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diff --git a/main/ch_encode_peaks.aux b/main/ch_encode_peaks.aux
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-\newlabel{encode_peaks_ctcf_association}{{3.7}{38}{\textbf {CTCF motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The CTCF dataset ORs are too high to be represented in this plot. \textbf {B} Density of CTCF motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif. The absence of CTCF motif within the first 70bp around CTCF binding sites is explained by the peak processing (see section \ref {encode_peaks_methods_data}).\relax }{figure.caption.25}{}}
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diff --git a/main/ch_introduction.tex b/main/ch_introduction.tex
index 4fc5a92..fa4d28f 100644
--- a/main/ch_introduction.tex
+++ b/main/ch_introduction.tex
@@ -1,129 +1,150 @@
\cleardoublepage
\chapter{Introduction}
\label{intro}
\markboth{Introduction}{Introduction}
\addcontentsline{toc}{chapter}{Introduction}
Each living organism contains DNA which is the molecular support on which genes are encoded. Genes are the hereditary unit of life and code for a set of instructions involved in all the aspects of life, from an organism development to the functions of a specific cell type. However, since all these instructions are not needed at the same time, gene expression needs to be regulated.
Transcription factors (TFs) form an important class of nuclear proteins that can bind to specific DNA sequences and drive target gene expression. Thus, in order to control gene expression, the activity of TFs needs to be tightly regulated.
This work report the results of different computational studies, all focuses on TFs binding sites, and explore regulatory regions of the genome, TF sequence specificity and the organization of chromatin around TF binding sites.
\section{About chromatin}
In eukaryotes, the DNA is stored in the nucleus. In human, each cell contains about two meters of DNA. In order to fit the DNA inside the nucleus, the cells have to organize and compact the genome while maintaining it readable. Unbeatable, evolution came out with an elegant solution : the chromatin. The chromatin is the association of the DNA with specialized proteins - the histones - around which the it wraps, resulting in a genome compaction The histone/DNA structure is also associated with other protein families such as RNA/DNA polymerases, helicases and TFs which forms the chromatin.
% 1) structure, histones, nucleosomes, genome compaction
% 2) nucleosome positioning, statistical positioning, sequence positioning
% 3) measuring nucleosome occupancy (MNase-seq)
% 4) histone PTMs, euchromatin, heterochromatin, TAD
% 5) regulatory regions (TF binding, NDR, open chromatin)
% 6) DGF / DNase-seq / ATAC-seq, footprint
% 7) pioneering TF
\subsection{The chromatin structure}
% structure, histones, nucleosomes, genome compaction
-\begin{figure}[!htbp]
+\begin{figure}
\begin{center}
\includegraphics[scale=0.2]{images/ch_introduction/chromatin.png}
\captionof{figure}{\textbf{A} Top view of a nucleosome core particle (NCP) displayed as a ribbon representation on the left and space filling representation on the left. The NCP is made of a four hetero-dimers histone octamer around which 146-148 DNA bp wraps. The histone tails protrude out of the nucleosome core particle and are accessible to other factors, unlike the inner part of the histone octamer. Taken and modified from \cite{mcginty_robert_k._and_tan_song_fundamentals_2014}. \textbf{B} The chromatin structure. Inside eukaryotes, DNA is wrapped around histones cores forming nucleosomes. Nucleosomes can then be organized into higher-level helical-like structure, compacting the DNA. The ultimate compaction state is reached at mitosis meta-phase, when the mitotic chromosomes are visible.}
\label{intro_chromatin}
\end{center}
\end{figure}
% histones
-In human, there are four major (canonical) histones : H2A, H2B, H3 and H4. These four histones are found assembled together into an octamer, composed of two H2A/H2B and two H3/H4 hetero-dimers, around which ~146/8bp of DNA wrap (Figure \ref{intro_chromatin}A). This structure is called the nucleosome core particule (that I will later simply refer to as "nucleosome").
+In human, there are four major (canonical) histones : H2A, H2B, H3 and H4. These four histones are found assembled together into an octamer, composed of two H2A/H2B and two H3/H4 hetero-dimers, around which ~146/8bp of DNA wrap (Figure \ref{intro_chromatin}A), forming the nucleosome core particule (that I will later simply refer to as "nucleosome"). The DNA is kept wrapped around the histone octamer because of strong electrostatic interactions. Indeed, the DNA backbone, which is negatively charged in nuclear conditions, shows a high affinity for the positively charged histones. As a consequence, the nucleosome is a quite stable structure.
The histones proteins are highly conserved among eukaryotes at both the sequence and the structure level. All the histones share the overall same design. They are composed of a N-terminal tail, a central histone-fold domain and of C-terminal tail. Histones associate with each other through their histone-fold domains which compose the center of the nucleosome. In contrast, the histone N-terminal tails are extruding out from the nucleosome and are hotspots for post-translational modifications (PTMs) \citep{kouzarides_chromatin_2007}.
For completeness, it should be mentioned that "variant histones" - also called "replacement histone", by opposition to the "canonical replicative histones" - exist and can replace canonical histones in nucleosomes, at specific genome locations. to fulfill dedicated functions \citep{henikoff_histone_2015}. However, this topic is outside of this work scope.
% chromatin fibers
The genome is organized into a repetition of nucleosomes, each separated by a linker DNA, forming the 11-nm chromatin fiber. Under this conformation, the chromatin is quite relaxed and the DNA accessible. The 11-nm fiber is itself stored into a more compacted structure called the 30-nm fiber (Figure \ref{intro_chromatin}B). Under the inclusion of the H1 linker histone, the nucleosome arrays are organized into a higher level structure, diminishing the linker DNA accessibility and further increasing the genome compaction level \citep{jolma_methods_2011-2, mcginty_robert_k._and_tan_song_fundamentals_2014}.
% compaction
It is now commonly accepted that the compaction of the genome comes with a trade-off. The DNA sequences found in nucleosomes are though the be unaccessible for DNA reading processes such as TF binding whereas the linker DNA remains accessible \citep{jolma_methods_2011-2}. Thus storing the genome impedes its readability. Because transcribing genes is all about reading the DNA template, the state of the chromatin eventually impact gene expression. Consequently, the cell faces a situation where it needs to keep only the immediately useful genomic regions readable while keeping the ability to open/close other regions on demand.
-\subsection{The chromatin is a dynamic structure}
+\subsection{The chromatin is dynamic}
% chromatin modification/remodelling
-Because the cell needs may vary, for instance because of lineage commitment, the chromatin structure needs to be adapted. Some regions needs to become accessible in order to be read while other are not needed anymore. Consequently, the chromatin is a highly dynamic structure that undergoes constant modifications. Two broad families of chromatin modifier complexes exist : ATPase chromatin remodelers and histone modifiers.
+Because the cell needs may vary, for instance because of lineage commitment, the chromatin structure needs to be adapted. Some regions needs to become accessible in order to be read while other are not needed anymore. Consequently, the chromatin is a highly dynamic structure that undergoes constant modifications. Two broad families of chromatin modifier exist : ATPase chromatin remodelers and histone modifiers.
% chromatin remodelers
-ATPase chromatin remodelers is a group of proteins that are able to affect the chromatin packaging by interfering directly with the nucleosome, at the cost of hydrolyzing ATP molecules. Chromatin remodelers can be subdivided into 4 sub-groups, each fulfilling a different function \citep{langst_chromatin_2015}. SWI/SNF members can slide and/or to evict nucleosomes from DNA and are linked with chromatin opening. ISWI members tend to recognize unmodified H4 histone and catalyze nucleosome spacing and chromatin compaction. CHD members are less well functionally characterized but bear chromo domains that allows them to recognize histone methylation. Finally, INO80 seems to be able to slide and evict nucleosomes and to recognize Hollidays junction and the DNA replication fork, suggesting a role for DNA repair and replication.
+ATPase chromatin remodelers are a group of proteins that are able to affect the chromatin packaging by interfering directly with the nucleosome, at the cost of hydrolyzing ATP molecules. Chromatin remodelers can be subdivided into 4 sub-groups, each fulfilling a different function \citep{langst_chromatin_2015}. SWI/SNF members can slide and/or to evict nucleosomes from DNA and are linked with chromatin opening. ISWI members tend to recognize unmodified H4 histone and catalyze nucleosome spacing and chromatin compaction. CHD members are less well functionally characterized but bear chromo domains that allows them to recognize histone methylation. Finally, INO80 members seem to be able to slide and evict nucleosomes and to recognize Hollidays junction and the DNA replication fork, suggesting a role in DNA repair and replication.
% histone modifiers
Histone modifiers are enzymes that can deposite PTMs on the histone tails. Different types of PTMs exist such as acetylation or methylation. Each histone has several residues that can be modified, sometimes together. This leads to an astonishingly high number of possibilities. So far more than a hundred histone PTMs have been identified, each linked with different biological functions. If the deposition of PTMs is made by dedicated factors (also called writers), this is also true for their recognitions \citep{kouzarides_chromatin_2007,hyun_writing_2017}. The direct consequence is that histone PTMs are used to recruit specific factors at given genomic location. For instance, H3 lysine 4 di-methylation (H3K4me2) has been shown to be enriched at the promoters of actively transcribed genes and at enhancers \citep{hyun_writing_2017,zhou_charting_2011} and to be specifically recognized by CHD1, a member of the CHD chromatin remodelers \citep{hyun_writing_2017}.
+\subsection{Measuring nucleosome occupancy}
+MNAase-seq
+
\subsection{About nucleosome positioning}
-statistical positioning, sequence positioning
+% statistical positioning, sequence positioning
+
+\begin{figure}
+\begin{center}
+ \includegraphics[scale=0.2]{images/ch_introduction/nucleosome_positioning.png}
+ \captionof{figure}{\textbf{Nucleosome positioning} \textbf{A} Activated gene transcription start site (TSS) region. The nucleosomes located immediately downstream of the TSS show a strong positioning. The positioning of the first nucleosome can be influence by sequence preferences. Eventually the phasing is propagated to neighboring nucleosomes through statistical positioning. The nucleosome array is not anymore visible as the nucleosomes become fuzzily positioned among the cells. \textbf{B} Influence of the rotational positioning on the sequence accessibility. Left, a sequence (indicated by the black ‘rungs’ on the DNA helix) has its major groove facing toward the nucleosome outside and is accessible. Center, a 5bp rotation of the nucleosome hides the sequence as its major groove is not facing the histone octamer. Right, another 5bp rotation makes the sequence accessible again. Both images are taken and adapted from \citep{jiang_nucleosome_2009}.}
+\label{intro_nucleosome_positioning}
+\end{center}
+\end{figure}
+
+The advent of MNase-seq allowed to draw high resolution maps of nucleosome occupancy in many species, for instance in yeast \citep{kubik_nucleosome_2015}, mouse \citep{west_nucleosomal_2014}, human \citep{schones_dynamic_2008, gaffney_controls_2012}.
+
+% strongly positioned nucleosomes
+The wealth of data collected allowed to determined that nucleosomes are not packaging the genome by covering it uniformly. Nucleosome rather seems to show preferred location were they sit at. Interestingly, single nucleosome can be visualize from batch sequencing experiment, indicating that an important fraction of the cells all bear a nucleosome at the same position. In these cases, the nucleosome is said to be "phased" or "strongly positioned" (see Figure \ref{intro_nucleosome_positioning}A).
+
+% statistical positioning
+Nucleosome arrays are a striking case of strongly positioned nucleosome. Arrays can be seen at throughout the human genome \citep{gaffney_controls_2012}. However, there are regions where they are enriched, for instance at the CCCTC-binding factor (CTCF) binding sites \citep{fu_insulator_2008}. In this case, it has been proposed that the arrays result from the nucleosomes organizing with respect to a barrier (or anchor). In this case, the barrier is CTCF. The regular array organization has been proposed to be propagated far from their anchors because the immediately flanking nucleosome positioning are constrained by the barrier. In turn, these nucleosomes constrain the lateral freedom of movement of the following ones, and so one. Eventually, an array is formed. However, the degree of constrain diminishes at each new nucleosome, leading the signal to blur out at some point. There, the nucleosomes are not sufficiently phased anymore throughout the cell population. This model is called the "statistical positioning" \citep{jiang_nucleosome_2009}.
+
+% effect of sequence
+Another important driver of nucleosome positioning is the DNA sequence. For instance, strongly positioned nucleosomes are also visible at the transcription start sites (TSSs) of activated genes. In this case, the DNA sequence composition seem to be a major factor influencing the nucleosome positioning \citep{dreos_influence_2016}. Because the DNA is wrapped around the histone octamer, the bound DNA is required to be bendable. WW (W=A/T) and SS (S=C/G) dinucleotides have been shown to curve the DNA by extending the major and the minor groove respectively \citep{jiang_nucleosome_2009}. However, because the major and minor grooves precess around the DNA helix axis, each groove alternatively face the nucleosome center (the histone octamer) and the nucleosome outside (the opposite direction) every ~5bp (thus the DNA helix periodicity is ~10.4bp, see Figure \ref{intro_nucleosome_positioning}B). Consequently, dinucleotides favoring DNA flexibility are required to occur at different locations around the nucleosome, according to their effect on the DNA helix structure. For instance, stretching the major groove needs to occur when it is facing the nucleosome outside, to force the adjacent DNA segment to be curved toward (around) the nucleosome center. This is referred to as "rotational positioning" \citep{jiang_nucleosome_2009}. Interestingly, if a nucleosome is bound to a favorable sequence, the next most likely favorable binding sites are located 10bp upstream or downstream. These correspond to the locations at which all the dinucleotides will reacquire the same orientation with respect to the histone octamer. However, any other nucleosome displacement will modify the orientations of the sequences wrapped around. This has the potential of making them accessible - if they are oriented toward the nucleosome outside - or hidden - if they are facing the nucleosome core (Figure \ref{intro_nucleosome_positioning}B). In 2011, Trifonov identified the YRRRRRYYYYYR (where R=A/G and Y=C/T) consensus sequence to be a nucleosome positioning sequence matching these criteria \citep{trifonov_cracking_2011}. The first and last positions indicate the cyclic nature of this pattern.
+
+In vivo, both statistical and rotational positioning occur. Additionally, chromatin remodelers are also constantly catalyzing thermodynamically unfavorable nucleosome displacement reaction in exchange of ATP hydrolysis. It is likely that each nucleosome is subjected to all of these phenomenons. However, one may be predominant over the others.
-\subsection{Measuring nucleosome occupancy}
-MNAase-seq
\subsection{About chromatin domains}
histone PTMs, euchromatin, heterochromatin, TAD
-\citep{}
\subsection{Regulatory elements}
TF binding, motifs, NDR, open chromatin
\subsection{Pioneering factors, a special class of TFs}
pioneering TFs
\subsection{Digital footprinting}
DGF / DNase-seq / ATAC-seq, footprint
\section{About transcription factors}
Transcription factors (TFs) are a special class of proteins that posses a DNA binding domain (DBD). This DBD allows them to recognizes specific DNA sequences and to selectively bind them.
Another few words about TF sequence specificity / affinity
1) specificity models, additivity, sequence scoring given model
2) TF complexes
3) co-binding scenarios
4) in vivo (ChIP-seq)
5) in vitro (HT-SELEX, PBM, B1H)
\subsection{How chromatin affects TF binding}
Jolma and Taipale book 2011 chapter 9
Jolma and Taipale book 2011 chapter 11
\subsection{Modeling sequence specificity}
models, additivity, sequence scoring given model
\subsection{TF co-binding}
TF complexe, homo-dimer, hetero-dimers, independent co-binding
Jolma and Taipale book 2011 chapter 8
Jolman and Taipale book 2011 chapter 11.4 (nucleosome breathing / TFs cooperate to evict nucleosome and open chromatin)
\subsection{Measuring TF binding in vivo}
ChIP-seq
\subsection{Measuring TF binding in vitro}
HT-SELEX, PBM, B1H
\section{Data analysis}
current technologies limitations
cannot assess exactly the binding site, both using ChIP-seq or in vitro measures
-> need to realign the data
-> need to reorient the data
Gaffney et al and ArchAlign proposed realignment procedures for MNase-seq
Lawrence and Rilley proposed realignment procedure for sequences
MEME as well
both cases, data heterogeneity is captured
-> clustering to resolve this, list some methods (NOT MINE)
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diff --git a/my_thesis.bbl b/my_thesis.bbl
index 0ab6081..da90dbe 100644
--- a/my_thesis.bbl
+++ b/my_thesis.bbl
@@ -1,716 +1,750 @@
\begin{thebibliography}{}
\bibitem[Adey et~al., 2010]{adey_rapid_2010}
Adey, A., Morrison, H.~G., {Asan}, Xun, X., Kitzman, J.~O., Turner, E.~H.,
Stackhouse, B., MacKenzie, A.~P., Caruccio, N.~C., Zhang, X., and Shendure,
J. (2010).
\newblock Rapid, low-input, low-bias construction of shotgun fragment libraries
by high-density in vitro transposition.
\newblock {\em Genome Biology}, 11(12):R119.
\bibitem[Aerts et~al., 2003]{aerts_toucan:_2003}
Aerts, S., Thijs, G., Coessens, B., Staes, M., Moreau, Y., and Moor, B.~D.
(2003).
\newblock Toucan: deciphering the cis ‐regulatory logic of coregulated genes.
\newblock {\em Nucleic Acids Research}, 31(6):1753--1764.
\bibitem[Aibar et~al., 2017]{aibar_scenic:_2017}
Aibar, S., González-Blas, C.~B., Moerman, T., Huynh-Thu, V.~A., Imrichova, H.,
Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van~den
Oord, J., Atak, Z.~K., Wouters, J., and Aerts, S. (2017).
\newblock {SCENIC}: single-cell regulatory network inference and clustering.
\newblock {\em Nature Methods}, 14(11):1083--1086.
\bibitem[Ambrosini et~al., 2016a]{ambrosini_chip-seq_2016}
Ambrosini, G., Dreos, R., Kumar, S., and Bucher, P. (2016a).
\newblock The {ChIP}-{Seq} tools and web server: a resource for analyzing
{ChIP}-seq and other types of genomic data.
\newblock {\em BMC Genomics}, 17:938.
\bibitem[Ambrosini et~al., 2016b]{ambrosini_chip-seq_2016-1}
Ambrosini, G., Dreos, R., Kumar, S., and Bucher, P. (2016b).
\newblock The {ChIP}-{Seq} tools and web server: a resource for analyzing
{ChIP}-seq and other types of genomic data.
\newblock {\em BMC Genomics}, 17(1):938.
\bibitem[Ambrosini et~al., 2018]{ambrosini_pwmscan:_2018}
Ambrosini, G., Groux, R., and Bucher, P. (2018).
\newblock {PWMScan}: a fast tool for scanning entire genomes with a
position-specific weight matrix.
\newblock {\em Bioinformatics}, 34(14):2483--2484.
\bibitem[Ambrosini et~al., 2003]{ambrosini_signal_2003}
Ambrosini, G., Praz, V., Jagannathan, V., and Bucher, P. (2003).
\newblock Signal search analysis server.
\newblock {\em Nucleic Acids Research}, 31(13):3618--3620.
\bibitem[Angerer et~al., 2017]{angerer_single_2017}
Angerer, P., Simon, L., Tritschler, S., Wolf, F.~A., Fischer, D., and Theis,
F.~J. (2017).
\newblock Single cells make big data: {New} challenges and opportunities in
transcriptomics.
\newblock {\em Current Opinion in Systems Biology}, 4:85--91.
\bibitem[Bailey et~al., 2015]{bailey_znf143_2015}
Bailey, S.~D., Zhang, X., Desai, K., Aid, M., Corradin, O., Cowper-Sal·lari,
R., Akhtar-Zaidi, B., Scacheri, P.~C., Haibe-Kains, B., and Lupien, M.
(2015).
\newblock {ZNF}143 provides sequence specificity to secure chromatin
interactions at gene promoters.
\newblock {\em Nature Communications}, 2:6186.
\bibitem[Bailey et~al., 2009]{bailey_meme_2009}
Bailey, T.~L., Boden, M., Buske, F.~A., Frith, M., Grant, C.~E., Clementi, L.,
Ren, J., Li, W.~W., and Noble, W.~S. (2009).
\newblock {MEME} {Suite}: tools for motif discovery and searching.
\newblock {\em Nucleic Acids Research}, 37(suppl\_2):W202--W208.
\bibitem[Barrett et~al., 2011]{barrett_ncbi_2011}
Barrett, T., Troup, D.~B., Wilhite, S.~E., Ledoux, P., Evangelista, C., Kim,
I.~F., Tomashevsky, M., Marshall, K.~A., Phillippy, K.~H., Sherman, P.~M.,
Muertter, R.~N., Holko, M., Ayanbule, O., Yefanov, A., and Soboleva, A.
(2011).
\newblock {NCBI} {GEO}: archive for functional genomics data sets—10 years
on.
\newblock {\em Nucleic Acids Research}, 39(suppl\_1):D1005--D1010.
\bibitem[Barski et~al., 2007]{barski_high-resolution_2007}
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D.~E., Wang, Z., Wei,
G., Chepelev, I., and Zhao, K. (2007).
\newblock High-{Resolution} {Profiling} of {Histone} {Methylations} in the
{Human} {Genome}.
\newblock {\em Cell}, 129(4):823--837.
\bibitem[Beckstette et~al., 2006]{beckstette_fast_2006}
Beckstette, M., Homann, R., Giegerich, R., and Kurtz, S. (2006).
\newblock Fast index based algorithms and software for matching position
specific scoring matrices.
\newblock {\em BMC Bioinformatics}, 7:389.
\bibitem[Berest et~al., 2018]{berest_quantification_2018}
Berest, I., Arnold, C., Reyes-Palomares, A., Palla, G., Rasmussen, K.~D.,
Helin, K., and Zaugg, J. (2018).
\newblock Quantification of differential transcription factor activity and
multiomics-based classification into activators and repressors: {diffTF}.
\newblock {\em bioRxiv}.
\bibitem[Berger and Bulyk, 2009]{berger_universal_2009}
Berger, M.~F. and Bulyk, M.~L. (2009).
\newblock Universal protein-binding microarrays for the comprehensive
characterization of the {DNA}-binding specificities of transcription factors.
\newblock {\em Nature Protocols}, 4(3):393--411.
\bibitem[Boller et~al., 2018]{boller_defining_2018}
Boller, S., Li, R., and Grosschedl, R. (2018).
\newblock Defining {B} {Cell} {Chromatin}: {Lessons} from {EBF}1.
\newblock {\em Trends in Genetics}, 34(4):257--269.
\bibitem[Boller et~al., 2016]{boller_pioneering_2016}
Boller, S., Ramamoorthy, S., Akbas, D., Nechanitzky, R., Burger, L., Murr, R.,
Schübeler, D., and Grosschedl, R. (2016).
\newblock Pioneering {Activity} of the {C}-{Terminal} {Domain} of {EBF}1
{Shapes} the {Chromatin} {Landscape} for {B} {Cell} {Programming}.
\newblock {\em Immunity}, 44(3):527--541.
\bibitem[Boyle et~al., 2008]{boyle_high-resolution_2008}
Boyle, A.~P., Davis, S., Shulha, H.~P., Meltzer, P., Margulies, E.~H., Weng,
Z., Furey, T.~S., and Crawford, G.~E. (2008).
\newblock High-{Resolution} {Mapping} and {Characterization} of {Open}
{Chromatin} across the {Genome}.
\newblock {\em Cell}, 132(2):311--322.
\bibitem[Bucher and Trifonov, 1986]{bucher_compilation_1986}
Bucher, P. and Trifonov, E.~N. (1986).
\newblock Compilation and analysis of eukaryotic {POL} {II} promoter sequences.
\newblock {\em Nucleic Acids Research}, 14(24):10009--10026.
\bibitem[Buenrostro et~al., 2013]{buenrostro_transposition_2013}
Buenrostro, J.~D., Giresi, P.~G., Zaba, L.~C., Chang, H.~Y., and Greenleaf,
W.~J. (2013).
\newblock Transposition of native chromatin for fast and sensitive epigenomic
profiling of open chromatin, {DNA}-binding proteins and nucleosome position.
\newblock {\em Nature Methods}, 10(12):1213--1218.
\bibitem[Castro-Mondragon et~al., 2017]{castro-mondragon_rsat_2017}
Castro-Mondragon, J.~A., Jaeger, S., Thieffry, D., Thomas-Chollier, M., and van
Helden, J. (2017).
\newblock {RSAT} matrix-clustering: dynamic exploration and redundancy
reduction of transcription factor binding motif collections.
\newblock {\em Nucleic Acids Research}, 45(13):e119--e119.
\bibitem[Chatr-aryamontri et~al., 2017]{chatr-aryamontri_biogrid_2017}
Chatr-aryamontri, A., Oughtred, R., Boucher, L., Rust, J., Chang, C., Kolas,
N.~K., O'Donnell, L., Oster, S., Theesfeld, C., Sellam, A., Stark, C.,
Breitkreutz, B.-J., Dolinski, K., and Tyers, M. (2017).
\newblock The {BioGRID} interaction database: 2017 update.
\newblock {\em Nucleic Acids Research}, 45(D1):D369--D379.
\bibitem[Cheng et~al., 2012]{cheng_understanding_2012}
Cheng, C., Alexander, R., Min, R., Leng, J., Yip, K.~Y., Rozowsky, J., Yan,
K.-K., Dong, X., Djebali, S., Ruan, Y., Davis, C.~A., Carninci, P., Lassman,
T., Gingeras, T.~R., Guigó, R., Birney, E., Weng, Z., Snyder, M., and
Gerstein, M. (2012).
\newblock Understanding transcriptional regulation by integrative analysis of
transcription factor binding data.
\newblock {\em Genome Research}, 22(9):1658--1667.
\bibitem[Cirillo et~al., 2002]{cirillo_opening_2002}
Cirillo, L.~A., Lin, F.~R., Cuesta, I., Friedman, D., Jarnik, M., and Zaret,
K.~S. (2002).
\newblock Opening of {Compacted} {Chromatin} by {Early} {Developmental}
{Transcription} {Factors} {HNF}3 ({FoxA}) and {GATA}-4.
\newblock {\em Molecular Cell}, 9(2):279--289.
\bibitem[Consortium, 2012]{consortium_integrated_2012}
Consortium, T. E.~P. (2012).
\newblock An integrated encyclopedia of {DNA} elements in the human genome.
\newblock {\em Nature}, 489(7414):57--74.
\bibitem[Dalton et~al., 2009]{dalton_clustering_2009}
Dalton, L., Ballarin, V., and Brun, M. (2009).
\newblock Clustering {Algorithms}: {On} {Learning}, {Validation},
{Performance}, and {Applications} to {Genomics}.
\newblock {\em Current Genomics}, 10(6):430--445.
\bibitem[Donohoe et~al., 2007]{donohoe_identification_2007}
Donohoe, M.~E., Zhang, L.-F., Xu, N., Shi, Y., and Lee, J.~T. (2007).
\newblock Identification of a {Ctcf} {Cofactor}, {Yy}1, for the {X}
{Chromosome} {Binary} {Switch}.
\newblock {\em Molecular Cell}, 25(1):43--56.
+\bibitem[Dreos et~al., 2016]{dreos_influence_2016}
+Dreos, R., Ambrosini, G., and Bucher, P. (2016).
+\newblock Influence of {Rotational} {Nucleosome} {Positioning} on
+ {Transcription} {Start} {Site} {Selection} in {Animal} {Promoters}.
+\newblock {\em PLOS Computational Biology}, 12(10):e1005144.
+
\bibitem[Dreos et~al., 2013]{dreos_epd_2013}
Dreos, R., Ambrosini, G., Cavin~Périer, R., and Bucher, P. (2013).
\newblock {EPD} and {EPDnew}, high-quality promoter resources in the
next-generation sequencing era.
\newblock {\em Nucleic Acids Research}, 41(D1):D157--D164.
\bibitem[Dreos et~al., 2017]{dreos_eukaryotic_2017}
Dreos, R., Ambrosini, G., Groux, R., Cavin Périer, R., and Bucher, P. (2017).
\newblock The eukaryotic promoter database in its 30th year: focus on
non-vertebrate organisms.
\newblock {\em Nucleic Acids Research}, 45(D1):D51--D55.
\bibitem[Dreos et~al., 2018]{dreos_mga_2018}
Dreos, R., Ambrosini, G., Groux, R., Périer, R.~C., and Bucher, P. (2018).
\newblock {MGA} repository: a curated data resource for {ChIP}-seq and other
genome annotated data.
\newblock {\em Nucleic Acids Research}, 46(D1):D175--D180.
\bibitem[Dreos et~al., 2015]{dreos_eukaryotic_2015}
Dreos, R., Ambrosini, G., Périer, R.~C., and Bucher, P. (2015).
\newblock The {Eukaryotic} {Promoter} {Database}: expansion of {EPDnew} and new
promoter analysis tools.
\newblock {\em Nucleic Acids Research}, 43(D1):D92--D96.
\bibitem[Fan et~al., 2016]{fan_characterizing_2016}
Fan, J., Salathia, N., Liu, R., Kaeser, G.~E., Yung, Y.~C., Herman, J.~L.,
Kaper, F., Fan, J.-B., Zhang, K., Chun, J., and Kharchenko, P.~V. (2016).
\newblock Characterizing transcriptional heterogeneity through pathway and gene
set overdispersion analysis.
\newblock {\em Nature Methods}, 13(3):241--244.
\bibitem[Fu et~al., 2004]{fu_motifviz:_2004}
Fu, Y., Frith, M.~C., Haverty, P.~M., and Weng, Z. (2004).
\newblock {MotifViz}: an analysis and visualization tool for motif discovery.
\newblock {\em Nucleic Acids Research}, 32(suppl\_2):W420--W423.
\bibitem[Fu et~al., 2008]{fu_insulator_2008}
Fu, Y., Sinha, M., Peterson, C.~L., and Weng, Z. (2008).
\newblock The {Insulator} {Binding} {Protein} {CTCF} {Positions} 20
{Nucleosomes} around {Its} {Binding} {Sites} across the {Human} {Genome}.
\newblock {\em PLOS Genetics}, 4(7):e1000138.
\bibitem[Gaffney et~al., 2012]{gaffney_controls_2012}
Gaffney, D.~J., McVicker, G., Pai, A.~A., Fondufe-Mittendorf, Y.~N., Lewellen,
N., Michelini, K., Widom, J., Gilad, Y., and Pritchard, J.~K. (2012).
\newblock Controls of {Nucleosome} {Positioning} in the {Human} {Genome}.
\newblock {\em PLoS Genet}, 8(11):e1003036.
\bibitem[Gerstein et~al., 2012]{gerstein_architecture_2012}
Gerstein, M.~B., Kundaje, A., Hariharan, M., Landt, S.~G., Yan, K.-K., Cheng,
C., Mu, X.~J., Khurana, E., Rozowsky, J., Alexander, R., Min, R., Alves, P.,
Abyzov, A., Addleman, N., Bhardwaj, N., Boyle, A.~P., Cayting, P., Charos,
A., Chen, D.~Z., Cheng, Y., Clarke, D., Eastman, C., Euskirchen, G., Frietze,
S., Fu, Y., Gertz, J., Grubert, F., Harmanci, A., Jain, P., Kasowski, M.,
Lacroute, P., Leng, J., Lian, J., Monahan, H., O’Geen, H., Ouyang, Z.,
Partridge, E.~C., Patacsil, D., Pauli, F., Raha, D., Ramirez, L., Reddy,
T.~E., Reed, B., Shi, M., Slifer, T., Wang, J., Wu, L., Yang, X., Yip, K.~Y.,
Zilberman-Schapira, G., Batzoglou, S., Sidow, A., Farnham, P.~J., Myers,
R.~M., Weissman, S.~M., and Snyder, M. (2012).
\newblock Architecture of the human regulatory network derived from {ENCODE}
data.
\newblock {\em Nature}, 489(7414):91--100.
\bibitem[Ghirlando and Felsenfeld, 2016]{ghirlando_ctcf:_2016}
Ghirlando, R. and Felsenfeld, G. (2016).
\newblock {CTCF}: making the right connections.
\newblock {\em Genes \& Development}, 30(8):881--891.
\bibitem[González-Blas et~al., 2019]{gonzalez-blas_cistopic:_2019}
González-Blas, C.~B., Minnoye, L., Papasokrati, D., Aibar, S., Hulselmans, G.,
Christiaens, V., Davie, K., Wouters, J., and Aerts, S. (2019).
\newblock {cisTopic}: cis-regulatory topic modeling on single-cell {ATAC}-seq
data.
\newblock {\em Nature Methods}, 16(5):397.
\bibitem[Grant et~al., 2011]{grant_fimo:_2011}
Grant, C.~E., Bailey, T.~L., and Noble, W.~S. (2011).
\newblock {FIMO}: scanning for occurrences of a given motif.
\newblock {\em Bioinformatics}, 27(7):1017--1018.
\bibitem[Grossman et~al., 2018]{grossman_positional_2018}
Grossman, S.~R., Engreitz, J., Ray, J.~P., Nguyen, T.~H., Hacohen, N., and
Lander, E.~S. (2018).
\newblock Positional specificity of different transcription factor classes
within enhancers.
\newblock {\em Proceedings of the National Academy of Sciences},
115(30):E7222--E7230.
\bibitem[Groux and Bucher, 2019]{groux_spar-k:_2019}
Groux, R. and Bucher, P. (2019).
\newblock {SPar}-{K}: a method to partition {NGS} signal data.
\newblock {\em Bioinformatics}.
\bibitem[Guo et~al., 2012]{guo_high_2012}
Guo, Y., Mahony, S., and Gifford, D.~K. (2012).
\newblock High {Resolution} {Genome} {Wide} {Binding} {Event} {Finding} and
{Motif} {Discovery} {Reveals} {Transcription} {Factor} {Spatial} {Binding}
{Constraints}.
\newblock {\em PLOS Computational Biology}, 8(8):e1002638.
\bibitem[Hagman and Lukin, 2005]{hagman_early_2005}
Hagman, J. and Lukin, K. (2005).
\newblock Early {B}-cell factor ‘pioneers’ the way for {B}-cell
development.
\newblock {\em Trends in Immunology}, 26(9):455--461.
\bibitem[Heinz et~al., 2010]{heinz_simple_2010}
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y.~C., Laslo, P., Cheng,
J.~X., Murre, C., Singh, H., and Glass, C.~K. (2010).
\newblock Simple {Combinations} of {Lineage}-{Determining} {Transcription}
{Factors} {Prime} cis-{Regulatory} {Elements} {Required} for {Macrophage} and
{B} {Cell} {Identities}.
\newblock {\em Molecular Cell}, 38(4):576--589.
\bibitem[Henikoff and Smith, 2015]{henikoff_histone_2015}
Henikoff, S. and Smith, M.~M. (2015).
\newblock Histone {Variants} and {Epigenetics}.
\newblock {\em Cold Spring Harbor Perspectives in Biology}, 7(1):a019364.
\bibitem[Hertz et~al., 1990]{hertz_identification_1990}
Hertz, G.~Z., Hartzell, G.~W., and Stormo, G.~D. (1990).
\newblock Identification of consensus patterns in unaligned {DNA} sequences
known to be functionally related.
\newblock {\em Computer applications in the biosciences: CABIOS}, 6(2):81--92.
\bibitem[Hon et~al., 2008]{hon_chromasig:_2008}
Hon, G., Ren, B., and Wang, W. (2008).
\newblock {ChromaSig}: {A} {Probabilistic} {Approach} to {Finding} {Common}
{Chromatin} {Signatures} in the {Human} {Genome}.
\newblock {\em PLOS Computational Biology}, 4(10):e1000201.
\bibitem[Hyun et~al., 2017]{hyun_writing_2017}
Hyun, K., Jeon, J., Park, K., and Kim, J. (2017).
\newblock Writing, erasing and reading histone lysine methylations.
\newblock {\em Experimental \& Molecular Medicine}, 49(4):e324--e324.
\bibitem[Ioshikhes et~al., 2011]{ioshikhes_variety_2011}
Ioshikhes, I., Hosid, S., and Pugh, B.~F. (2011).
\newblock Variety of genomic {DNA} patterns for nucleosome positioning.
\newblock {\em Genome Research}, 21(11):1863--1871.
\bibitem[Isakova et~al., 2017]{isakova_smile-seq_2017}
Isakova, A., Groux, R., Imbeault, M., Rainer, P., Alpern, D., Dainese, R.,
Ambrosini, G., Trono, D., Bucher, P., and Deplancke, B. (2017).
\newblock {SMiLE}-seq identifies binding motifs of single and dimeric
transcription factors.
\newblock {\em Nature Methods}, advance online publication.
+\bibitem[Jiang and Pugh, 2009]{jiang_nucleosome_2009}
+Jiang, C. and Pugh, B.~F. (2009).
+\newblock Nucleosome positioning and gene regulation: advances through
+ genomics.
+\newblock {\em Nature Reviews Genetics}, 10(3):161--172.
+
\bibitem[Jolma et~al., 2010]{jolma_multiplexed_2010}
Jolma, A., Kivioja, T., Toivonen, J., Cheng, L., Wei, G., Enge, M., Taipale,
M., Vaquerizas, J.~M., Yan, J., Sillanpää, M.~J., Bonke, M., Palin, K.,
Talukder, S., Hughes, T.~R., Luscombe, N.~M., Ukkonen, E., and Taipale, J.
(2010).
\newblock Multiplexed massively parallel {SELEX} for characterization of human
transcription factor binding specificities.
\newblock {\em Genome Research}, 20(6):861--873.
\bibitem[Jolma and Taipale, 2011]{jolma_methods_2011-2}
Jolma, A. and Taipale, J. (2011).
\newblock Methods for {Analysis} of {Transcription} {Factor} {DNA}-{Binding}
{Specificity} {In} {Vitro}, {Chapter} 9, {How} {Transcription} {Factors}
{Identify} {Regulatory} {Sites} in {Genomic} {Sequence}.
\newblock In Hughes, T.~R., editor, {\em A {Handbook} of {Transcription}
{Factors}}, number~52 in Subcellular {Biochemistry}, pages 193--204. Springer
Netherlands.
\bibitem[Jolma et~al., 2013]{jolma_dna-binding_2013}
Jolma, A., Yan, J., Whitington, T., Toivonen, J., Nitta, K., Rastas, P.,
Morgunova, E., Enge, M., Taipale, M., Wei, G., Palin, K., Vaquerizas, J.,
Vincentelli, R., Luscombe, N., Hughes, T., Lemaire, P., Ukkonen, E., Kivioja,
T., and Taipale, J. (2013).
\newblock {DNA}-{Binding} {Specificities} of {Human} {Transcription} {Factors}.
\newblock {\em Cell}, 152(1–2):327--339.
\bibitem[Kent, 2002]{kent_blatblast-like_2002}
Kent, W.~J. (2002).
\newblock {BLAT}—{The} {BLAST}-{Like} {Alignment} {Tool}.
\newblock {\em Genome Research}, 12(4):656--664.
\bibitem[Khan et~al., 2018]{khan_jaspar_2018}
Khan, A., Fornes, O., Stigliani, A., Gheorghe, M., Castro-Mondragon, J.~A., van
der Lee, R., Bessy, A., Chèneby, J., Kulkarni, S.~R., Tan, G., Baranasic,
D., Arenillas, D.~J., Sandelin, A., Vandepoele, K., Lenhard, B., Ballester,
B., Wasserman, W.~W., Parcy, F., and Mathelier, A. (2018).
\newblock {JASPAR} 2018: update of the open-access database of transcription
factor binding profiles and its web framework.
\newblock {\em Nucleic Acids Research}, 46(D1):D260--D266.
\bibitem[Kiselev et~al., 2017]{kiselev_sc3:_2017}
Kiselev, V.~Y., Kirschner, K., Schaub, M.~T., Andrews, T., Yiu, A., Chandra,
T., Natarajan, K.~N., Reik, W., Barahona, M., Green, A.~R., and Hemberg, M.
(2017).
\newblock {SC}3: consensus clustering of single-cell {RNA}-seq data.
\newblock {\em Nature Methods}, 14(5):483--486.
\bibitem[Kouzarides, 2007]{kouzarides_chromatin_2007}
Kouzarides, T. (2007).
\newblock Chromatin {Modifications} and {Their} {Function}.
\newblock {\em Cell}, 128(4):693--705.
+\bibitem[Kubik et~al., 2015]{kubik_nucleosome_2015}
+Kubik, S., Bruzzone, M., Jacquet, P., Falcone, J.-L., Rougemont, J., and Shore,
+ D. (2015).
+\newblock Nucleosome {Stability} {Distinguishes} {Two} {Different} {Promoter}
+ {Types} at {All} {Protein}-{Coding} {Genes} in {Yeast}.
+\newblock {\em Molecular Cell}, 60(3):422--434.
+
\bibitem[Kulakovskiy et~al., 2018]{kulakovskiy_hocomoco:_2018}
Kulakovskiy, I.~V., Vorontsov, I.~E., Yevshin, I.~S., Sharipov, R.~N.,
Fedorova, A.~D., Rumynskiy, E.~I., Medvedeva, Y.~A., Magana-Mora, A., Bajic,
V.~B., Papatsenko, D.~A., Kolpakov, F.~A., and Makeev, V.~J. (2018).
\newblock {HOCOMOCO}: towards a complete collection of transcription factor
binding models for human and mouse via large-scale {ChIP}-{Seq} analysis.
\newblock {\em Nucleic Acids Research}, 46(D1):D252--D259.
\bibitem[Kulakovskiy et~al., 2016]{kulakovskiy_hocomoco:_2016}
Kulakovskiy, I.~V., Vorontsov, I.~E., Yevshin, I.~S., Soboleva, A.~V.,
Kasianov, A.~S., Ashoor, H., Ba-alawi, W., Bajic, V.~B., Medvedeva, Y.~A.,
Kolpakov, F.~A., and Makeev, V.~J. (2016).
\newblock {HOCOMOCO}: expansion and enhancement of the collection of
transcription factor binding sites models.
\newblock {\em Nucleic Acids Research}, 44(D1):D116--D125.
\bibitem[Kundaje et~al., 2012]{kundaje_ubiquitous_2012}
Kundaje, A., Kyriazopoulou-Panagiotopoulou, S., Libbrecht, M., Smith, C.~L.,
Raha, D., Winters, E.~E., Johnson, S.~M., Snyder, M., Batzoglou, S., and
Sidow, A. (2012).
\newblock Ubiquitous heterogeneity and asymmetry of the chromatin environment
at regulatory elements.
\newblock {\em Genome Research}, 22(9):1735--1747.
\bibitem[Kurotaki et~al., 2017]{kurotaki_transcriptional_2017}
Kurotaki, D., Sasaki, H., and Tamura, T. (2017).
\newblock Transcriptional control of monocyte and macrophage development.
\newblock {\em International Immunology}, 29(3):97--107.
\bibitem[Langmead and Salzberg, 2012]{langmead_fast_2012}
Langmead, B. and Salzberg, S.~L. (2012).
\newblock Fast gapped-read alignment with {Bowtie} 2.
\newblock {\em Nature Methods}, 9(4):357--359.
\bibitem[Langmead et~al., 2009]{langmead_ultrafast_2009}
Langmead, B., Trapnell, C., Pop, M., and Salzberg, S.~L. (2009).
\newblock Ultrafast and memory-efficient alignment of short {DNA} sequences to
the human genome.
\newblock {\em Genome Biology}, 10(3):R25.
\bibitem[Li et~al., 2009]{li_sequence_2009}
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth,
G., Abecasis, G., and Durbin, R. (2009).
\newblock The {Sequence} {Alignment}/{Map} format and {SAMtools}.
\newblock {\em Bioinformatics}, 25(16):2078--2079.
\bibitem[Li et~al., 2019]{li_identification_2019}
Li, Z., Schulz, M.~H., Look, T., Begemann, M., Zenke, M., and Costa, I.~G.
(2019).
\newblock Identification of transcription factor binding sites using
{ATAC}-seq.
\newblock {\em Genome Biology}, 20(1):45.
\bibitem[Lizio et~al., 2015]{lizio_gateways_2015}
Lizio, M., Harshbarger, J., Shimoji, H., Severin, J., Kasukawa, T., Sahin, S.,
Abugessaisa, I., Fukuda, S., Hori, F., Ishikawa-Kato, S., Mungall, C.~J.,
Arner, E., Baillie, J.~K., Bertin, N., Bono, H., de~Hoon, M., Diehl, A.~D.,
Dimont, E., Freeman, T.~C., Fujieda, K., Hide, W., Kaliyaperumal, R.,
Katayama, T., Lassmann, T., Meehan, T.~F., Nishikata, K., Ono, H., Rehli, M.,
Sandelin, A., Schultes, E.~A., ‘t Hoen, P.~A., Tatum, Z., Thompson, M.,
Toyoda, T., Wright, D.~W., Daub, C.~O., Itoh, M., Carninci, P., Hayashizaki,
Y., Forrest, A.~R., Kawaji, H., and {the FANTOM consortium} (2015).
\newblock Gateways to the {FANTOM}5 promoter level mammalian expression atlas.
\newblock {\em Genome Biology}, 16(1):22.
\bibitem[Losada, 2014]{losada_cohesin_2014}
Losada, A. (2014).
\newblock Cohesin in cancer: chromosome segregation and beyond.
\newblock {\em Nature Reviews Cancer}, 14(6):389--393.
\bibitem[Längst and Manelyte, 2015]{langst_chromatin_2015}
Längst, G. and Manelyte, L. (2015).
\newblock Chromatin {Remodelers}: {From} {Function} to {Dysfunction}.
\newblock {\em Genes}, 6(2):299--324.
\bibitem[Maerkl and Quake, 2007]{maerkl_systems_2007}
Maerkl, S.~J. and Quake, S.~R. (2007).
\newblock A {Systems} {Approach} to {Measuring} the {Binding} {Energy}
{Landscapes} of {Transcription} {Factors}.
\newblock {\em Science}, 315(5809):233--237.
\bibitem[Maier et~al., 2004]{maier_early_2004}
Maier, H., Ostraat, R., Gao, H., Fields, S., Shinton, S.~A., Medina, K.~L.,
Ikawa, T., Murre, C., Singh, H., Hardy, R.~R., and Hagman, J. (2004).
\newblock Early {B} cell factor cooperates with {Runx}1 and mediates epigenetic
changes associated with mb-1 transcription.
\newblock {\em Nature Immunology}, 5(10):1069--1077.
\bibitem[Marsland, 2015]{marsland_machine_2015-1}
Marsland, S. (2015).
\newblock {\em Machine {Learning}, {An} algorithmic {Perspective}, {Chapter} 7
{Probabilistic} {Learning}}.
\newblock CRC Press, Boca Raton, second edition edition.
\bibitem[Mathelier et~al., 2014]{mathelier_jaspar_2014}
Mathelier, A., Zhao, X., Zhang, A.~W., Parcy, F., Worsley-Hunt, R., Arenillas,
D.~J., Buchman, S., Chen, C.-y., Chou, A., Ienasescu, H., Lim, J., Shyr, C.,
Tan, G., Zhou, M., Lenhard, B., Sandelin, A., and Wasserman, W.~W. (2014).
\newblock {JASPAR} 2014: an extensively expanded and updated open-access
database of transcription factor binding profiles.
\newblock {\em Nucleic Acids Research}, 42(D1):D142--D147.
\bibitem[{McGinty Robert K. and Tan Song},
2014]{mcginty_robert_k._and_tan_song_fundamentals_2014}
{McGinty Robert K. and Tan Song} (2014).
\newblock {\em Fundamentals of {Chromatin}, chapter 1 {Histone}, {Nucleosomes}
and {Chromatin} {Structure}}.
\newblock Jerry L. Workman and Susan M. Abmayr, New York, 2014 edition.
\bibitem[Nair et~al., 2014]{nair_probabilistic_2014}
Nair, N.~U., Kumar, S., Moret, B. M.~E., and Bucher, P. (2014).
\newblock Probabilistic partitioning methods to find significant patterns in
{ChIP}-{Seq} data.
\newblock {\em Bioinformatics}, 30(17):2406--2413.
\bibitem[Neph et~al., 2012]{neph_expansive_2012}
Neph, S., Vierstra, J., Stergachis, A.~B., Reynolds, A.~P., Haugen, E., Vernot,
B., Thurman, R.~E., John, S., Sandstrom, R., Johnson, A.~K., Maurano, M.~T.,
Humbert, R., Rynes, E., Wang, H., Vong, S., Lee, K., Bates, D., Diegel, M.,
Roach, V., Dunn, D., Neri, J., Schafer, A., Hansen, R.~S., Kutyavin, T.,
Giste, E., Weaver, M., Canfield, T., Sabo, P., Zhang, M., Balasundaram, G.,
Byron, R., MacCoss, M.~J., Akey, J.~M., Bender, M.~A., Groudine, M., Kaul,
R., and Stamatoyannopoulos, J.~A. (2012).
\newblock An expansive human regulatory lexicon encoded in transcription factor
footprints.
\newblock {\em Nature}, 489(7414):83--90.
\bibitem[Nielsen et~al., 2012]{nielsen_catchprofiles:_2012}
Nielsen, F. G.~G., Markus, K.~G., Friborg, R.~M., Favrholdt, L.~M.,
Stunnenberg, H.~G., and Huynen, M. (2012).
\newblock {CATCHprofiles}: {Clustering} and {Alignment} {Tool} for {ChIP}
{Profiles}.
\newblock {\em PLOS ONE}, 7(1):e28272.
\bibitem[Ong and Corces, 2014]{ong_ctcf:_2014}
Ong, C.-T. and Corces, V.~G. (2014).
\newblock {CTCF}: an architectural protein bridging genome topology and
function.
\newblock {\em Nature Reviews Genetics}, 15(4):234--246.
\bibitem[Orenstein and Shamir, 2014]{orenstein_comparative_2014}
Orenstein, Y. and Shamir, R. (2014).
\newblock A comparative analysis of transcription factor binding models learned
from {PBM}, {HT}-{SELEX} and {ChIP} data.
\newblock {\em Nucleic Acids Research}, 42(8):e63--e63.
\bibitem[Ou et~al., 2018]{ou_motifstack_2018}
Ou, J., Wolfe, S.~A., Brodsky, M.~H., and Zhu, L.~J. (2018).
\newblock {motifStack} for the analysis of transcription factor binding site
evolution.
\newblock {\em Nature Methods}, 15(1):8--9.
\bibitem[Pizzi and Ukkonen, 2008]{pizzi_fast_2008}
Pizzi, C. and Ukkonen, E. (2008).
\newblock Fast profile matching algorithms — {A} survey.
\newblock {\em Theoretical Computer Science}, 395(2):137--157.
\bibitem[Pollard et~al., 2010]{pollard_detection_2010}
Pollard, K.~S., Hubisz, M.~J., Rosenbloom, K.~R., and Siepel, A. (2010).
\newblock Detection of nonneutral substitution rates on mammalian phylogenies.
\newblock {\em Genome Research}, 20(1):110--121.
\bibitem[Quinlan and Hall, 2010]{quinlan_bedtools:_2010}
Quinlan, A.~R. and Hall, I.~M. (2010).
\newblock {BEDTools}: a flexible suite of utilities for comparing genomic
features.
\newblock {\em Bioinformatics}, 26(6):841--842.
\bibitem[Raney et~al., 2014]{raney_track_2014}
Raney, B.~J., Dreszer, T.~R., Barber, G.~P., Clawson, H., Fujita, P.~A., Wang,
T., Nguyen, N., Paten, B., Zweig, A.~S., Karolchik, D., and Kent, W.~J.
(2014).
\newblock Track data hubs enable visualization of user-defined genome-wide
annotations on the {UCSC} {Genome} {Browser}.
\newblock {\em Bioinformatics}, 30(7):1003--1005.
\bibitem[Rico et~al., 2017]{rico_comparative_2017}
Rico, D., Martens, J.~H., Downes, K., Carrillo-de Santa-Pau, E., Pancaldi, V.,
Breschi, A., Richardson, D., Heath, S., Saeed, S., Frontini, M., Chen, L.,
Watt, S., Müller, F., Clarke, L., Kerstens, H.~H., Wilder, S.~P., Palumbo,
E., Djebali, S., Raineri, E., Merkel, A., Esteve-Codina, A., Sultan, M.,
Bommel, A.~v., Gut, M., Yaspo, M.-L., Rubio, M., Fernandez, J.~M., Attwood,
A., Torre, V. d.~l., Royo, R., Fragkogianni, S., Gelpí, J.~L., Torrents, D.,
Iotchkova, V., Logie, C., Aghajanirefah, A., Singh, A.~A., Janssen-Megens,
E.~M., Berentsen, K., Erber, W., Rendon, A., Kostadima, M., Loos, R., Ent, M.
A. v.~d., Kaan, A., Sharifi, N., Paul, D.~S., Ifrim, D.~C., Quintin, J.,
Love, M.~I., Pisano, D.~G., Burden, F., Foad, N., Farrow, S., Zerbino, D.~R.,
Dunham, I., Kuijpers, T., Lehrach, H., Lengauer, T., Bertone, P., Netea,
M.~G., Vingron, M., Beck, S., Flicek, P., Gut, I., Ouwehand, W.~H., Bock, C.,
Soranzo, N., Guigo, R., Valencia, A., and Stunnenberg, H.~G. (2017).
\newblock Comparative analysis of neutrophil and monocyte epigenomes.
\newblock {\em bioRxiv}, page 237784.
\bibitem[{Roadmap Epigenomics Consortium} et~al.,
2015]{roadmap_epigenomics_consortium_integrative_2015}
{Roadmap Epigenomics Consortium}, Kundaje, A., Meuleman, W., Ernst, J.,
Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang,
J., Ziller, M.~J., Amin, V., Whitaker, J.~W., Schultz, M.~D., Ward, L.~D.,
Sarkar, A., Quon, G., Sandstrom, R.~S., Eaton, M.~L., Wu, Y.-C., Pfenning,
A.~R., Wang, X., Claussnitzer, M., {Yaping Liu}, Coarfa, C., Alan~Harris, R.,
Shoresh, N., Epstein, C.~B., Gjoneska, E., Leung, D., Xie, W., David~Hawkins,
R., Lister, R., Hong, C., Gascard, P., Mungall, A.~J., Moore, R., Chuah, E.,
Tam, A., Canfield, T.~K., Scott~Hansen, R., Kaul, R., Sabo, P.~J., Bansal,
M.~S., Carles, A., Dixon, J.~R., Farh, K.-H., Feizi, S., Karlic, R., Kim,
A.-R., Kulkarni, A., Li, D., Lowdon, R., Elliott, G., Mercer, T.~R., Neph,
S.~J., Onuchic, V., Polak, P., Rajagopal, N., Ray, P., Sallari, R.~C.,
Siebenthall, K.~T., Sinnott-Armstrong, N.~A., Stevens, M., Thurman, R.~E.,
Wu, J., Zhang, B., Zhou, X., Beaudet, A.~E., Boyer, L.~A., Jager, P. L.~D.,
Farnham, P.~J., Fisher, S.~J., Haussler, D., Jones, S. J.~M., Li, W., Marra,
M.~A., McManus, M.~T., Sunyaev, S., Thomson, J.~A., Tlsty, T.~D., Tsai,
L.-H., Wang, W., Waterland, R.~A., Zhang, M.~Q., Chadwick, L.~H., Bernstein,
B.~E., Costello, J.~F., Ecker, J.~R., Hirst, M., Meissner, A., Milosavljevic,
A., Ren, B., Stamatoyannopoulos, J.~A., Wang, T., and Kellis, M. (2015).
\newblock Integrative analysis of 111 reference human epigenomes.
\newblock {\em Nature}, 518(7539):317--330.
\bibitem[Rustici et~al., 2013]{rustici_arrayexpress_2013}
Rustici, G., Kolesnikov, N., Brandizi, M., Burdett, T., Dylag, M., Emam, I.,
Farne, A., Hastings, E., Ison, J., Keays, M., Kurbatova, N., Malone, J.,
Mani, R., Mupo, A., Pedro~Pereira, R., Pilicheva, E., Rung, J., Sharma, A.,
Tang, Y.~A., Ternent, T., Tikhonov, A., Welter, D., Williams, E., Brazma, A.,
Parkinson, H., and Sarkans, U. (2013).
\newblock {ArrayExpress} update—trends in database growth and links to data
analysis tools.
\newblock {\em Nucleic Acids Research}, 41(D1):D987--D990.
+\bibitem[Schones et~al., 2008]{schones_dynamic_2008}
+Schones, D.~E., Cui, K., Cuddapah, S., Roh, T.-Y., Barski, A., Wang, Z., Wei,
+ G., and Zhao, K. (2008).
+\newblock Dynamic {Regulation} of {Nucleosome} {Positioning} in the {Human}
+ {Genome}.
+\newblock {\em Cell}, 132(5):887--898.
+
\bibitem[Schones et~al., 2007]{schones_statistical_2007}
Schones, D.~E., Smith, A.~D., and Zhang, M.~Q. (2007).
\newblock Statistical significance of cis-regulatory modules.
\newblock {\em BMC Bioinformatics}, 8(1):19.
\bibitem[Schütz and Delorenzi, 2008]{schutz_mamot:_2008}
Schütz, F. and Delorenzi, M. (2008).
\newblock {MAMOT}: hidden {Markov} modeling tool.
\newblock {\em Bioinformatics}, 24(11):1399--1400.
\bibitem[Siepel et~al., 2005]{siepel_evolutionarily_2005}
Siepel, A., Bejerano, G., Pedersen, J.~S., Hinrichs, A.~S., Hou, M.,
Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L.~W., Richards, S.,
Weinstock, G.~M., Wilson, R.~K., Gibbs, R.~A., Kent, W.~J., Miller, W., and
Haussler, D. (2005).
\newblock Evolutionarily conserved elements in vertebrate, insect, worm, and
yeast genomes.
\newblock {\em Genome Research}, 15(8):1034--1050.
\bibitem[Soufi et~al., 2015]{soufi_pioneer_2015}
Soufi, A., Garcia, M.~F., Jaroszewicz, A., Osman, N., Pellegrini, M., and
Zaret, K.~S. (2015).
\newblock Pioneer {Transcription} {Factors} {Target} {Partial} {DNA} {Motifs}
on {Nucleosomes} to {Initiate} {Reprogramming}.
\newblock {\em Cell}, 161(3):555--568.
\bibitem[Stedman et~al., 2008]{stedman_cohesins_2008}
Stedman, W., Kang, H., Lin, S., Kissil, J.~L., Bartolomei, M.~S., and
Lieberman, P.~M. (2008).
\newblock Cohesins localize with {CTCF} at the {KSHV} latency control region
and at cellular c-myc and {H}19 {Igf}2 insulators.
\newblock {\em The EMBO Journal}, 27(4):654--666.
\bibitem[Trifonov, 2011]{trifonov_cracking_2011}
Trifonov, E.~N. (2011).
\newblock Cracking the chromatin code: {Precise} rule of nucleosome
positioning.
\newblock {\em Physics of Life Reviews}, 8(1):39--50.
\bibitem[Turatsinze et~al., 2008]{turatsinze_using_2008}
Turatsinze, J.-V., Thomas-Chollier, M., Defrance, M., and Helden, J.~v. (2008).
\newblock Using {RSAT} to scan genome sequences for transcription factor
binding sites and cis -regulatory modules.
\newblock {\em Nature Protocols}, 3(10):1578--1588.
\bibitem[Vierstra and Stamatoyannopoulos, 2016]{vierstra_genomic_2016}
Vierstra, J. and Stamatoyannopoulos, J.~A. (2016).
\newblock Genomic footprinting.
\newblock {\em Nature Methods}, 13(3):213--221.
\bibitem[Voss and Hager, 2014]{voss_dynamic_2014}
Voss, T.~C. and Hager, G.~L. (2014).
\newblock Dynamic regulation of transcriptional states by chromatin and
transcription factors.
\newblock {\em Nature Reviews Genetics}, 15(2):69--81.
\bibitem[Wang et~al., 2012]{wang_sequence_2012}
Wang, J., Zhuang, J., Iyer, S., Lin, X., Whitfield, T.~W., Greven, M.~C.,
Pierce, B.~G., Dong, X., Kundaje, A., Cheng, Y., Rando, O.~J., Birney, E.,
Myers, R.~M., Noble, W.~S., Snyder, M., and Weng, Z. (2012).
\newblock Sequence features and chromatin structure around the genomic regions
bound by 119 human transcription factors.
\newblock {\em Genome Research}, 22(9):1798--1812.
\bibitem[Weirauch et~al., 2013]{weirauch_evaluation_2013}
Weirauch, M.~T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T.~R.,
Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., Talukder, S., {Dream5
Consortium}, Bussemaker, H.~J., Morris, Q.~D., Bulyk, M.~L., Stolovitzky, G.,
and Hughes, T.~R. (2013).
\newblock Evaluation of methods for modeling transcription factor sequence
specificity.
\newblock {\em Nature Biotechnology}, 31(2):126--134.
+\bibitem[West et~al., 2014]{west_nucleosomal_2014}
+West, J.~A., Cook, A., Alver, B.~H., Stadtfeld, M., Deaton, A.~M.,
+ Hochedlinger, K., Park, P.~J., Tolstorukov, M.~Y., and Kingston, R.~E.
+ (2014).
+\newblock Nucleosomal occupancy changes locally over key regulatory regions
+ during cell differentiation and reprogramming.
+\newblock {\em Nature Communications}, 5(1):1--12.
+
\bibitem[Wu et~al., 2016]{wu_biogps:_2016}
Wu, C., Jin, X., Tsueng, G., Afrasiabi, C., and Su, A.~I. (2016).
\newblock {BioGPS}: building your own mash-up of gene annotations and
expression profiles.
\newblock {\em Nucleic Acids Research}, 44(D1):D313--D316.
\bibitem[Zaret and Carroll, 2011]{zaret_pioneer_2011}
Zaret, K.~S. and Carroll, J.~S. (2011).
\newblock Pioneer transcription factors: establishing competence for gene
expression.
\newblock {\em Genes \& Development}, 25(21):2227--2241.
\bibitem[Zhang et~al., 2014]{zhang_canonical_2014}
Zhang, Y., Vastenhouw, N.~L., Feng, J., Fu, K., Wang, C., Ge, Y., Pauli, A.,
Hummelen, P.~v., Schier, A.~F., and Liu, X.~S. (2014).
\newblock Canonical nucleosome organization at promoters forms during genome
activation.
\newblock {\em Genome Research}, 24(2):260--266.
\bibitem[Zhao et~al., 2005]{zhao_tred:_2005}
Zhao, F., Xuan, Z., Liu, L., and Zhang, M.~Q. (2005).
\newblock {TRED}: a {Transcriptional} {Regulatory} {Element} {Database} and a
platform for in silico gene regulation studies.
\newblock {\em Nucleic Acids Research}, 33(suppl\_1):D103--D107.
\bibitem[Zhao et~al., 2009]{zhao_inferring_2009}
Zhao, Y., Granas, D., and Stormo, G.~D. (2009).
\newblock Inferring {Binding} {Energies} from {Selected} {Binding} {Sites}.
\newblock {\em PLOS Comput Biol}, 5(12):e1000590.
\bibitem[Zhou et~al., 2011]{zhou_charting_2011}
Zhou, V.~W., Goren, A., and Bernstein, B.~E. (2011).
\newblock Charting histone modifications and the functional organization of
mammalian genomes.
\newblock {\em Nature Reviews Genetics}, 12(1):7--18.
\end{thebibliography}
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