, some components of PcG
proteins have histone methyltransferase activity and trimethylate
histone H3 on lysine 27, which is a mark of transcriptionally
silent chromatin.
Splice Site
During splicing, introns are removed from the pre-messenger
RNA transcript and remaining exons are joined together to
later form mature messenger RNA. Generally, in eukaryotes, the
process of splicing is catalyzed by spliceosomes. These complex
molecular machines recognize a donor site (almost invariably GU
at the 5end of the intron), a branch site (adenine nucleotide
followed by a pyrimidine-rich tract near the 3end of the intron),
and an acceptor site (almost always AG at the 3end of the intron)
on RNA transcripts. A DNA mutation in a splice site may have a
wide range of functional consequences, among them exclusion of
an exon from the mature mRNA, or inclusion of an intron or part
of one. The latter often results in disruption of the reading frame
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Boeva Motif Analysis for TFBSs
FIGURE 2 | Sequence logo of the PWM created by ChIPMunk
(Kulakovskiy et al., 2010) using 17,781 binding site regions predicted
for PU.1/Spi-1 using ChIP sequencing (ChIP-seq) data
(Ridinger-Saison et al., 2012).
or a premature stop codon, and thus gives rise to a defective or
truncated protein.
miRNA Binding Sites
While binding of regulatory proteins to promoter and enhancer
DNA regions regulates expression of the targeted protein at the
transcription level, binding of micro RNA molecules (miRNAs)
to the 3UTR region of a mRNA transcript can regulate the
protein amount at the post-transcriptional level. The interaction
of an miRNA as part of an active RNA-induced silencing complex
(RISC) with a 3UTR of the targeted mRNA transcript results in
either inhibition of translation or increased degradation of this
transcript. The miRNA complex recognizes the 6–8 nucleotides
at the mRNA 3UTR, which is complementary to the miRNA
“seed” region (Bartel, 2009). In the human genome, there are
more than 2000 unique miRNAs. One miRNA can target several
genes, and the same 3UTR can be targeted by multiple miRNAs.
Sequence analysis of gene’s 3UTR, coupled with the analysis
of evolutionary conservation of the 3UTR region, allows the
prediction of miRNA-target pairs (Yue et al., 2009). Mutations in
an miRNA target site may disrupt miRNA repressive regulation,
and thus result in protein overexpression (Chin et al., 2008).
Alternatively, a mutation in the 3UTR of a gene can create a new
active miRNA binding site, negatively affecting gene expression
(Ramsingh et al., 2010).
In this review, we present methods for in silico prediction
of TFBSs, which can overlap any other type of genomic motif:
repeats, CpG islands, splice sites, and so on. Some of the motif
analysis methods discussed in this review in Section “In silico
Detection of TFBSs” can be also applied to other types of motifs
than TFBSs. In Section “Applications of Motif Analysis”, we also
demonstrate how motif discovery can be used to improve peak
calling from chromatin immunoprecipitation (ChIP) sequencing
data and obtain insights about mechanisms of transcriptional
regulation by specific TFs.
IN SILICO DETECTION OF
TRANSCRIPTION FACTOR BINDING SITES
We define TF binding motifs as sets of DNA sequences having
high affinity for binding TFs. Each occurrence of a sequence from
the binding motif in a genomic region is referred to as a motif
instance. In the case of direct binding of a TF to DNA, a DNA
region surrounding the binding site usually contains one or more
instances of the corresponding binding motif.
There are several models for defining binding motifs. These
can be used to scan a DNA sequence to predict TFBSs.
Enumeration
All sequences with the potential to be bound by a TF can be
enumerated. Information about these sequences can be obtained
from SELEX experiments (Oliphant et al., 1989). To allow for
discrimination between sequences with strong and weak binding
affinities, one can use for example the SELEX affinity score
assigned to each particular k-mer.
Consensus
An alternative model for motif description is a consensus
motif, constructed using the nomenclature of the
International Union of Pure and Applied Chemistry (IUPAC):
A=adenine C =cytosine
G=guanine T =thymine
Y=T | C (pyrimidine) R =G | A (purine)
K=G | T (keto) M =A | C (amino)
S=G | C (strong bonds) W =A | T (weak bonds)
B=G | T | C (all but A) V =G | C | A (all but T)
D=G | A | T (all but C) H =A | C | T (all but G)
N=A | G | C | T (any)
For instance, the IUPAC consensus for the binding motif of TF
PU.1/Spi-1 can be written RRVRGGAASTS (the corresponding
motif logo is depicted in Figure 2;Ridinger-Saison et al., 2012).
The shortcoming of this way of modeling binding motifs is
that many functional binding sequences may not be included in
the motif when using a stringent consensus, and indeed, when
consensus is poor, the motif can comprise motif instances of very
low binding affinity, due to the uncaptured effect of nucleotide
combinations on several low-affinity positions.
Position Weight Matrix (PWM)
The PWM is the most frequently used mathematical model for
binding motifs (Stormo, 2000). A PWM contains information
about the position-dependent frequency or probability of each
nucleotide in the motif. This information is usually represented
as log-weights {wα, j}of probabilities (wα, j=log(pα, j)) or, most
frequently, odds ratios (wα, j=log2(pα, j/bα)) for computing
a match score. Here pα, jis the probability of nucleotide αα
at position j, and bαthe background probability of nucleotide
α. Small sample correction is usually included in pα, jto avoid
taking the logarithm of zero. A PWM match score for an
arbitrary k-mer A=a1a2...akis computed as SA=Pjwaj,j.
Recent “deep learning” techniques (Alipanahi et al., 2015) use
PWMs where weights are not required to be probabilities or
log-odds ratios.
PWMs can be visualized using sequence logos (Schneider
and Stephens, 1990;Figure 2). The total height of each bin is
the information content in bits of the corresponding position:
Hj=2Pαpα,jlog2(pα, j). The height of each nucleotide
in the logo is proportional to its probability pα, jand, for each
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Boeva Motif Analysis for TFBSs
position, the four nucleotides are ordered by pα, jwith the most
likely nucleotides depicted on top of the stack.
PWMs can be experimentally determined from SELEX
experiments or computationally discovered from protein binding
microarrays (PBMs; Berger and Bulyk, 2009), genomic-context
PBM (gcPBM; Gordân et al., 2013), ChIP-seq, and ChIP-exo data.
Using the PWM motif representation, it is possible to
distinguish strong binding sites (high PWM score) from weak
binding sites (moderate PWM score). It may however, be a
problem to discriminate weak binding sites from background
(low or negative PWM score). Usually, a cutoff in the PWM score
is used to decide whether a given sequence matches the motif. The
choice of this cutoff is a complex statistical task that we discuss
further here and in Section “Detection of TFBSs with Known
PWMs”.
A PWM is constructed based on single nucleotide frequencies
(four letter alphabet). However, from the methodological point of
view, this model can be easily extended to the 16 letter alphabet
of consecutive dinucleotides. This model has been used in the
de novo motif discovery methods Dimont (Grau et al., 2013),
diChIPMunk (Kulakovskiy I. et al., 2013), and BEEML-PBM
(Zhao and Stormo, 2011; Zhao et al., 2012), the latter being
designed to work with PBM data.
Bayesian Networks and Other Supervised
Classification Methods
Although PWM is the most widely used mathematical
representation of TF specificity, it still has drawbacks. For
instance, it assumes the independence of positions within the
motif: each position contributes separately to the PWM score,
which reflects binding affinity. Modeling position dependencies
with Bayesian networks provides an elegant solution to this
problem (Barash et al., 2003; Ben-Gal et al., 2005; Grau et al.,
2006). However, since there is no easy way to visualize motifs
defined as a Bayesian network, this approach is rarely used by
the research community.
This class of models was followed by another class of graphical
model approaches based on Markov models (Wasson and
Hartemink, 2009; Reid et al., 2010; Mathelier and Wasserman,
2013; Eggeling et al., 2014). The approach proposed by Mathelier
and Wasserman (2013) has been included in the JASPAR
database. Slim probabilistic graphical models, implemented by
Keilwagen and Grau (2015), can be used via a Galaxy wrapper
(http://galaxy.informatik.uni-halle.de); the authors also provide
an intuitive model visualization.
In addition, motifs can be modeled and searched for using k-
mer frequencies via support vector machine (SVM) approaches
(Holloway et al., 2005; Jiang et al., 2007; Gorkin et al., 2012;
Fletez-Brant et al., 2013). This class of approaches can be
successfully applied to PBM data (Agius et al., 2010; Mordelet
et al., 2013).
One of the important advantages of these graphical model
and SVM-based approaches is that they can account for variable
spacing between half-sites of two-box TFs (examples of such
motifs are shown in Figure 6A). The DREAM5 challenge
paper provides a comparative study of different methods for
modeling transcription factor sequence specificity (Weirauch
et al., 2013).
Given a motif described with one of the above-listed
models, one can scan a set of genomic sequences or even
a whole genome in order to detect possible TF binding
sites. This can be achieved by applying efficient algorithms
employing deterministic and non-deterministic finite
automata accepting motif instances (Navarro and Raffinot,
2002; Antoniou et al., 2006; Boeva et al., 2007; Marschall
and Rahmann, 2008; Marschall, 2011; Holub, 2012). The
AhoPro (http://favorov.bioinfolab.net/ahokocc/seach_motifs.,
html Boeva et al., 2007) and PWMTools (http://ccg.vital-it.ch/
pwmtools/pwmscan.php, Iseli et al., 2007) websites allow for
fast online searches of instances of motifs with several of the
models described above, in a set of sequences in FASTA format
or in whole genomes. More tools allowing for a fast scan of
sequences in FASTA format for motif instances are listed in the
next section.
In the following, we choose the PWM model to represent
binding motifs. Given that a cutoff is correctly selected, we
assume that a TF binds DNA sequences with PWM scores higher
than the cutoff. This assumption is a very rough approximation
of reality. Using a high cutoff implies rejecting most of the weak
binding sites, while using a lower cutoff can result in adding too
much noise to predictions and muddle biological conclusions. In
practice, the cutoff can be selected in a way to predict one motif
instance per 1 or 10 Kb of the genome (Kulakovskiy I. V. et al.,
2013). Cutoff choice can be also based on the hypothesis that the
corresponding motif is over-represented in a given set of DNA
sequences; this cutoff selection strategy is discussed in the next
section.
In silico detection of TFBS may be separated into two tasks:
detection of binding sites of TFs with known binding motifs
(PWMs), and de novo motif discovery. Sections “Detection of
TFBSs with Known PWMs” and “De novo Motif Discovery” focus
on these two questions.
Detection of TFBSs with Known PWMs
Detection of TF binding motif instances for known motifs has
its application in promoter analysis or the analysis of more
distant regulatory regions (enhancers), where the goal is to find
TFs possibly regulating corresponding genes. Scanning a set of
sequences with PWMs of known motifs can also be used to detect
co-factor binding in ChIP-seq-derived binding site regions of a
TF of interest. Alternatively, one can use known-motif discovery
to assess the effect of SNPs and mutations on TF binding. With
the increase in the number of sequenced genomes, the second
question has recently gained in importance, and novel tools
permitting annotation of variants within TF motif instances have
begun to be developed (Boyle et al., 2012; Ward and Kellis, 2016).
There exist several public and commercial databases storing
PWMs for known TF binding motifs.
HOCOMOCO: a comprehensive collection of human TFBS
models (Kulakovskiy I. V. et al., 2013)
JASPAR 2016: an extensively expanded and updated open-
access database of TF binding profiles that can capture
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Boeva Motif Analysis for TFBSs
FIGURE 3 | PWM score cutoff selection for a set of enhancer regions. Two local maxima in the P-value graph provide two p-value cutoffs that correspond to
primary binding sites (high cutoff) and “shadow” binding sites (low cutoff). The table shows how many potential k-mer sequences match the PWM with a given cutoff
(column 2), the number of motif instances in the set of enhancers (column 3), and the corresponding p-value (column 4).
dinucleotide dependencies within TF binding sites (Mathelier
et al., 2016)
SwissRegulon: a database of genome-wide annotations of
regulatory sites (Pachkov et al., 2007)
TRANSFAC R
: a commercial database on TFBSs, PWMs, and
regulated genes in eukaryotes (Matys et al., 2006)
footprintDB: a database summarizing motifs from
HOCOMOCO, JASPAR, and other databases (Sebastian
and Contreras-Moreira, 2014).
True binding sites usually score high with the corresponding
PWM, while background sequences have low PWM scores. It
is not sufficient to scan a DNA region to get a PWM score at
each position. The main difficulty is to correctly set the cutoff on
the PWM score to separate true binding sites from background.
Evaluation of the statistical significance of motif instances can
help solve this issue (Boeva et al., 2007).
When a PWM score cutoff cis given, it is possible
to enumerate all possible sequences matching PWM with
a score above the cutoff. Let us call this set Mc=
{As1,As2, . . . , Asm}si>c, where each sequence Asiis a k-mer with
PWM score si>c. The higher the cutoff c, the smaller the set of
motif sequences Mc. Given a set of regulatory regions (enhancers
or promoters) R, we can define the number NR,cshowing how
many Asifrom Mcoccurred in R. With a higher cutoff, fewer
motif instances will be detected; corresponding binding sites are
likely to have strong binding affinity. With a lower cutoff, more
motif instances are detected; these may correspond to both strong
and weak binding sites.
In regulatory regions, binding sites often tend to occur in
clusters, and binding motifs are over-represented in the set Rof
regulatory sequences targeted by the transcription factor. This is
not the case for random sequences. The procedure developed in
Boeva et al. (2007) to specify the cutoff on the PWM score for a
set Ris based on this assumption.
The significance of motif instance over-representation can be
measured through the p-value, i.e., the probability to observe
at least the same number NR,cof motif instances with cutoff
cin a random sequence with total length equal to the total
length of sequences in R(Figure 3). Setting different cutoffs c,
one gets different numbers of motif instances NR,cin Rand
different p-values, P(Mc,NR,c). The minimum of P(Mc,NR,c)
over cprovides a cutoff corresponding to the most significant
motif over-representation in R. This approach can be equally
applied to several PWM corresponding to several TF binding
motifs (Figure 4).
The exact p-value calculation for multiple motifs with
overlapping (and self-overlapping) motifs is a difficult
computational task. The compound Poisson distribution
formula for the p-value generally provides a good approximation,
but not in the case of several highly-overlapping motifs.
An exact algorithm for p-value calculation for the general
case of heterotypic clusters of motifs may be based on the
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Boeva Motif Analysis for TFBSs
FIGURE 4 | Simultaneous PWM score cutoff selection for PWMs of two
D. melanogaster TFs: Bicoid and Krüppel. The graph shows the
distribution of log10(p-value) as a function of the cutoff for the two PWMs for
the enhancer of the gene even-skipped stripe 2 (eve2). The red point
corresponds to the most significant combination of PWM and cutoffs (from
Boeva et al., 2007).
Aho-Corasick automaton, and employ a prefix tree together
with a transition function (Boeva et al., 2007; Marschall and
Rahmann, 2008).
The approach for automatic cutoff choice for a set of PWMs
was applied to the identification of binding sites of cooperatively
and anti-cooperatively functioning regulatory proteins in D.
melanogaster (Boeva et al., 2007). By employing this method,
we discovered the phenomenon of “shadow” TFBS in enhancers
of the D. melanogaster genome. Shadow binding sites are low
affinity binding sites that alone are not capable of retaining the
TF long enough to ensure activation/repression, but instead are
used to maintain a high concentration of TF in the vicinity of
the primary binding sites. This phenomenon has been recently
confirmed by other studies (Kozlov et al., 2015).
We should mention that the choice of the background model
is quite important in the calculation of probabilities of motif
occurrences. A Markov chain employed as a background model
allows us to capture dependencies between nucleotides. This can
take into account low or high frequencies of CpG nucleotides in
the set of enhancer or promoter sequences.
An automatic scan of a set of DNA sequences using motifs
from the databases listed above, with tool-specific cutoffs, is
available through the following websites and programs:
AME or FIMO of the MEME suite (McLeay and Bailey, 2010)
http://meme-suite.org/
SeqPos of Galaxy Cistrome (Liu et al., 2011) http://
cistrome.org/ap/
PWMScan of PWMTools (Iseli et al., 2007) http://ccg.
vital-it.ch/pwmtools/pwmscan.php
oPOSSUM-3 (Kwon et al., 2012) http://opossum.cisreg.
ca/oPOSSUM3/
HOMER (Heinz et al., 2010) http://homer.salk.edu/homer/
De novo Motif Discovery
When the PWM of a TF of interest is not known, it can be
obtained using de novo motif discovery from a set of DNA
sequences containing binding sites of this TF. The technique
consists of defining the most over-represented motif in a given
set of DNA sequences. The set of DNA sequences containing
TFBSs of a particular protein can be obtained with SELEX, PBM
or ChIP-x experiments (i.e., ChIP-seq, ChIP-exo, ORGANIC,
ChIP-on-chip). ChIP-Seq (Johnson et al., 2007), ChIP-exo (Rhee
and Pugh, 2011), and ORGANIC (Kasinathan et al., 2014)
consist of immunoprecipitation of DNA–protein complexes and
sequencing of short ends of the immunoprecipitated DNA.
These techniques provide enhanced resolution of binding regions
compared to ChIP-on-chip, which is based on microarrays, and
have almost replaced the latter. The ChIP-exo technique provides
an even better resolution of binding sites than ChIP-seq, at
the expense of a more elaborate library preparation protocol,
including an exonuclease step. In this section, we focus on de
novo motif discovery in ChIP-seq datasets.
ChIP-seq yields a set of genomic regions (also called peaks)
that are thought to contain TFBSs. The output of a ChIP-seq
experiment can include tens of thousands of peaks, some longer
than 1000 bp. Each peak position has a weight reflecting how
often a given DNA fragment was cross-linked with the protein
of interest during the ChIP stage (coverage profiles).
There exist a large number of methods for the de novo
detection of over-represented motifs. The classical tool, MEME
(Bailey et al., 2009), was developed for motif discovery in a small
number of short DNA sequences, and scales poorly to large
ChIP-seq datasets. Subsequently, several methods were newly
created to analyze large sets of sequences resulting from ChIP-
seq experiments: HMS (Hu et al., 2010), cERMIT (Georgiev
et al., 2010), ChIPMunk (Kulakovskiy et al., 2010), diChIPMunk
(Kulakovskiy I. et al., 2013), MEME-ChIP (Machanick and
Bailey, 2011), POSMO (Ma et al., 2012), XXmotif (Hartmann
et al., 2013), FMotif (Jia et al., 2014), Dimont (Grau et al., 2013),
RSAT (Medina-Rivera et al., 2015), and DeepBind (Alipanahi
et al., 2015). The latter method uses increasingly popular “deep
learning” techniques; however, it has only been tested on sets of
rather short input sequences (up to 101 bp).
There is a tradeoff between the user-friendliness of these tools,
speed, and accuracy of predictions. For instance, the use of
dinucleotide frequencies and application of read coverage profiles
(.wig files) as priors for motif locations, improves the quality of
resulting motifs. Both options are supported by diChIPMunk
(Kulakovskiy I. et al., 2013). Dimont (Grau et al., 2013) can
also use dinucleotide sequences for PWM construction and take
into account peak height information, i.e., number of reads
supporting each putative binding region. However, the user may
find it encumbering extracting coverage information from the
ChIP-seq data. Also, dinucleotide PWMs can come across as
illegible in biological publications. It appears that intuitive and
fast online methods based on classical PWMs are generally in
higher demand by biologists than more sophisticated methods.
Indeed, speed is one of the key issues in this type of analysis. In
this context, k-mer enumeration methods like POSMO (Ma et al.,
2012), cERMIT (Georgiev et al., 2010), and RSAT-peak-motifs
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Boeva Motif Analysis for TFBSs
FIGURE 5 | Two modes of multiple motif detection: “Mask sequences” mode to discover binding motifs of the same TF, and “Mask motifs” mode to
discover binding motifs of co-factors. After the first motif is identified, either all sequences containing this motif instance are removed from further analysis
(sequences in gray, “Mask sequences” mode), or motif instances are masked (motif instances in gray, “Mask motifs” mode). The second motif is defined as the motif
with the highest KDIC in the remaining nucleotide sequences.
(Medina-Rivera et al., 2015) show very competitive runtimes
on large ChIP-seq datasets. However, probabilistic approaches
(e.g., ChIPMunk, Dimont) may provide higher accuracy results
(Grau et al., 2013). Overall, according to comparative studies,
POSMO, Dimont, and ChIPMunk seem to be the most suitable
methods for motif discovery among currently available ones (Ma
et al., 2012; Grau et al., 2013). However, a more detailed study
including more recent methods is required. More information
about recently published methods is available in several reviews
(Tran and Huang, 2014; Lihu and Holban, 2015). Most of the
above-cited methods allow detection of several over-represented
motifs. Below, we illustrate de novo multiple motif discovery with
the ChIPMunk tool.
Multiple motif discovery allows us to identify (i) all possible
binding motifs for the same TF and (ii) co-factor binding motifs.
For these two cases, different motif discovery procedures should
be applied. These two procedures are implemented in ChIPMunk
as “Mask sequences” and “Mask motifs” modes. The first motif
identified is always the motif with the highest Kullback discrete
information content (KDIC). Then, the second motif is identified
as the motif with the highest KDIC either in the sequences that
do not contain the first motif (“Mask sequences” mode), or in the
total set of sequences where the instances of the first motif have
been masked (“Mask motifs” mode; Figure 5).
The underlying assumption when using the “Mask sequences”
mode is that the same TF can, in some cases, bind to significantly
different binding motifs; but almost every binding site region
should contain at least one motif instance (Wang et al., 2012).
We should mention that frequently a TF has only one binding
motif; the higher the PWM score of the corresponding motif,
the stronger the binding affinity (Kulakovskiy et al., 2010;
Kulakovskiy I. V. et al., 2013). In this case, the “Mask sequences”
mode is likely to output only one motif. This motif will be
present in almost all sequences from the set. The situation
where the same TF has different binding motifs, occur less
frequently (Badis et al., 2009). For instance, this is the case for
TFs EWS-FLI1 (Guillon et al., 2009) and NRSF (Johnson et al.,
2007;Figure 6). Also, some proteins, such as PU.1, can bind
to DNA both directly and indirectly (Figure 1). In these cases,
the “Mask sequences” mode will provide, as a result, several
motifs. This will be the motifs for the direct and indirect binding
(e.g., motifs for PU.1 and GATA1 for the situation illustrated in
Figure 2).
The underlying assumption for the use of the “Mask motifs”
mode is that co-factors of the main TF bind close to the main TF
in regions detected with chromatin immunoprecipitation using
an antibody specific to the main TF of interest (Figure 5, right
panel). Thus, binding motifs of co-factors can be detected as over-
represented motifs after the motif instances of the main TF have
been masked.
When a binding motif is identified de novo, it is possible to
compare its PWM or IUPAC consensus with the known motif
PWMs stored in the TF motif databases via:
JASPAR (Mathelier et al., 2016)—http://jaspar.genereg.net/,
Motif Comparison Tool of the MEME Suite (Gupta et al.,
2007)—http://meme-suite.org/tools/tomtom
MACRO-APE (Vorontsov et al., 2013)—http://autosome.
ru/macroape/
STAMP (Mahony and Benos, 2007)—http://www.benoslab.
pitt.edu/stamp.
In this section, we have focused on the prediction of TFSB
sites in a set of rather short regulatory regions provided by the
user (regulatory regions obtained from ChIP-seq experiments).
However, in some situations, one may be interested in analyzing
much larger genomic regions (up to the whole genomes). In
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Boeva Motif Analysis for TFBSs
FIGURE 6 | A transcription factor can have several binding motifs. (A) Logos for binding motifs of TF NRSF with 11 and 17 bp spacer between half-sites
(Johnson et al., 2007); (B) Logos for binding motifs of chimeric TF EWS-FLI1 (Guillon et al., 2009; Boeva et al., 2010).
this case, one can narrow down the space of possible TFBS
positions by considering known open chromatin regions in a
given cell type, histone marks, and by using conservation profiles
between species (Zhong et al., 2013). For instance, using a PWM-
based score for the promoter, together with a profile of a single
histone modification (H3K4me3), can produce highly accurate
predictions of TF-promoter binding (McLeay et al., 2011).
APPLICATIONS OF MOTIF ANALYSIS
Motif discovery finds its applications in the analysis of
promoters of co-expressed or co-regulated genes and in the
analysis of regulatory regions frequently extracted from ChIP-
x experiments. In this section, we explain a frequently applied
procedure for promoter analysis. Then, we provide two examples
on how motif analysis can be used in the exploration of ChIP-
x data. We show how motif information can be applied to
get a more accurate set of TFBSs from a ChIP-x experiment,
and demonstrate how motif analysis can lead to insights into
mechanisms of transcriptional regulation when it is integrated
with information about changes in gene expression in a TF
inhibition experiment.
Promoter Analysis: Looking for
Over-Represented TF Motifs
Discovery of over-represented motifs in a set of genomic regions
is often used to determine TFs likely to regulate genes co-
modulated following some system perturbation, e.g., knockout or
knockdown of a protein or cell differentiation. This type of study
is called promoter analysis; it is based on the assumption that
several promoters from the gene list are regulated by the same TF
via binding of this TF to the promoter area of the corresponding
genes. Thus, the goal of promoter analysis is to detect known (or
less frequently de novo) motifs for which the number of motif
instances is significantly higher in the set tested compared to
background. As background, one should preferably use a set of
promoters of non-modulated genes. Alternatively, one can define
a set of random genomic regions or simply specify a background
model (e.g., a Markov model of order 1 taking into account
dinucleotide frequencies in promoters). Most of the methods
apply the zero-or-one occurrences per sequence (ZOOPS) model
(Bailey and Elkan, 1995), which enables detection of the strongest
motif in a set of sequences; under this model, the strongest motif
does not necessarily have instances in every input sequence. The
presence of clusters of the same motif in one sequence is not taken
into account by this model. The ZOOPS model is also applied
by motif discovery tools designed to analyze ChIP-seq data
(described above).
There are several major caveats to this approach. First, not
every motif incidence corresponds to a true binding event.
Thus, the definition of promoter length affects the results of
the analysis. Larger promoter regions are likely to include a
certain number of false predictions of binding sites, and at the
same time are likely to capture more true binding sites. The
use of large regions upstream of TSS in promoter analysis is
especially unjustified when looking for short or highly degenerate
motifs. The second caveat is that genes can be regulated by
TF binding to distant regulatory elements: enhancers. These
are often tissue specific, and thus not generally included in
the set of sequences in which we look for motifs. The third
caveat is the selection of the cutoff on the motif strength. Some
methods allow the choice of the best cutoff as that providing
the lowest p-value, while other methods use predefined cutoffs
(Marstrand et al., 2008). Fourth, co-factors may be required
for TF binding. In this case, one should probably search
for combinations of motifs within a certain distance of one
another.
Several tools have been developed specifically for promoter
analysis. Some tools require gene lists while others expect
sequences in FASTA format as input. The latter methods can be
also applied to enhancer regions.
Web-based promoter analysis tools:
Amadeus (Linhart et al., 2008) http://acgt.cs.tau.ac.il/
amadeus/—requires program download; can search for
pairs of co-occurring motifs; accepts gene lists as input
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Boeva Motif Analysis for TFBSs
i-cisTarget (Herrmann et al., 2012; Imrichová et al.,
2015) https://gbiomed.kuleuven.be/apps/lcb/i-cisTarget/—
accepts.BED files or gene names; when gene names are
provides, motif search is performed in 20 Kb window
around gene TSSs overlapping with predefined candidate
regularity regions
Pscan (Zambelli et al., 2009) http://www.beaconlab.it/
pscan—requires a gene list and provides a choice of 5
lengths for promoter intervals
OTFBS (Zheng et al., 2003) http://genome.ucsf.edu/
jiashun/OTFBS/—online version accepts no more than 200
sequences in FASTA format
Asap (Marstrand et al., 2008) http://servers.binf.ku.dk/
asap/—accepts sequences in FASTA format; PWM
threshold should be selected by the user
oPOSSUM-3 (Kwon et al., 2012) http://opossum.cisreg.ca/
oPOSSUM3/—accepts both sequences in FASTA format
and gene lists
Match and P-Match (Chekmenev et al., 2005) http://www.
gene-regulation.com/pub/programs.html—TRANSFAC R
motif scanning algorithms
SiTaR (Fazius et al., 2011) https://sbi.hki-jena.de/ sitar/—
needs a motif in enumeration format
Offline promoter analysis tools:
HOMER (Heinz et al., 2010)—command line tool to search
for de novo motifs and compare them to known PWMs
Clover (Frith et al., 2004).
The motifs in the output are sorted according to the method-
specific p-values and enrichment scores. These p-values may be
calculated through binomial or hyper-geometric statistical tests
(Frith et al., 2004; Marstrand et al., 2008; Heinz et al., 2010;
Kwon et al., 2012), ranking-and-recovery analysis of predefined
tracks (Imrichová et al., 2015), or using the Z-transform of
scores (Linhart et al., 2008; Zambelli et al., 2009). Correction
for multiple tests is optionally performed by some methods
(Marstrand et al., 2008).
As mentioned earlier, complementary information about
sequence conservation, regions of open chromatin, and presence
of specific histone marks, helps to increase TFBS prediction
accuracy (Cuellar-Partida et al., 2012; Grant et al., 2015;
Imrichová et al., 2015).
Promoter analysis usually predicts binding sites
independently for several TFs. However, some recent approaches
propose a different strategy, where the goal is to detect
combinations of binding sites of several TFs forming cis-
regulatory modules (CRMs). These approaches can be based on
both de novo discovery of motifs, or using available motifs from
databases. They can be applied to a set of promoter sequences,
but also on predefined sets of enhancers, which can be obtained,
for example, using profiles of histone marks. Some methods such
as Allegro (Halperin et al., 2009) can take into account a range of
changes in gene expression to better predict CRMs.
Online tools:
MatrixCatch (Deyneko et al., 2013) http://www.gene-
regulation.com/cgi-bin/mcatch/MatrixCatch.pl—works
with TFBS PWMs from the TRANSFAC R
database;
accepts a set of sequences in FASTA format
ModuleMiner (Loo et al., 2008) http://tomcatbackup.esat.
kuleuven.be/moduleminer/—accepts Ensembl gene IDs to
look for conserved CRMs upstream gene TSSs;
PC-TraFF (Meckbach et al., 2015) http://pctraff.bioinf.
med.uni-goettingen.de/—uses TRANSFAC R
PMWs on
gene IDs or sequences in FASTA format
DistanceScan (Shelest et al., 2010) https://www.omnifung.
hki-jena.de/Rpad/Distance_Scan/index.htm—requires an
output from FIMO or Match
oPOSSUM-3 (Kwon et al., 2012) http://opossum.cisreg.ca/
oPOSSUM3/—requires the name of the anchoring TF
MCAST (Grant et al., 2015) http://meme-suite.org/tools/
mcast—a tool from the extensive MEME suite; searches for
clusters of provided motifs in sequences in FASTA format
Cluster-Buster (Frith et al., 2003) http://zlab.bu.edu/
cluster-buster/—searches for motif clusters; accepts PMWs
in JASPAR or TRANSFAC R
formats
Offline tools:
ModuleDigger, CPModule, CORECLUST: stand-alone
programs that require a set of known PWMs as input (Sun
et al., 2009, 2012; Nikulova et al., 2012).
Validation of TFBSs can be carried out using a combination of
chromatin immunoprecipitation with an antibody specific to the
TF of interest, and real time PCR with primers specific to the
predicted target region.
There are numerous illustrations of application of promoter
analysis. For instance, analysis of promoters of protein coding
genes and those of long non-coding RNA have shown that
these two classes of genes tend to have different transcriptional
regulators: motifs for 140 TFs were found to be over-represented
in lncRNA gene promoters; this list of TFs includes nuclear
hormone receptors and FOX family proteins (Alam et al.,
2014). Dopamine-responsive genes have been shown to be
regulated by the CREB protein (Frith et al., 2004). Analysis of
melanocyte enhancers has predicted binding of key melanocyte
TFs, including SOX10 and MITF (Gorkin et al., 2012). Motifs of
6 TFs (Hb, Foxa1, Cf2-ii, Lhx3, Mef2a, and slp1) have been found
to be associated with insect bidirectional promoters (Behura and
Severson, 2015). Similar analyses in the human genome have
revealed 7 TFs (GABPA, MYC, E2F1, E2F4, NRF-1, CCAAT, and
YY1) associated with promoter bidirectionality (Lin et al., 2007).
Using promoter analysis, several ETS-domain TFs (GABPA,
ELK1, and ELK4) have been discovered as likely regulators of
breast cancer relevant sense-antisense gene pairs (Grinchuk et al.,
2015).
The Use of Motif Information Improves the
Accuracy of Binding Site Detection in
ChIP-seq and ChIP-exo Data
ChIP-seq and ChIP-exo (ChIP-x) experiments have been widely
used to define genomic positions of TF binding and discover
TF binding motifs. The usual way to process ChIP-x data is to
define TF binding regions first, then perform motif discovery to
Frontiers in Genetics | www.frontiersin.org 9February 2016 | Volume 7 | Article 24
Boeva Motif Analysis for TFBSs
FIGURE 7 | Illustration of the procedure for peak score calculation used by the MICSA algorithm.
FIGURE 8 | The number of Spi-1/PU.1 motif instances correlates with
the Spi-1/PU.1-binding intensity measured by the peak height. The
boxplot represents the distribution of the peak heights (y-axis) for each number
of Spi-1/PU.1 motif instances/peak (x-axis). The dark red squares indicate the
mean values, and the black line within each box indicates the median. The
Spearman coefficient correlation (ρ) and the p-value of correlation test are
reported (from Ridinger-Saison et al., 2012).
construct PWMs of TF binding motifs. In this section, we show
that simultaneous instead of successive analysis of ChIP-x signal
and motif instances improves the accuracy of TFBS prediction
(Boeva et al., 2010; Guo et al., 2012; Starick et al., 2015). Below,
we briefly describe the main elements of ChIP-x data analysis.
In the first step of ChIP-x data analysis, by extending each
read to the length of the initial immunoprecipitated DNA
fragment, it is possible to identify areas of fragment overlap and
locate candidate regions of TF-DNA binding. These regions with
high fragment density are called candidate peaks (Fejes et al.,
2008). Not every peak contains a true binding site. Low peaks
(with moderate read density) can appear by chance. Thus, to
characterize the read enrichment and discriminate true binding
from background noise, a statistical model needs to be applied.
There are more than 20 different tools that perform this task
for ChIP-x TF data (Wilbanks and Facciotti, 2010; Kim et al.,
2011). The background model may be based on the uniform
distribution of sequenced reads along the genome. Under such
a background model, a Poisson test can be applied to evaluate the
significance of read over-representation in a given region (Zhang
et al., 2008). Often, in the ChIP-seq protocol, a negative control
experiment is performed to assess the distribution of sequenced
reads in the background. Recent studies have shown that an
appropriate control data set is critical for analysis of any ChIP-seq
experiment, because of biases in DNA breakage during sonication
(Landt et al., 2012). The ChIP-exo datasets are usually generated
with negative controls.
In (Boeva et al., 2010), we presented a peak and motif
calling algorithm, MICSA, based on the idea that functional
binding sites of TFs should contain a consensus motif (or a
set of consensus motifs). The MISCA workflow consists of
four phases: (i) identification of all candidate peaks using read
extension, (ii) identification of binding motif PWMs from a
subset of peaks, (iii) detection of motif instances in all candidate
peaks, and (iv) optimization of the peak calling output by
calculating statistics taking into account information about both
motif instance and depth of coverage. Importantly, MICSA
identifies several binding motifs. The statistics calculated by
MICSA allow us to retain strong binding sites (i.e., regions
with high numbers of overlapping fragments) as well as weak
binding sites with strong motif instances in the peak center
(Figure 7). Weak binding sites without strong motif instances
are removed from the final dataset. When applied to a ChIP-
seq dataset for oncogenic TF EWS-FLI1, MICSA identified
two consensus motifs (Figure 6B): a (GGAA)6microsatellite,
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Boeva Motif Analysis for TFBSs
FIGURE 9 | Distribution of the distances between pairs of Spi-1/PU.1 and Klf-1 motif instances in direct or reverse orientation for genes activated and
repressed by Spi-1/PU.1. The x-axis shows the length of the spacers separating pairs of Spi-1 and Klf-1 motif instances. The y-axis shows the fraction of
sequences with at least one pair of motif instances separated by the selected spacer. Promoters with CpG island (GCI): green; promoter devoted to CpG island
(NoGCI): blue; enhancer regions: orange; intragenic regions: red. Data from Ridinger-Saison et al. (2012).
and a motif corresponding to the consensus RCAGGAARY,
further referred to as the ETS motif. Surprisingly, the ETS motif
did not coincide with the FLI1 binding motif (CCGGAARY),
although EWS-FLI1 and FLI1 make up the same DNA-
binding domain. Further analysis revealed the tendency of
sites bearing GGAA-microsatellites to activate the expression of
neighboring genes (sites found from 150-kb upstream to 50-
kb downstream of gene TSSs), while sites with the ETS motif
do not seem to have a definite activator function. In fact, ETS-
sites negatively affected gene expression when located in the
50-kb region downstream of the TSSs. When ETS sites were
located further away from gene TSSs (within 1 Mb upstream
or downstream), both activator and inhibitory action of EWS-
FLI1 was observed. More recent research from (Riggi et al.,
2014) has shown that EWS-FLI1 creates de novo enhancers
when it binds to GGAA-microsatellites, and may disrupt existing
regulatory elements of ETS family TFs when it binds to single
ETS-sites.
The idea of simultaneous analysis of the ChIP-x read density
signal and motif instances has been further developed by Guo
et al. (2012). Their GEM algorithm consists of five main steps:
(i) detect candidate binding regions, (ii) discover and cluster
sets of enriched k-mers, (iii) generate a positional prior for peak
calling using k-mer classes, (iv) predict binding sites with a k-
mer-based positional prior, and (v) re-discover enriched k-mer
clusters in peaks from (iv). On the one hand, by considering
motif information, the GEM method gives a better spatial
resolution of binding sites than other peak calling methods,
also enabling it to resolve closely-spaced binding events. On
the other hand, on 214 ENCODE ChIP-Seq experiments for
63 TFs, binding motifs discovered by GEM were overall closer
to the expected ones compared to motifs discovered by other
methods. In fact, in 15 cases out of 215, GEM outperformed
both MEME and ChIPmunk. Using the output of GEM on
ENCODE ChIP-seq data in five different cell lines, Guo et al.
(2012) studied pairwise binding relationships between different
TFs. As a result, 390 pairs of TFs were shown to have
significant binding distance constraints within a 100 bp distance,
including known interaction pairs MYC-MAX, FOS-JUN, and
CTCF-YY1.
The concept of combining ChIP-exo read density with
motif information has been employed in the ExoProfiler
computational pipeline (Starick et al., 2015). ExoProfiler searches
for both de novo motifs and known motifs from the JASPAR
database. It then extracts regions in ChIP-seq peaks centered on
motifs, and analyzes strand specific read density. By applying
ExoProfiler to glucocorticoid receptor (GR) ChIP-exo data,
Starick et al. (2015) discovered indirect binding of GR to
DNA via cofactors (FOX proteins) and discovered a novel GR
binding sequence (“combi motif”), at which a GR forms a
heterodimer with other TFs (ETS or TEAD families) to activate
transcription.
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Boeva Motif Analysis for TFBSs
Getting Insights into Physical Mechanisms
of Transcriptional Modulation:
Co-Directional Clustered Binding of the
Oncogenic TF Spi-1/PU.1 Modulates Gene
Expressionin Erythroleukemia
Spi-1/PU.1 belongs to the same ETS TF family as FLI1 (the
DNA-binding partner of EWS in the gene fusion causing
Ewing sarcoma). Spi-1/PU.1 expression beyond physiological
expression levels promotes oncogenesis in erythroid cells
(Rimmelé et al., 2010). Here, we refer to our study of Spi-1/PU.1
ChIP-seq data, where motif analysis allowed us to get insights
into mechanisms of how Spi-1/PU.1 physically modulates the
expression of its target genes (Ridinger-Saison et al., 2012).
Analysis of the Spi-1/PU.1 ChIP-seq dataset resulted in a total
of 17,781 binding site regions, which were assigned to genes using
the Nebula peak-to-gene annotation module (Boeva et al., 2012).
Of the 21 Spi-1/PU.1 binding sites tested, 20 were validated using
real time PCR. As we detected instances of the binding motif
in 88% of the Spi-1/PU.1-bound regions, we concluded that in
erythroleukemia, Spi-1/PU.1 binds to DNA directly.
Interestingly, bound to a gene or even to a gene promoter,
Spi-1/PU.1 rarely causes transcriptional modulation. Half of all
mouse genes contained Spi-1/PU.1 binding sites, i.e., within
a30 kb region upstream of the TSS to +5 kb downstream
of the transcription end, but only 8.1% (854 out of 10,560) of
the Spi-1/PU.1-occupied genes were transcriptionally modulated.
Therefore, we decided to study what additional factors influenced
the gene modulation activity of Spi-1/PU.1.
The first factor that correlated to the modulation status
of genes was the distance between gene TSS and Spi-1/PU.1
binding sites: 60% of Spi-1/PU.1-activated genes contained Spi-
1/PU.1 peaks in 5 kb area around TSSs, though only 40 and
22% of repressed and non-modulated genes, respectively, had
peaks within this distance around TSSs. A second factor was
the binding affinity, indicated by the peak height: peaks in the
promoters of activated genes were significantly higher than in
the promoters of repressed and non-modulated genes (p-value
<105). The binding affinity/peak height correlated with the
number of motif instances per peak (Figure 8). In agreement
with this observation, the number of Spi-1/PU.1 motif instances
in Spi-1/PU.1 ChIP-seq peaks in promoters of activated genes
was significantly higher than in promoters of repressed or non-
modulated genes (p-values <106). The third factor was the
presence of a CpG island. Our analysis also indicated that
Spi-1/PU.1 binding is favored at CG-rich sequences, but the
absence of CpG islands increases the potential of Spi-1/PU.1
to activate gene expression. A fourth factor was the orientation
of motif instances within a regulatory region. In cases when
Spi-1/PU.1 induces gene modulation (activation or repression),
Spi-1/PU.1 motif instances form co-oriented clusters (head-
to-tail orientation). We observed these clusters of co-oriented
motifs both in promoters of up-regulated genes, and enhancers
of down-regulated genes. The fifth factor was the distance
and orientation of Spi-1/PU.1 binding motifs, and motifs of
other TFs. To get this information, we scanned ChIP-seq peak
sequences with PWMs of known TFs using PATSER (Hertz
and Stormo, 1999; Transfac and Jaspar motifs libraries). The
most striking pattern was observed for pairs of Spi-1/PU.1 and
KLF family motifs (Figure 9). For instance, in promoters of
Spi-1/PU.1-up-regulated genes, we observed an enrichment of
Spi-1/PU.1-KLF pairs where the direct KLF motif immediately
follows the direct Spi-1/PU.1 motif. The patterns observed
suggest cooperative interactions between Spi-1/PU.1 and KLF
family TFs. The functional significance of these observations
needs to be validated by biological experiments.
CONCLUSION
Sequence analysis methods are extremely useful for decrypting
the complex structure of patterns and motifs present in
eukaryotic genomes. In particular, motif discovery methods
applied to promoter/enhancer or ChIP-seq peak sequences
enable detection of TFBSs in genomic DNA. In this review, we
have presented de novo motif discovery techniques, and methods
to find over-represented binding motifs of TFs with known
motifs (PWMs). We have demonstrated that the application of
these techniques improves accuracy of peak calling during ChIP-
seq data analysis, and may provide novel biological insights into
mechanisms of transcriptional regulation when sequence analysis
is coupled with the analysis of gene expression changes. We
expect that with time, motif discovery methods will become
even more user-friendly, and will allow rapid processing of large
datasets, while TRANSFAC R
, JASPAR, and other databases will
include an increasing number of TF motifs extracted from ChIP-
seq experiments.
AUTHOR CONTRIBUTIONS
The author confirms being the sole contributor of this work and
approved it for publication.
ACKNOWLEDGMENTS
This work has been supported by The INSERM Atip-Avenir
Program and The ARC Foundation.
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Conflict of Interest Statement: The author declares that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2016 Boeva. This is an open-access article distributed under the terms
of the Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) or licensor
are credited and that the original publication in this journal is cited, in accordance
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which does not comply with these terms.
Frontiers in Genetics | www.frontiersin.org 15 February 2016 | Volume 7 | Article 24
... TFs often co-bind in a specific manner to control the gene expression: order, spacing, orientation, and affinity of binding sites within a CRE are important for TF binding which is referred to as TF binding grammar (Jindal et al 2021). TFs can interact with each other when they co-bind in a close genomic vicinity or compete to their spatially closely located binding sites (Boeva 2016). Furthermore, TF spatial occupancy around its binding sites is another important feature to regulate gene expression. ...
... For this aim, lab-based ChIP-exo approaches such as ChIP-nexus (He et al 2015) method have been developed to detect smaller binding regions, and also TF binding sites detection methods have been improved computationally. TFs generally tend to bind to a set of DNA sequences having high affinity for binding TFs referred to as binding site motifs (Boeva 2016). These TF binding site motifs have been modelled as consensus DNA IUPAC sequence, position weight matrices (PWMs), and k-mers (Boeva 2016). ...
... TFs generally tend to bind to a set of DNA sequences having high affinity for binding TFs referred to as binding site motifs (Boeva 2016). These TF binding site motifs have been modelled as consensus DNA IUPAC sequence, position weight matrices (PWMs), and k-mers (Boeva 2016). Various methods have been developed to prioritize the best binding sites motifs in detected binding regions from ChIP-seq experiments. ...
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Transcription factors (TFs) binding are in the core of the Regulatory networks studies. ChIP-seq experiments are available for many TFs in various species. As TFs co-bind in cis-regulatory elements regions to control gene expression, studying the existing relation among co binding TFs such as distance of binding sites or co occupancy are highly important to understand the regulatory mechanisms. Currently, to detect binding sites of each TF in cis-regulatory elements, first binding regions of each TF are detected by standard peak calling methods, and at the second step the best candidate binding sites are prioritized by motif detection methods in binding regions. However, it is well-known that the best prioritized candidate motifs are not necessarily the actual binding sites of TFs. Furthermore, motif prioritizing methods that consider more genomic features complexities of TFs bindings are usually computationally expensive methods. Here, we tend to improve the TF binding sites accuracy detection by using the original ChIP-seq signal. The motifs which are located closer to the summits of binding region peaks are more likely to be the actual binding sites. Therefore, We developed a novel post-processing Bioconductor package called Motif2Site to detect TFs binding sites from user provided motif sets and recenter them across experiments. We applied Motif2Site method to detect TF binding sites for major mouse embryonic stem cell (mESC) as well as mouse fetal and birth time (P0) heart TFs. Motif2Site could detect binding regions with comparable accuracy to the existing state-of-the-art while it substantially increased the accuracy of the detected binding sites. Motif2Site could future improve the accuracy of binding sites prediction by recentering binding sites across developmental conditions (fetal/P0 heart) and across homologous TFs (ex. GATA4/GATA6 and MEF2A/MEF2C). Purifying high-confidence binding sites in mouse fetal heart, enabled us to study the co-binding properties of TFs in cis-regulatory elements. We could also traced TFs footprints in selected heart-specific VISTA enhancers chromatin accessible regions.
... This suggests that at least some, if not most, of those TFs may serve as anchors for recruitment of p53 R273H to the promoters of R273 signature genes. Interestingly, many of those TFs bind specifically to GCrich DNA sequences 49 and contain CpG dinucleotides within their recognition motif 50 . Congruently, the promoters of the R273 signature genes were found to be highly enriched for CpG islands (Supplementary Fig. 7e). ...
Article
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The TP53 gene is mutated in approximately 60% of all colorectal cancer (CRC) cases. Over 20% of all TP53-mutated CRC tumors carry missense mutations at position R175 or R273. Here we report that CRC tumors harboring R273 mutations are more prone to progress to metastatic disease, with decreased survival, than those with R175 mutations. We identify a distinct transcriptional signature orchestrated by p53R273H, implicating activation of oncogenic signaling pathways and predicting worse outcome. These features are shared also with the hotspot mutants p53R248Q and p53R248W. p53R273H selectively promotes rapid CRC cell spreading, migration, invasion and metastasis. The transcriptional output of p53R273H is associated with preferential binding to regulatory elements of R273 signature genes. Thus, different TP53 missense mutations contribute differently to cancer progression. Elucidation of the differential impact of distinct TP53 mutations on disease features may make TP53 mutational information more actionable, holding potential for better precision-based medicine.
... Generally, TFs exist as modular proteins containing a DNA-binding domain that interacts with cis-elements of their target genes [52]. Moreover, it also consists of a protein-protein interaction domain that assists oligomerization between TFs or with other regulators [53]. We hypothesized that miRNAs could act with targets that are TF, and thus influence other genes, resulting in the changing of transcriptome profiles in somaclonal lines. ...
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The role of miRNAs in connection with the phenomenon of somaclonal variation, which occurs during plant in vitro culture, remains uncertain. This study aims to investigate the possible role of miRNAs in multi-omics regulatory pathways in cucumber somaclonal lines. For this purpose, we performed sRNA sequencing (sRNA-seq) from cucumber fruit samples identified 8, 10 and 44 miRNAs that are differentially expressed between somaclones (S1, S2, S3 lines) and the reference B10 line of Cucumis sativus. For miRNA identification, we use ShortStack software designed to filter miRNAs from sRNAs according to specific program criteria. The identification of predicted in-silico targets revealed 2,886 mRNAs encoded by 644 genes. The functional annotation of miRNA’s target genes and gene ontology classification revealed their association with metabolic processes, response to stress, multicellular organism development, biosynthetic process and catalytic activity. We checked with bioinformatic analyses for possible interactions at the level of target proteins, differentially expressed genes (DEGs) and genes affected by genomic polymorphisms. We assume that miRNAs can indirectly influence molecular networks and play a role in many different regulatory pathways, leading to somaclonal variation. This regulation is supposed to occur through the process of the target gene cleavage or translation inhibition, which in turn affects the proteome, as we have shown in the example of molecular networks. This is a new approach combining levels from DNA-seq through mRNA-seq, sRNA-seq and in silico PPI in the area of plants’ somaclonal variation.
... The current analysis discovered multiple binding motifs for MAGE genes, which is significant to find all possible binding motifs for the same TF and co-factor binding motifs [20]. Likewise, the analysis revealed multiple binding sites in the promoter region of candidate motifs, which could be used to strengthen binding interactions and different regulatory effect [21]. ...
Article
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Background: Melanoma Antigen Genes (MAGEs) are a family of genes that have piqued the interest of scientists for their unique expression pattern. The MAGE genes can be classified into type I MAGEs that expressed in testis and other reproductive tissues while type II MAGEs that have broad expression in many tissues. Several MAGE gene families are expressed in embryonic tissues in almost all eukaryotes, which is essential for embryo development mainly during germ cell differentiation. The aim of this study was to analyze the promoter regions and regulatory elements (transcription factors and CpG islands) of MAGE genes encoding for embryonic development in cattle. Results: The in silico analysis revealed the highest promoter prediction scores (1.0) for TSS were obtained for two gene sequences (MAGE B4-like and MAGE-L2) while the lowest promoter prediction scores (0.8) was obtained for MAGE B17-like. It also revealed that the best common motif, motif IV, bear a resemblance with three TF families including Zinc-finger family, SMAD family and E2A related factors. From thirteen identified TFs candidates, majority of them (11/13) were clustered to Zinc-finger family serving as transcriptionally activator role whereas three (SP1, SP3 and Znf423) of them as activator or repressor in response to physiological and pathological stimuli. On the other hand we revealed slightly rich CpG islands in the gene body and promoter regions of MAGE genes encoding for embryonic development in cattle. Conclusion: This in silico analysis of gene promoter regions and regulatory elements in MAGE genes could be useful for understanding regulatory networks and gene expression patterns during embryo development in bovine.
... The PWM is also used for the binding specificity of a transcription factor (TF) [50]. It can be used to scan a sequence for the presence of DNA words, which are comparatively more similar to the PWM than to the background [51,52]. Authors in [53] evaluated the Bayesian network and a support vector machine (SVM) on four different TF binding sitebased datasets, and analyzed their performances using PWM. ...
Article
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The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models.
... In addition, conserved motifs analysis was used to determine transcriptional activity, protein-protein interactions, and DNA-binding activity of TFs [44]. The conserved motifs analysis of pecan AP2/ERF TFs indicated that these motifs might be related to specific functions. ...
Article
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The ethylene-responsive element (AP2/ERF) is one of the keys and conserved transcription factors (TFs) in plants that play a vital role in regulating plant growth, development, and stress response. A total of 202 AP2/ERF genes were identified from the pecan genome and renamed according to the chromosomal distribution of the CiAP2/ERF genes. They were divided into four subfamilies according to the domain and phylogenetic analysis, including 26 AP2, 168 ERF, six RAV, and two Soloist gene family members. These genes were distributed randomly across the 16 chromosomes, and we found 19 tandem and 146 segmental duplications which arose from ancient duplication events. The gene structure and conserved motif analysis demonstrated the conserved nature of intron/exon organization and motifs among the AP2/ERF genes. Several cis-regulatory elements, which were related to light responsiveness, stress, and defense responses, were identified in the promoter regions of AP2/ERFs. The expression profiling of 202 CiAP2/ERF genes was assessed by using RNA-Seq data and qRT-PCR during development (pistillate flowering development, graft union development, and kernel development) and under abiotic stresses (waterlogging, drought). Moreover, the results suggested that the ERF-VII members may play a critical role in waterlogging stress. These findings provided new insights into AP2/ERF gene evolution and divergence in pecan and can be considered a valuable resource for further functional validation, as well as for utilization in a stress-resistance-variety development program.
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To gain insights into the molecular interactions of an intracellular pathogen and its host cell, we studied the gene expression and chromatin states of human fibroblasts infected with the Apicomplexan parasite Toxoplasma gondii . We show a striking activation of host cell genes that regulate a number of cellular processes, some of which are protective of the host cell, others likely to be advantageous to the pathogen. The simultaneous capture of host and parasite genomic information allowed us to gain insights into the regulation of the T . gondii genome. We show how chromatin accessibility and transcriptional profiling together permit novel annotation of the parasite’s genome, including more accurate mapping of known genes and the identification of new genes and cis -regulatory elements. Motif analysis reveals not only the known T . gondii AP2 transcription factor-binding site but also a previously-undiscovered candidate TATA box-containing motif at one-quarter of promoters. By inferring the transcription factor and upstream cell signaling responses involved in the host cell, we can use genomic information to gain insights into T . gondii’s perturbation of host cell physiology. Our resulting model builds on previously-described human host cell signalling responses to T . gondii infection, linked to induction of specific transcription factors, some of which appear to be solely protective of the host cell, others of which appear to be co-opted by the pathogen to enhance its own survival.
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Cis-regulatory elements (CREs) are non-coding parts of the genome that play a critical role in gene expression regulation. Enhancers, as an important example of CREs, interact with genes to influence complex traits like disease, heat tolerance and growth rate. Much of what is known about enhancers come from studies of humans and a few model organisms like mouse, with little known about other mammalian species. Previous studies have attempted to identify enhancers in less studied mammals using comparative genomics but with limited success. Recently, Machine Learning (ML) techniques have shown promising results to predict enhancer regions. Here, we investigated the ability of ML methods to identify enhancers in three non-model mammalian species (cattle, pig and dog) using human and mouse enhancer data from VISTA and publicly available ChIP-seq. We tested nine models, using four different representations of the DNA sequences in cross-species prediction using both the VISTA dataset and species-specific ChIP-seq data. We identified between 809,399 and 877,278 enhancer-like regions (ELRs) in the study species (11.6–13.7% of each genome). These predictions were close to the ~8% proportion of ELRs that covered the human genome. We propose that our ML methods have predictive ability for identifying enhancers in non-model mammalian species. We have provided a list of high confidence enhancers at https://github.com/DaviesCentreInformatics/Cross-species-enhancer-prediction and believe these enhancers will be of great use to the community.
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