diff --git a/head/abstracts.tex b/head/abstracts.tex index 50563b0..2114cd4 100644 --- a/head/abstracts.tex +++ b/head/abstracts.tex @@ -1,72 +1,72 @@ %\begingroup %\let\cleardoublepage\clearpage % English abstract \cleardoublepage \chapter*{Abstract} \markboth{Abstract}{Abstract} \addcontentsline{toc}{chapter}{Abstract (English/Français/Deutsch)} % adds an entry to the table of contents % put your text here Any living organism contains a whole set of instructions encoded as genes on the DNA. This set of instructions contains all the necessary information that the organism will ever need, from its development to a mature individual to environment specific responses. Since all these instructions are not needed at the same time, the gene expression needs to be regulated. Eukaryotic genomes are stored inside nuclei as chromatin. The chromatin is the association of DNA with dedicated storage proteins - the histones - and the necessary machinery to regulate and express genes (RNA polymerases or RNAPs). In the nuclei, histones are assembled into octamers around which are wrapped ~148bp of DNA. This structure is known as the nucleosome. The repetition of nucleosomes along the genome allows to drastically compact the genome, eventually allowing to fit it inside the nucleus. However, this comes at the cost of rendering the DNA sequence inaccessible to DNA readers, such as the RNAPs and transcription factors (TFs). TFs are a class of proteins that have the remarkable property of recognizing and binding specific DNA sequences. More striking, each TF can recognize a multitude of different - but similar - DNA sequences providing TFs with a wide sequence specificity range. Eventually, this allows the cell to recruit TFs at dedicated locations in the genome called regulatory elements (REs). The action of TFs at REs is crucial to gene expression. Indeed, TFs are involved in many processes such as the opening of the chromatin structure or the recruitment of RNAPs. However if TFs can influence the chromatin structure, the opposite is also true as histones impede TF binding on DNA. Thus the regulation of genes relies on a subtle and complex crosstalk between the chromatin and TFs. To better understand how TFs and chromatin interact together to regulate gene expression, I lead several projects prospecting TF binding specificity and the chromatin structure at REs in human. -First, I used ENCODE next generation sequencing (NGS) data to explore how TF binding influences the nearby nucleosome organization and the propensity of some TFs to bind together. The results suggest that regular nucleosome arrays are found near all TFs. They also point out two special cases. When CTCF binds with the cohesin complex, it seems to drive the nucleosome organization, which is a unique feature among all TFs investigates. Additionally I present evidence supporting that EBF1 is a pioneer factor - a special class of TFs able to bind nucleosome. +First, I used ENCODE next generation sequencing (NGS) data to explore how TF binding influences the nearby nucleosome organization and the propensity of some TFs to bind together. The results suggest that regular nucleosome arrays are found near all TFs. They also point out two special cases. When CTCF binds with the cohesin complex, it seems to drive the nucleosome organization, which is a unique feature among all TFs investigated. Additionally I present evidence supporting that EBF1 is a pioneer factor - a special class of TFs able to bind nucleosome. Secondly, I developed several clustering algorithms and software to partition genomic regions according to NGS data and/or on their DNA sequences. These methods allow to discover important trends, for instance different nucleosome architectures . I illustrated the usefulness of these methods for the study of chromatin accessibility data and the identification of REs. -Thirdly, I participated to the assessment SMiLE-seq, a new microfluidic device that generates TF specificity data. The creation of TF specificity models and their comparison with other publicly available models demonstrated the value of SMiLE-seq to study TF specificity. +Thirdly, I participated to the assessment of SMiLE-seq, a new microfluidic device that generates TF specificity data. The creation of TF specificity models and their comparison with other publicly available models demonstrated the value of SMiLE-seq to study TF specificity. Finally, I participated in the development of a software that predicts TF binding sites. A careful benchmarking suggested that this software is - at the time of writing - the best available software in terms of speed while showing other performances similar to its competitors. % German abstract % \begin{otherlanguage}{german} % \cleardoublepage % \chapter*{Zusammenfassung} % \markboth{Zusammenfassung}{Zusammenfassung} % % put your text here % \lipsum[1-2] % \end{otherlanguage} % French abstract \begin{otherlanguage}{french} \cleardoublepage \chapter*{Résumé} \markboth{Résumé}{Résumé} Tout organisme vivant contient un jeu d'instructions encodé sous la forme de gènes dans son ADN. Ces instructions contiennent toutes les informations nécessaires à la vie de l'organisme en question, de son développement à l'adaptation à des conditions environnementales spécifiques. Étant donné que ces instructions ne sont pas toutes nécessaires en même temps, l'expression des gènes doit être régulée. Chez les eucaryotes, le génome est stocké dans dans le noyau sous forme de chromatine. La chromatine est l'association de l'ADN, de protéines dédiées au stockage de celui-ci et de toute la machinerie nécessaire à la régulation et à l'expression des gènes. Dans le noyau, les histones sont assemblés en octamères autour desquels s'enroulent 148pb d'ADN et forment le nucleosome. La répétition de nucleosomes le long du génome permet de le compacter fortement et d’être contenu dans le noyau. Cependant, cela se fait au prix de rendre certaines séquences d’ADN inaccessibles aux facteurs le lisant tels que la machinerie de transcription (les ARNs polymerases ou ARNPs) ou les facteurs de transcription (FTs). Les FTs forment une classe de protéines qui possède la remarquable capacité de pouvoir reconnaître et lier spécifiquement certaines séquences d’ADN. Plus encore, chaque FT est capable de reconnaître une multitude de séquences différentes – mais similaires – étendant d’autant plus la spécificité de reconnaissance. Cela permet à la cellule de recruter certains FTs à des endroits précis du génome appelés éléments régulateurs (ERs). Le rôle des FTs au niveau des REs est crucial pour l’expression des gènes. En effet, les FTs sont nécessaires pour plusieurs processus tels que la décompaction locale de la chromatine ou le recrutement de la machinerie transcriptionnelle. Cependant, si les FTs sont capables d’influer sur la structure de la chromatine, l’inverse est aussi vrai. Les histones sont capables d’empêcher la liaison des FTs à l’ADN. La régulation de l’expression des gènes s’appuie donc sur un phénomène subtile et complexe d’interactions entre la chromatine et les FTs. Afin de mieux comprendre ces interactions et comment elles participent à la régulation de l’expression des gènes, j’ai conduits plusieurs projets ayant pour sujet la spécificité des FTs et la structure de la chromatine dans les ERs. Premièrement, j’ai utilisé les données de séquençage à haut débit (SAD) générées par ENCODE afin d’explorer comment la liaison des FTs à l’ADN influence l’organisation des nucleosomes proches ainsi que la tendance de certains FTs à s’associer. Les résultats suggèrent que des agencements de nucléosomes réguliers se trouvent autour des sites de liaison de tous les FTs. Cependant, seule l’association entre CTCF et la cohésine semble capable d’influencer cette organisation. De plus, d’autres observations suggèrent fortement que le FT EBF1 est un facteur pionnier – une classe de FTs spéciaux capables de lier les nucléosomes. Deuxièmement, j’ai développé plusieurs algorithmes et sofwares de clustering permettant de grouper les régions du génome en fonction de données SAD et/ou de leur séquence ADN. Ces méthodes permettent d’identifier des tendances, par exemple différentes organisation des nucléosomes. J’ai illustré l’utilité de ces méthodes au travers de l’étude de l’accessibilité de la chromatine et de l’identification d’ERs. Troisièmement, j’ai participé à l’évaluation du SMiLE-seq, une nouvelle plateforme microfluidique permettant de générer des données sur la spécificité des FTs. La mise au point de modèles de spécificité et leur comparaison avec d’autres modèles disponibles a permis de démontrer la valeur du SMiLE-seq pour les études de spécificité des FTs. Finalement, j’ai participé au développement et à l’évaluation d’un software prédisant les régions liées par un FT, le long d’un génome. Le processus d’évaluation suggère que ce software est actuellement – au moment de la rédaction – le meilleur en terme de rapidité tout en présentant d’autres performances similaires à ses compétiteurs. \end{otherlanguage} %\endgroup %\vfill diff --git a/main/ch_discussion.aux b/main/ch_discussion.aux index fb1ee64..f65766a 100644 --- a/main/ch_discussion.aux +++ b/main/ch_discussion.aux @@ -1,54 +1,54 @@ \relax \providecommand\hyper@newdestlabel[2]{} \@writefile{toc}{\contentsline {chapter}{\numberline {8}Discussion}{111}{chapter.8}} \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} \@writefile{loa}{\addvspace {10\p@ }} \newlabel{discussion}{{8}{111}{Discussion}{chapter.8}{}} -\@writefile{toc}{\contentsline {chapter}{Discussions}{111}{chapter.8}} +\@writefile{toc}{\contentsline {chapter}{Discussion}{111}{chapter.8}} \@setckpt{main/ch_discussion}{ \setcounter{page}{114} \setcounter{equation}{0} \setcounter{enumi}{8} \setcounter{enumii}{0} \setcounter{enumiii}{0} \setcounter{enumiv}{0} \setcounter{footnote}{0} \setcounter{mpfootnote}{0} \setcounter{part}{0} \setcounter{chapter}{8} \setcounter{section}{0} \setcounter{subsection}{0} \setcounter{subsubsection}{0} \setcounter{paragraph}{0} \setcounter{subparagraph}{0} \setcounter{figure}{0} \setcounter{table}{0} \setcounter{NAT@ctr}{0} \setcounter{FBcaption@count}{0} \setcounter{ContinuedFloat}{0} \setcounter{KVtest}{0} \setcounter{subfigure}{0} \setcounter{subfigure@save}{0} \setcounter{lofdepth}{1} \setcounter{subtable}{0} \setcounter{subtable@save}{0} \setcounter{lotdepth}{1} \setcounter{lips@count}{0} \setcounter{lstnumber}{1} \setcounter{Item}{8} \setcounter{Hfootnote}{0} \setcounter{bookmark@seq@number}{0} \setcounter{AM@survey}{0} \setcounter{ttlp@side}{0} \setcounter{myparts}{0} \setcounter{parentequation}{0} \setcounter{AlgoLine}{39} \setcounter{algocfline}{2} \setcounter{algocfproc}{2} \setcounter{algocf}{2} \setcounter{float@type}{8} \setcounter{nlinenum}{0} \setcounter{lstlisting}{0} \setcounter{section@level}{0} } diff --git a/main/ch_discussion.tex b/main/ch_discussion.tex index e61a842..77b1dca 100644 --- a/main/ch_discussion.tex +++ b/main/ch_discussion.tex @@ -1,45 +1,45 @@ \cleardoublepage \chapter{Discussion} \label{discussion} \markboth{Discussions}{Discussion} -\addcontentsline{toc}{chapter}{Discussions} +\addcontentsline{toc}{chapter}{Discussion} This contribution of this work to genomic bioinformatics is dual. It has a resource development component and a research component, which importances can only be appreciated properly by considering them together. % ressource part The resource aspect of this work has been presented in Chapter \ref{lab_resources}. It concerned the maintenance of the MGA and EPD databases. Even though this is not purely research work, it is a necessary support to active research that allows many researchers in different fields - from wet lab to purely computational laboratories - to ask and, importantly, answer their questions. %EPD Currently, the EPD database contains the most precise genome annotation of TSSs which is a crucial information in many fields of life sciences. From the creation of efficient expression vectors in bio-engineering, to the reconstruction of gene interaction networks in computational biology. % MGA The benefice of the MGA database is less direct but not less important. The wealth of publicly available sequencing data is a treasure. As for each treasure, something is sitting atop, like a terrifying dragon or a tremendous overhead effort. Re-utilization of published data is an astonishingly difficult task that requires to search ill-annotated databases, to download the selected entries and map the data, hoping that the quality is acceptable. The sole task of mapping the data requires to have the proper software and genome locally stored. The MGA, as a majestic an well armored knight, allows to get ride of the overhead and to access data in utilizable format. Not only a vast amount of data from landmark studies is available on a central platform but they are efficiently searchable because of high quality, standardized and hand currated annotations. Such initiatives contribute to allow big-data like types of projects by making the burden of re-utilization acceptable. % research part The research part of my thesis was presented in chapters \ref{encode_peaks}, \ref{smile_seq}, and \ref{atac_seq}. It covered several topics related to the characterization of TF binding sites. % chromatin structure First, I explored the chromatin environment of TFs which binding has been assessed in GM17878 cells by the ENCODE Consortium. Using a specialized partitioning method, it was possible to detect well organized nucleosome arrays in the vicinity of all TFs. However, only CTCF has the ability to act as a barrier against which the nucleosome arrays are organized. Other TFs did not show this property, suggesting that other chromatin remodeling mechanisms are at play. Also, as expected, all TFs showed binding to a NDR with the exception of EBF1 which seemed to be binding on nucleosome arrays. % EBF1 Further analyses strongly supported that EBF1 binding sites tend to be located at the edges of nucleosomes. Nonetheless, these results do not allow to state whether EBF1 binds nucleosome edges or whether the consequence of its binding is a remodeling leading to a situation in which EFB1 directly flanks a nucleosome. In line with other reports suggesting a pioneer activity for EBF1, I propose a model in which EBF1 engages a nucleosome and promotes its displacement such that EBF1 is located at its entry. Because EBF1 binding motif shows properties of nucleosome positioning sequences, EBF1 could be involved in the stabilization of the nucleosome at this position. Like a stone under a wheel impedes its movement. Some parts of this model, like EBF1 ability to engage nucleosome arrays, could be tested in vitro. % interactions The analysis of ENCODE data also allowed to predict 35 interactions involving CTCF and junD. Moreover, this method allowed to segregate between four functionally different types of interactions. Out of the 35 interactions, 5 were new and could be tested in vitro as they are predicted to involve a direct physical interaction between the partners. % SMiLE-seq The characterization of TFs was also tackled in terms of their binding specificity. I participated in the SMiLE-seq project of the Deplancke laboratory of EPFL. The aim was to build a microfluidic based technology that would allow high throughput in vitro measurements of TF sequence specificity. I was in charge of modeling each TF specificity and to assess the suitability of SMiLE-seq for this problem. Interestingly, the SMiLE-seq technology turned out to be a really competitive method for this problem, paving the path to bigger scale studies. For instance, the specificity of zinc finger TFs and TF-dimers remain a largely unsolved problem. This work also tackled computational challenges linked with partitioning and classification problems in chapters \ref{spark}, \ref{pwmscan} and \ref{atac_seq}. % SPar-K The partitioning of genomic regions based on their sequencing profiles is not a trivial task. Among all the algorithms that have been developed to solve this problem, ChIPPartitioning - that has been developed by our laboratory - was the most efficient in term of partitioning accuracy. However, because it involves heavy probability related computations and because of implementation issues, it turned out to be really slow. To remedy this, I developed SPar-K which is a modified version of K-means. SPar-K achieved competitive partitioning accuracy while being clearly faster. % PWMScan I also contributed to the development of PWMScan that is a software that predicts TF binding sites along a genome based on a binding specificity model. Currently, this software is the fastest existing for this type of problem but is also as accurate as all other existing competitor programs. PWMScan introduced the usage of read mapper to solve this problem as an alternative to regular genome scanners (that it can also use). % ATAC-seq Finally, I modified ChIPPartitioning to create a de novo mofif discovery algorithm that can partition genomic regions based on their DNA sequence. I proposed to use this new algorithm together with ChIPPartioning to study the chromatin structure at TF binding sites. The results suggested that it was possible to study the heterogeneity of TF binding sites by using a 2 step procedure that i) aligns the regions on a given TF motif and ii) partitions the data to retrieve different groups of regions based on their chromatin accessibility patterns. In their current state, these results are preliminary. Several adjustments need to be done. For instance, the regions of interest were defined using peak calling. Stricto sensu there is nothing wrong with this. However, the full potential of DGF is not exploited. Peaks represent regions of high signal whereas footprints - which most likely are inside peaks - are the precise regions of interest. Defining the regions of interest as the footprint centers instead of peak center is likely to ease the problem of finding different classes of footprints. Furthermore, I proposed to use this framework to draw a catalog of possible chromatin/motif organizations from the pooled data that could be used to annotate each single cell in order to create cell molecular states that could be later used to find cell populations. Alternatively, the individual cells can be replaced by experiments/patients and the same strategy could be applied to discover groups of experiments or patients. % conclusions In conclusions, in this work, I tackled several different aspects of the bioinformatics research related to TF and chromatin biology. The results are in line with the current state of the knowledge in the field. In most cases they confirmed previous results and in some other, they complemented them. I also developed or participated to the development of softwares, resources and technologies that are already valuable assets to the research community, such as EPD or PWMScan or that have to potential to be so, such as SMiLE-seq or SPar-K. diff --git a/main/ch_introduction.tex b/main/ch_introduction.tex index d7313bc..e05da70 100644 --- a/main/ch_introduction.tex +++ b/main/ch_introduction.tex @@ -1,416 +1,416 @@ \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) are a class of nuclear proteins that can bind to specific DNA sequences and drive target gene expression. Thus TFs are a major regulator of gene expression. -The results reported in this work can be sub-divided in two sub-topics. The first topic focuses on data mining projects and reports the results of different computational genomic research projects that were focus on the characterization of the chromatin structure in the vicinity of TF binding sites as well as TF-TF interaction identification and on the modeling of TF DNA sequence specificity. The second topic focuses on the development of algorithms and computational methods to solve bioinformatic problems that are met in genomics. Several algorithms to identify important chromatin signatures or over-represented DNA sequences in the genome using a data partition approach are presented as well as a software that predicts TF binding sites in a genome given a specificity model. The two topics are not entirely separated and can be presented jointly in some chapters. +The results reported in this work can be sub-divided in two sub-topics. The first topic focuses on data mining projects and reports the results of different computational genomic research projects that were focused on the characterization of the chromatin structure in the vicinity of TF binding sites as well as TF-TF interaction identification and on the modeling of TF DNA sequence specificity. The second topic focuses on the development of algorithms and computational methods to solve bioinformatic problems that are met in genomics. Several algorithms to identify important chromatin signatures or over-represented DNA sequences in the genome using a data partition approach are presented as well as a software that predicts TF binding sites in a genome given a specificity model. The two topics are not entirely separated and can be presented jointly in some chapters. \section{About chromatin} \label{intro_about_chromatin} In eukaryotes, the DNA is stored in the nucleus. Each human 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 it wraps. Other families of proteins are also found in the chromatin, such as RNA polymerases, histone chaperones, helicases or TFs. \subsection{The chromatin structure} % structure, histones, nucleosomes, genome compaction \begin{figure} \begin{center} \includegraphics[scale=0.3]{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 right. 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 NCP and are accessible to other factors, unlike the inner part of the histone octamer. \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. Figure and legend taken and modified from \cite{mcginty_robert_k._and_tan_song_fundamentals_2014}.} \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, forming the nucleosome core particule (that I will later simply refer to as "nucleosome", Figure \ref{intro_chromatin}A). The DNA is kept wrapped around the histone octamer because of strong electrostatic interactions. Indeed, the DNA backbone, which is negatively charged in physiological conditions, shows a high affinity for the positively charged histones. As a consequence, the nucleosome is a quite stable structure. +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 wraps, forming the nucleosome core particule (that I will later simply refer to as "nucleosome", Figure \ref{intro_chromatin}A). The DNA is kept wrapped around the histone octamer because of strong electrostatic interactions. Indeed, the DNA backbone, which is negatively charged in physiological conditions, shows a high affinity for the positively charged histones. As a consequence, the nucleosome is a quite stable structure. -The histone 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 protruding out of the nucleosome and are hotspots for post-translational modifications (PTMs) \citep{kouzarides_chromatin_2007}. +The histone 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 a 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 protruding out of 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 histones", 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 the scope of this work. % chromatin fibers The genome is organized into a repetition of nucleosomes, each separated by a linker DNA, forming the 11-nm chromatin fiber. This chromatin conformation is quite relaxed and the DNA accessible. The H1 linker histone can be recruited in the chromatin, in which case it binds the linker DNA and makes it inaccessible. The 11-nm fiber is itself stored into a denser and less accessible structure called the 30-nm fiber (Figure \ref{intro_chromatin}B). Eventually, higher order structure are achieved, further increasing the genome compaction level \citep{ 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{weirauch_methods_2011}. 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. +It is now commonly accepted that the compaction of the genome comes with a trade-off. The DNA sequences found in nucleosomes are thought to be unaccessible for DNA reading processes such as TF binding whereas the linker DNA remains accessible \citep{weirauch_methods_2011}. 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 dynamic} % chromatin modification/remodelling Because the required activated genes may vary over time, for instance because of lineage commitment, the chromatin structure needs to be adapted. Some regions need to become accessible in order to be read while others are not needed anymore. Consequently, the chromatin is a highly dynamic structure that undergoes constant modifications. Two broad families of chromatin modifiers exist : ATPase chromatin remodelers and histone modifiers. % chromatin remodelers -ATPase chromatin remodelers are a group of proteins that are able to affect the chromatin packaging by interfering directly with the nucleosomes, 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 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 seems to be involved in 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 nucleosomes, 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 evict nucleosomes from DNA and are linked with chromatin opening. ISWI members tend to recognize unmodified H4 histones and catalyze nucleosome spacing and chromatin compaction. CHD members are less well functionally characterized but bear chromo domains that allow them to recognize histone methylation. Finally, INO80 members seem to be able to slide and evict nucleosomes and seems to be involved 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 combinations. 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 (referred to as writers), their recognition is also performed by dedicated proteins (referred to as readers) \citep{kouzarides_chromatin_2007,hyun_writing_2017}. This allows histone PTMs to be 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{zhou_charting_2011,hyun_writing_2017} and to be specifically recognized by CHD1, a member of the CHD chromatin remodelers \citep{hyun_writing_2017}. \subsection{About nucleosome 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 now 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, such as in yeast \citep{kubik_nucleosome_2015}, mouse \citep{west_nucleosomal_2014} or human \citep{schones_dynamic_2008, gaffney_controls_2012}. % strongly positioned nucleosomes -The wealth of data collected allowed to determined that nucleosomes do not cover the genome uniformly. Nucleosomes rather seem to show preferred locations were they sit at. Interestingly, single nucleosomes can be visualize from batch sequencing experiments, indicating that an important fraction of the cells bear nucleosomes at the same positions. In these cases, the nucleosomes are said to be "phased" or "strongly positioned" (see Figure \ref{intro_nucleosome_positioning}A). +The wealth of data collected allowed to determine that nucleosomes do not cover the genome uniformly. Nucleosomes rather seem to show preferred locations where they sit at. Interestingly, single nucleosomes can be visualized from batch sequencing experiments, indicating that an important fraction of the cells bear nucleosomes at the same positions. In these cases, the nucleosomes are 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. Their most prominent feature is the regularity of the spacing between the pairs of nucleosomes that are part of the array. Arrays can occur 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}. It has been proposed that the arrays resulted from the nucleosomes organizing with respect to a barrier (or anchor). In this case, the barrier would be CTCF. The regular array organization has been proposed to be propagated far from their anchors because the immediately flanking nucleosome positions are constrained by the barrier. In turn, these nucleosomes become a barrier for the following ones, and so one, eventually forming the array. However, because the degree of constrain diminishes at each new nucleosome, the nucleosomes are not sufficiently phased anymore throughout the cell population. They become fuzzy and the signal blur out at some point. This phenomenon is referred to as "statistical positioning" \citep{jiang_nucleosome_2009}. +Nucleosome arrays are a striking case of strongly positioned nucleosome. Their most prominent feature is the regularity of the spacing between the pairs of nucleosomes that are part of the array. Arrays can occur 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}. It has been proposed that the arrays resulted from the nucleosomes organizing with respect to a barrier (or anchor). In this case, the barrier would be CTCF. The regular array organization has been proposed to be propagated far from their anchors because the immediately flanking nucleosome positions are constrained by the barrier. In turn, these nucleosomes become a barrier for the following ones, and so one, eventually forming the array. However, because the degree of constrain diminishes at each new nucleosome, the nucleosomes are not sufficiently phased anymore throughout the cell population. They become fuzzy and the signal blurs out at some point. This phenomenon is referred to as "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. However, unlike for CTCF binding sites, the DNA sequence composition seem to be a major factor driving the nucleosome positioning \citep{dreos_influence_2016}. To wrap around the histone octamer the DNA should be curved, which requires some flexibility. 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 faces 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. 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. This is referred to as "rotational positioning" \citep{jiang_nucleosome_2009}. 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. Interestingly, the exact positioning of a nucleosome has a deep impact on the accessibility of the DNA. None 10bp displacements have the potential of changing a sequence orientation with respect to the histone core and thus its accessibility.(Figure \ref{intro_nucleosome_positioning}B). In vivo, both statistical and rotational positioning occur. Additionally, chromatin remodelers are also constantly catalyzing thermodynamically unfavorable nucleosome displacements in exchange of ATP hydrolysis. It is likely that each nucleosome is subjected to all of these phenomenons. However, on a single nucleosome basis, one may predominate over the others. \section{About transcription factors} % 1) specificity models, additivity, sequence scoring given model % 2) TF complexes % 3) co-binding scenarios % About TF and their structure TFs are a special class of proteins that is crucial for gene expression regulation. TFs have the special ability to recognize specific DNA sequences among others. Once recruited on the DNA template, TFs have the ability to regulate transcription by promoting or repressing the activity of the RNA polymerase II complex (RNAPII). In the first case, one speaks of (transcriptional) activators, in the second of (transcriptional) repressors. TFs share a modular architecture. Two types of domains are of particular importance for TF functions: the DNA binding domain (DBD) and the activation domain (AD). % DNA binding domain The DBD allows TFs to bind their DNA target. Many different DBDs exist, each one being structurally different than the others. The DBD structure if typically used to classify TFs into families. This is for instance the case in the TFclass database \citep{wingender_tfclass:_2013}. In metazoans, TFs have been grouped into four distinct super-families : i) the basic domain TFs, ii) the zinc coordinated TFs, iii) the helix-turn-helix TFs and iv) the $\beta$ scaffold TFs. Each type of domain has a different structure and thus can interact with different DNA structures and sequences \citep{weirauch_methods_2011}. Of further importance, a single DBD is able to recognize different yet similar sequences. Because the sequence differences have an impact on the TF-protein interaction interface, each sequence is bound with a different affinity. Biologically, having high and low affinity binding sites may be useful to tune the intensity of one TF action on a given gene. % activation domain In addition to their DBD, many TFs also bear an AD that is important for the regulation of transcription. ADs allow TFs to regulate gene expression directly, by interacting with the basal transcriptional machinery, or indirectly by recruiting co-regulators. Coupled with specific DNA recognition, this allows TFs to regulate the transcription of specific regions of the genome. Whether TFs exert an activator or a repressor role, depends on the exact interaction they can exert with the transcriptional machinery and on the co-regulators they can recruit \citep{latchman_transcription_1997}. Ultimately, the activity of a TF is regulated by controlling its access to the DNA. This can be done by sequestrating it in the cytoplasm (by any mean) or even by occupying its binding sites to impede the TF recruitement on the genome \citep{latchman_transcription_1997}. \subsection{TF co-binding} \label{intro_tf_cobinding} % 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) \begin{figure} \begin{center} \includegraphics[scale=0.4]{images/ch_introduction/TF_associations.png} \captionof{figure}{\textbf{Possible interaction scenarios between TFs} \textbf{A} Direct co-binding. The TFs dimerize and bind together on DNA. \textbf{B} Indirect co-binding. Both TF dimerize but only one binds the DNA, the other (the blue) is the tethering factor. \textbf{C} Independent co-binding. Both TF bind in close vicinity but without forming a complex. Both TFs may not be necessarily bound at the same time. \textbf{D} Interference. Both motifs partially or totally overlap each other.} \label{intro_tf_association} \end{center} \end{figure} -The four above-mentioned TF super-families offer a huge variety of different TFs and thus allows a substantial complexity in terms of transcriptional regulation. Nonetheless, life further expended the possible complexity of regulatory wiring by evolving different types of combinatorial TF co-binding \citep{field_methods_2011}. By TF co-binding, I mean a functional association of TFs that requires them to bind either as a complex or in close vicinity. Furthermore, from a strictly DNA-centric point of view, the binding of each TF does not need to be synchronous. One TF may bind after the other, even after it left. +The four above-mentioned TF super-families offer a huge variety of different TFs and thus allows a substantial complexity in terms of transcriptional regulation. Nonetheless, life further expanded the possible complexity of regulatory wiring by evolving different types of combinatorial TF co-binding \citep{field_methods_2011}. By TF co-binding, I mean a functional association of TFs that requires them to bind either as a complex or in close vicinity. Furthermore, from a strictly DNA-centric point of view, the binding of each TF does not need to be synchronous. One TF may bind after the other, even after it left. % direct co-binding -First, two TFs can dimerize, forming either homo- or hetero-dimers, and bind to DNA using both DBDs (Figure \ref{intro_tf_association}A). This is for instance the case of the members of the basic domain super-family, which contains the leucine zipper and helix-loop-helix families, which are obligated dimer in order to bind DNA \citep{weirauch_methods_2011}. This can be referred to as "direct co-binding". +First, two TFs can dimerize, forming either homo- or hetero-dimers, and bind to DNA using both DBDs (Figure \ref{intro_tf_association}A). This is for instance the case of the members of the basic domain super-family, which contains the leucine zipper and helix-loop-helix families, which are obligated dimers in order to bind DNA \citep{weirauch_methods_2011}. This can be referred to as "direct co-binding". % tethering Second, two TFs can dimerize and bind to DNA using only one of the DBDs. This will result in having one of the TF binding to DNA while the other one is tethering DNA through its interaction with the other TF (Figure \ref{intro_tf_association}B). This can be referred to as "indirect co-binding". % independent co-binding Third, two TFs can both bind DNA using their own DBDs, in close vicinity but without any physical interaction (Figure \ref{intro_tf_association}C). This is for instance the case at distal REs, where many TFs can be found to be bound at the same time. Synergistic co-binding of several TFs has been proposed as a mechanism by which close chromatin structures could be opened and distal regulatory elements (REs) activated \citep{field_methods_2011,heinz_selection_2015}. On the other hand, the binding of different TFs to a given region can be asynchronous. This is the case for TFs involved at different time of the activation cascade, such as what is happening during macrophage and B cell progenitors commitment \citep{heinz_simple_2010}. This can be referred to as "independent co-binding". % interference %Finally, two TFs binding motifs can overlap (Figure \ref{intro_tf_association}D). Different mechanisms may explain this phenomenon. A first possible explanation would be that two TFs compete to bind to the same region. This can occur in mechanisms linked to the regulation of a TF activity. In that case, as its binding site is occupied, TF binding is sterically hindered. A second possible explanation would be that, for some reason, only one TF binds, never the other. This can be referred to as "interference". Finally, two TF binding motifs can overlap (Figure \ref{intro_tf_association}D). The outcome is not clear but this can result in different plausible scenarios such as both TFs transiently binding the DNA or one TF winning the competition and stably binding, sterically excluding other TFs. This can be referred to as "interference". \section{Gene regulation in a nutshell} \label{intro_gene_regulation} % regulation gene expression definition The regulation of gene expression is a highly complex biological phenomenon which allows a proper allocation of resources to each individual gene such that the overall gene product output fits the cell needs as precisely as possible. % summary and aim of the section %The mechanisms that act to regulate gene expressions are diverse and operate in an intricate manner that is highly dynamic. The status of a gene, at a given time, is the results of the actions of activating and repressing mechanisms that act either on i) the recruitement of the pre-initiation complex (PIC) and the assembly of a functional RNAPII complex at gene regulatory elements (REs) or ii) the activation of the RNAPII complex. This section will briefly introduce each of these aspects and provide the necessary information for the further understanding of this work by the reader. The status of a gene, at a given time, is the results of the actions of activating and repressing mechanisms that, in fine, modulate the activity of the transcriptional machinery. This modulation takes place at different steps of the activation cascade of the transcriptional machinery. The really first step that occurs is the binding of general TFs, such as TFIID, that allows to recruit the catalytic subunits of the RNAPII, forming the pre-initiation complex (PIC). The further downstream steps include the recruitment of transcriptional regulator and chromatin remodelers, the proper positioning of the full RNAPII complex at the TSS and the activation of the RNAPII complex. This section will briefly introduce some of these aspects to provide the necessary information for the further understanding of this work by the reader. \subsection{The chromatin barrier} % chromatin is a barrier As discussed above (see section \ref{intro_about_chromatin}), the genome is stored as chromatin in the nuclei. Because nucleosome are bound to the DNA, they compete with other factors for binding. As such, the chromatin structure is a barrier to the recruitment of the PIC. On the brigh side, this is though to limit spurious activation of the RNAPII \citep{von_bakel_methods_2011}. On the other hand, this obviously also suppresses any gene expression. In human, the observation that TF binding is hindered by nucleosomes and that REs are nucleosome depleted suggest the existence of a mechanism that opens the chromatin at REs \citep{von_bakel_methods_2011}. \subsection{TFs cooperative binding} % cooperative binding to open nucleosomes The cooperative binding of TFs has been demonstrated to be able to open closed chromatin. In essence, this is a step-wise process during which a first TF binds its target on an accessible linker, leading to the destabilization of a neighboring nucleosome. This in turn increases the accessibility of a second TF binding site that can be engaged, further openining the chromatin. Eventually, the nucleosome is displaced or even evicted and the chromatin is locally opened \citep{von_bakel_methods_2011}. ATPase chromatin remodelers and/or of histone modifier can be recruited by TFs to set up a proper chromatin environment \citep{von_bakel_methods_2011}. The conditions for this phenomenon to ignite are not precisely known however several hypotheses and observations are of interest. First, compacted chromatin has been observed, in vitro, to undergo spontaneous transient local openings at the nucleosome entry sites. This phenomenon has been referred to as "nucleosome breathing" \citep{von_bakel_methods_2011}. This has the potential of creating windows of opportunity for TFs to engage their binding sites, in nucleosome arrays. Second, it has been hypothesized that, in human, the regions that show a high nuclosome density may facilitate the exclusion of the H1 histone. The rational is that the DNA linkers between any two nucleosomes is too short for H1 to bind. Eventually, H1 exclusion prevents the inclusions of these regions in more condensed chromatin structures while leaving the linkers somewhat accessible \citep{field_methods_2011}. Together with nucleosome breathing, this has the potential of creating engageable - but not open - windows throughout the genome. \subsection{Pioneer TFs} % pioneer TFs Alternatively, a special class of TFs named "pioneer factors" have been shown to be able to bind their target in a closed chromatin environment and to induce chromatin opening after binding \citep{zaret_pioneer_2011,iwafuchi-doi_pioneer_2014}. % introduce Fox1 to describe the precise mechanism of action The case of the prototypical pioneer factor FoxA1 (also called HNF3) is enlightening regarding the mechanistics of pioneer TFs. In liver, FoxA1 is able to bind the inactive \textit{albumin} enhancer and prime it for activation \citep{cirillo_opening_2002}. The enhancer activation is possible because of the hybrid nature of FoxA1. It binds DNA through its DBD, which has a similar structure to the H1 linker histone. Strikingly, FoxA1 can bind its motif directly on the nucleosome surface. Furthermore, FoxA1 posses a C-terminal domain that directly binds the histone core, which leads to the chromatin opening \citep{cirillo_opening_2002}. Alternatively, FoxA1 is also able to recruit co-regulators via its N-terminal trans-activation domain \citep{zaret_pioneer_2011}. Currently, many other pioneer TFs have been discovered, such as Oct4, Sox2 and Klf4 \citep{soufi_pioneer_2015} - also known together with myc as the "Yamanaka factors" - or PU.1 which has been shown to induce nucleosome remodeling at macrophage and B-cell specific enhancers \citep{heinz_simple_2010}. Interesting in this regard, most of the TFs that have been discovered to drive cellular reprogramming, such as the Yamanaka factors which have been shown to be sufficient to reprogram fibroblasts into stem cells \citep{takahashi_induction_2006} are pioneer TFs. \subsection{Regulatory elements} % RE definition Chromatin opening and the recruitment of the transcriptional machinery do not happen at random in the genome but is concentrated at REs. The specific recruitment of the transcriptional machinery regulators at given genomic locations allows to concentrate the regulatory signals on specific target genes. REs can be divided in two broad classes based on their vicinity to the gene(s) they regulate : proximal REs - or promoters - and distal REs. Both classes interact together by the mean of the genome 3D structural organization. % promoters Promoters are located immediately upstream of the target genes they regulate. Promoters functions are to recruit the RNAPII and position it properly for transcription. Interestingly two constrasting promoter groups have been identified with respect to their chromatin architectures \citep{cairns_logic_2009}. The first group includes house keeping genes. This group chromatin architecture tends to be constituvely open with a nucleosome depleted region (NDR), promoting gene expression. The second group contains highly regulated genes. Unlike the first group, these promoters tend to be constituvely covered by nucleosomes, hindering TFs and RNAPII recruitment. Their activation requires an active chromatin remodeling that is carried out by SWI/SNF ATPase family members. However, in both cases the chromatin is remodeled and a NDR is formed. The NDR usually contains core regulatory elements (CREs) involved in the recruitment of general TFs leading to the assembly of the RNAPII \citep{lenhard_metazoan_2012}. % enhancers Distal REs are located at distances that vary from kilobases to megabases from their target genes and have the ability to influence gene expression positively, in which case they are referred to as 'enhancers', or negatively, in which case they are referred to as 'silencers'. Distal REs are enriched with closely spaced TF recognition sequences that serve for the recruitement of TFs. In turn, TFs allow to recruit other transcriptional co-regulators such as histone modifiers \citep{heinz_selection_2015}. Through chromatin looping phenomenons, the recruited TFs (and all other factors) are brought in close spatial vicinity with target gene promoters. This increases TF concentrations (as well as other regulatory factors bound) at the promoter level and allows to strengthen regulatory signals directly where the RNAPII is sitting \citep{heinz_selection_2015}. Distal REs are not always active. Instead they are highly cell line specific and thus are important determinant of the cell identity \citep{heinz_selection_2015}. Distal REs activation requires to open the chromatin in order to be accessible for TFs to bind. Currently, both cooperative TF binding and pioneer TFs are though to be involved in chromatin opening and remodeling \citep{heinz_selection_2015}. Upon chromatin opening, specific histone PTMs are deposited, such as H3 lysine 4 mono-methylation (H3K4me1), H3K4me2 or H3 lysine 27 acetylation (H3K27Ac) \citep{zhou_charting_2011}. For instance, during B-cell and macrophage lineage commitment, PU.1 and EBF1 are essential TFs which action activate cell type specific enhancers, leading to the enforcement of differential genomic programs \citep{boller_pioneering_2016, heinz_simple_2010}. Failure to do so leads to lineage commitment defects \citep{hagman_early_2005,kurotaki_transcriptional_2017}. \subsection{The genome goes 3D} % TADs Finally, another layer of complexity involved in the regulation of gene expression can be added : the 3D organization of the genome. Nowadays it is clear that in the nucleus, the genome spatial organization is tightly regulated and that it has a functional meaning \citep{bonev_organization_2016}. As described above, enhancers and promoters physically interact together through loops. These looping phenomenons do not happen at random. The genome is organized into compartments, also called topological association domains (TADs). A TAD can be seen as high level chromatin loop in which the physical interactions between loci are favored compared to interactions with loci outside of the TAD. As a matter of fact, enhancers scope of action is limited to the TAD they are located in. Thus TADs can be seen as a functional regulatory genomic domains. TADs are thought to be established and maintained by a dedicated set of structural proteins and complexes including CTCF and the cohesin complex \citep{bonev_organization_2016}. CTCF seem to have two major functions. First it seems to facilitate promoter/enhancers interactions, within TADs and to promote gene expression. Second, CTCF has been found to be enriched at TAD borders and seems to be important for their proper delimitation \citep{ong_ctcf:_2014}, likely through a loop extrusion mechanism \citep{ghirlando_ctcf:_2016}. This second function is compatible with the insulator function of CTCF. Because it marks the boundary between TADs, enhancer/promoter interactions over this limit cannot happen. Finally, CTCF is often found to interact with the cohesin complex \citep{stedman_cohesins_2008}. The cohesin complex is composed of four members : SMC1, SMC3, RAD21 and either STAG1 or STAG2 \citep{losada_cohesin_2014}. Together they form a ring-like structure in which two DNA molecules are trapped and maintained together. This structure is one of the mechanisms allowing to pinch DNA and to form loops. The cohesin complex is important for both promoter/enhancer interactions and TADs maintenance \citep{losada_cohesin_2014,bonev_organization_2016}. \section{Measuring chromatin features} \label{measuring_chromatin_features} The occupancy of the difference components of the chromatin, the chromatin accessibility or even the sequence preference of TFs can be measured using dedicated assays. This section introduce the necessary information to further understand this work. \subsection{Measuring TF binding in vivo} % ChIP The advent of chromatin immuno-precipitation (ChIP) is central for the study of TFs. In essence, it consists in extracting the chromatin from the cell nuclei, shearing it either mechanically or enzymatically and adding an antibody (Ab) against a DNA binding protein of interest. The IP step allows to pull-down the Ab, its target as well as the DNA fragment it is bound to. % history of DNA detection methods Different methods, with varying throughput, have been used to identify of the purified DNA fragments. First, specific loci of interest were assayed by PCR. Then the growing availability of DNA microarrays allowed to drastically increase the throughput by testing a wide number of pre-selected loci at once \citep{odom_methods_2011}. Finally, protocols subjecting the purified DNA to high throughput sequencing (ChIP-seq) \citep{barski_high-resolution_2007,robertson_genome-wide_2007} allowed to identify the bound loci in an agnostic way, with an unprecedented throughput. % ChIP-seq ChIP-seq has truly revolutionized genomics and the study of TFs. In a single assay, it is possible to obtain a digital readout of TF binding sites. Mapping the sequenced reads to the genome of interest allowed to create a per position occupancy score, creating a digital readout of the TF occupancy. However, because the TF binding sites are smaller than the sequenced fragments, the precise location of the TF binding remains unknown. Interestingly, ChIP-seq allows to list the regions of the genome that are occupied and also provide an estimate of the binding affinity for the regions. Indeed, the stronger the propensity to bind to a given sequence (the affinity), the higher the probability of binding. This should be proportionally reflected in the density of signal \citep{jothi_genome-wide_2008}. Thus ChIP-seq allows to identify regions +/- 100bp in which a TF binds. Nonetheless, it is possible to identify over-represented DNA sequence motifs from these regions using de novo motif discovery methods (see section \ref{intro_aligning_binding_sites}). Typically, the identified sequence motifs belong to i) the TF of interest and/or ii) co-binders (see section \ref{intro_tf_cobinding}). \subsection{Measuring TF binding in vitro} In vivo measurement of TF binding as several drawbacks. ChIP-seq allows to estimate the binding specificity of TF however it has been proposed that de novo motif discovery method mostly capture the high affinity features of the TF binding specificity \citep{stormo_determining_2010}. Additionally, in vivo, the chromatin exert an effect on TF binding (see section \ref{intro_gene_regulation}). In regard to these limitations, in vitro binding assays offer experimental solutions to investigate i) TF binding over a wider range of affinities and ii) TF intrinsic specificity, without the chromatin influence. In the recent years, many different technologies have been developed to investigate TF binding in vitro. % MITOMI Microfluidic devices are typically composed of hundreds (if not more) of individual chambers and of the necessary piping to flow all the necessary reagents within each cell to run as many reactions in parallel. The reaction chambers are small and allow to use microliter reaction volumes. Maerkl and colleagues \citep{maerkl_systems_2007,geertz_massively_2012} have developed the mechanically induced trapping of molecular interactions (MITOMI). This assay is based on a microfluidic device that allows to run hundred of affinity assays with a given TF, in parallel. Each assay is run using a different designed oligo-nucleotide of known sequence. % HT-SELEX Originally, systematic evolution of ligands by exponential enrichment (SELEX) has been designed to discover few high affinity binding sequences \citep{tuerk_systematic_1990}. The SELEX assay was adapted to become high throughput SELEX (HT-SELEX, \citep{roulet_high-throughput_2002,zhao_inferring_2009,jolma_multiplexed_2010}). HT-SELEX assays a TF specificity by allowing a binding reaction between the TF and tens of millions of different DNA sequences of typically 20-30bp. The bound DNA molecules are purified by pulling down the TF. The purified DNA can either be sequenced using high throughput sequencing or be subjected to another cycle of selection. Repeated cycles allow to isolate higher affinity binders, eventually only returning a few hundreds. Under a limited number of cycles, this method has a large dynamic scale of binding affinities \citep{stormo_determining_2010} and allows to obtain a digital readout. However, the repeated cycles can introduce biaises that are hard to model in order to properly estimate the binding affinities. % PBM For completeness, protein binding microarrays (PBMs, \citep{bulyk_exploring_2001,mukherjee_rapid_2004,berger_compact_2006}) should also be mentioned. Typically, a PBM device is a chip on which tens of thousands of DNA probes are immobilized. The probes are arranged into spots such that only one probe specie is present per spot. A purified TF is then added on the chip and the TF binding is revealed using a fluorescent-labeled Ab. Because the identity of the probe specie in each spot is known, the affinity to this specie can be directly measured as the intensity of the fluorescent signal. The higher the affinity for a probe specie, the more TF binds to the spot, the stronger the fluorescent signal. The most important limitation of PBM is its limited space on the chip which restrict the number of different spots that can be present. Assaying all possible $4^{L}$ sequences of a given length $L$ is not possible passed a given length. To circumvent this, the sampling of the deposited sequences should be performed with caution to maximize the information on a single chip \citep{berger_universal_2009}. Additionally, it has been suggested that the position of the spot on the chip could influence the TF binding. \subsection{Measuring nucleosome occupancy} % MNase The micrococcal nuclease (MNase) - an endo-exo nuclease - is a key factor in producing nucleosome occupancy maps. Subjecting a chromatin extract to a MNase treatment, upon proper experimental conditions, releases "a ‘ladder’ of discrete DNA fragments" \citep{voong_genome-wide_2017} which sizes correspond to mono-, di-, tri-, and so on nucleosome fragments. The MNase is able to digest accessible linker DNA (endo-nuclease activity) and to trim the nick edges (exo-nuclease activity). The nucleosomal DNA is protected from digestion as the histone octamer sterically hinders the MNase access to its substrate \citep{voong_genome-wide_2017}. % MNase-seq Originally, MNase treated DNA was selectively amplified using PCR to map precise nucleosomes. The advent of microarray technologies allowed to interrogate entire genomes, even though the created map had relatively low resolution \citep{jiang_nucleosome_2009}. Eventually, this limitation was circumvented by subjecting the MNase treated chromatin fragments to next generation sequencing (-seq) \citep{schones_dynamic_2008}. The advent of MNase-seq lead to the creation of high resolution - down to individual nucleosomes - genome-wide nucleosome maps \citep{schones_dynamic_2008, gaffney_controls_2012, west_nucleosomal_2014, kubik_nucleosome_2015}. % data treatment Mapping the sequenced fragment of MNase-seq assay against a genome of reference produces a digital readout of the nucleosome density per genomic position. If single-end sequencing is used, the nucleosome center (the dyad) can be inferred by shifting the read position by \~70bp. If paired-end sequencing is performed, mono-nucleosome fragments can be selected based on their sizes (~150bp) and the dyads can be inferred as being their central positions. % limitations i) A/T sequence specificity ii) all nucleosomes likely not mapped If MNase-seq allows to unravel nucleosome occupancy with an unprecedented resolution, it also suffers some limitations. First, MNase has been demonstrated to exhibit a sequence preference toward A/T rich sequences, which could potentially lead to an overdigestion of nucleosome fragments in A/T rich regions \citep{voong_genome-wide_2017}. Second, some nucleosomes have been demonstrated to be "fragile" to the experimental conditions. In yeast, specific nucleosomes were found to be sensitive to the MNase concentration and could only be detected with reduced MNase concentrations \citep{kubik_nucleosome_2015}. Here, the MNase sequence preference may be at play. But another case of fragile nucleosomes was found in human, independently of the use of MNase. In this case, the fragile nucleosomes contained replacement histones and were sensitive to regular salt concentrations used during a ChIP-seq experiment \citep{jin_h3.3/h2a.z_2009}. Thus, it is likely that MNase-seq is not able to map all the nucleosome in a given genome. \subsection{Digital footprinting} \label{intro_dgf} % DGF / DNase-seq / ATAC-seq, footprint \begin{figure} \begin{center} \includegraphics[scale=0.3]{images/ch_introduction/dgf.png} \captionof{figure}{\textbf{Digital footprinting :} \textbf{A} DNase-seq uses the endonuclease DNaseI to cleave DNA within accessible chromatin. Endonuclease cleavage is greatly attenuated at the protein-bound loci (the red crosses denote cleavage blockade). Accessible library fragments are generated by barcoding each cleavage site independently after restriction digestion (single cut) or as proximal cleavage pairs (double cut). \textbf{B} Assay for transposase-accessible chromatin using sequencing (ATAC-seq) uses a hyperactive transposase (Tn5) to simultaneously cleave and ligate adaptors to accessible DNA. \textbf{C} The purified DNA fragments are then subjected to massively parallel sequencing and mapped to the reference genome to generate a digital readout of per-nucleotide insertion (DNaseI nick or Tn5 transposition event) genome-wide. Figure and legend taken and adapted from \citep{vierstra_genomic_2016, klemm_chromatin_2019}.} \label{intro_figure_dgf} \end{center} \end{figure} % DGF Digital genomic footprinting (DGF) methods are a powerful mean to reveal the active REs in a genome. DGF gives a measure of the chromatin accessibility genome-wide. The essence of DGF assay relies on reagents - enzymes or chemicals (this work will only cover enzymes) - that are able to generate single- or double-stranded DNA cleavages into a chromatin-stored DNA template \citep{tsompana_chromatin_2014, vierstra_genomic_2016} DGF assays relies on a selective degradation of the loci stored in accessible chromatin followed by high throughput sequencing (-seq). The degradation of the accessible chromatin regions can be performed using either DNaseI (DNase-seq, \cite{neph_expansive_2012}) or a modified Tn5 transposon system (assay for transposable accessible chromatin, abbreviated ATAC-seq, \cite{adey_rapid_2010,buenrostro_transposition_2013}). % DNase-seq DNaseI is an endonuclease. Under proper ionic conditions, this enzyme introduces double-strand breaks in the genome based on the DNA accessibility, with a minor sequence specificity \citep{herrera_characterization_1994}, as shown in Figure \ref{intro_figure_dgf}A. On a technical note, DNase-seq assays are quite sensitive assays. Achieving a proper chromatin degradation - that is, avoiding over-digestion - is not an easy task and requires careful enzymatic titrations. % ATAC-seq ATAC-seq assays rely on a modified Tn5 transposase enzyme to selectively fragment the accessible regions of the genome (\cite{adey_rapid_2010,buenrostro_transposition_2013}, Figure \ref{intro_figure_dgf}B). The enzyme inserts small double-stranded barcodes inside the DNA wherever it is accessible resulting a the creation of double-strand breaks. This process, known as tagmentation, allows to i) fragment the genome and ii) insert sequencing barcodes at once. It should be noted that the Tn5 acts as an homodimer and thus inserts two copies of the same adaptors separated from each other by 9bp \citep{adey_rapid_2010}. % output In both cases, the genome is chopped down into fragments starting (and ending) wherever the chromatin is accessible. A sequencing library is then created from the fragments and their ends are sequenced using high throughput sequencing technologies. Finally, the insertion sites are located by mapping the sequenced reads against the reference genome of interest. This eventually leads to the creation of a per position cut (nicks for DNase-seq, insertions for ATAC-seq) density (Figure \ref{intro_figure_dgf}C). % footprinting Whenever a TF, a nucleosome or any other factor is engaged in a binding interaction with the DNA template, steric hindrance phenomenons protect the DNA from being degraded by the enzyme, leading to the creation of a typical signal diminution called "footprint" (Figure \ref{intro_figure_dgf}C). Formally, a footprint is a degradation signal drop over a DNA sequence that is protected from degradation because of binding event \citep{vierstra_genomic_2016}, but I will later use the term "footprint" the refer to signal drop in a degradation signal for aggregation profiles as well. DGF assays encounter a yet ever-growing popularity because of the wealth of data produced in a single experiment. Indeed, instead of running thousands - one per transcription factor (TF) - of ChIP-seq assays to know where each TF is binding, it is sufficient to run a single chromatin accessibility assay. Additionally, DGF is totally agnostic in the sense that it does not require a prior knowledge of the factors to look for. However, if DGF reveals the active regulatory regions, it does not provide the information about which factors bound. Methods to circumvent this limitation will be discussed later. \section{Modeling sequence specificity} \label{intro_specificity_modeling} In the nucleus, the number of potential binding sites for a TF is incredibly high. Most of these sites are non-sites and a minority are bona fide binding sites. The ability of a TF to distinguish between both is called "specificity". Additionally, all binding site are not equal. Some are bound tighter than others. The forces and the propensity with which a sequence is bound by a given TF is called the affinity. Modeling TF specificity has been an crucial issue in biology as it allows to predict where a given TF binds in the genome and to infer its regulatory targets. However because TFs recognize degenerated sequence motifs, solving this problem turned out to be complicated. \subsubsection{The physics approach to PWMs} Let us assume a simple binding reaction between a TF $TF$ and a DNA sequence $S$, at the equilibrium, \ch{TF + S <>[][] TF.S}. The chemical definition of the affinity is the association constant $k_{a}$ : \begin{equation} \begin{aligned} k_{a} = \frac {[TF \cdot S]} {[TF] \times [S]} \end{aligned} \label{intro_ka} \end{equation} where the square brackets indicate the concentrations. Once at the equilibrium, this reaction releases a standard free energy equals to : \begin{equation} \begin{aligned} \Delta G^{0} = -R \times T \times ln(k_{a}) \end{aligned} \label{intro_delta_free_energy} \end{equation} where $R$ is the perfect gaz constant and $T$ the absolute temperature. When $\Delta G^{0} < 0$, $K>1$ indicating that, at the equilibrium, the product TF-S of the reaction is favored. The more product at equilibrium, the stronger the affinity of $TF$ for $S$ (see equation \ref{intro_ka}). The more energy is released by the reaction, the stronger $S$ is bound by $TF$. From this, for a given $TF$, it is possible to create a $\Delta G^{0}$ table for all possible sequences which would allow to predict the $TF$ specificity. In competition, the probability of binding to a sequence is directly proportional to the affinity. However, this is at least labor intensive and at most experimentally intractable. To circumvent this obstacle, the hypothesis of positional independence was formulated. The hypothesis states that the reaction $\Delta G^{0}$ is an additive function of the individually recognized bases. Thus, each base recognized does not influence the recognition of any base at any other position. From the beginning, this hypothesis was recognized to be an approximation that would apply to most of the cases but no all. Even though, this assumption was a subject of controversy \citep{man_non-independence_2001,bulyk_nucleotides_2002}. However, it is nowadays commonly admitted that, even if the assumption is violated in some cases, it does still allow to represent accurately most of the cases \citep{benos_additivity_2002,zhao_quantitative_2011}. Originally, the measurement of affinity was a labor intensive - and still is today - and was assayed using gel shift assays. Thus performing one affinity measurement for each of the $4^{L}$ sequences of length $L$ did not belong to the realm of the possible. Instead, working under the hypothesis of positional independence allowed to drastically lower the experimental workload and only requires to run an assay for each of the $3*L+1$ single position mutants. In turn, this allows to construct a matrix containing the $\Delta\Delta G^{0}$ induced by each base being recognized at each position of the binding site \citep{stormo_specificity_1998}. This matrix is called a position weight matrix (PWM) and allows to predict the affinity of $TF$ for any sequence. In such PWMs, the strongest affinity site is usually given a $\Delta G^{0}$ equals to 0 and all other $\Delta\Delta G^{0}$ values are expressed with respect to this strongest affinity site. Finally, it should be noted that inside a PWM, positive (unfavourable) $\Delta\Delta G^{0}$ are often turned to negative values such that the highest affinity binding site achieves the maximal score (corresponding to the lowest (favourable) $\Delta G^{0}$). \subsubsection{The statistical mechanic approach to PWMs} The TF sequence specificity has also be tackled from a statistical point of view. Given an alignment $A$ of binding sites of length $L$, it is possible to construct a letter probability matrix (LPM) or a letter frequency matrix (LFM) that contains either the number of time each base appears at each position of $A$ or the corresponding probabilities, respectively. % Obviously, the importance of a particular base at a given position within the binding site is directly proportional to its probability of appearing. In 1986, Schneider and colleagues quantified the importance of each positions in a binding site using the information content \citep{schneider_information_1986}. Nonetheless, this approach is not yet quantitative, only qualitative. It does not allow to predict whether a site will be bound with a stronger affinity than an other one. Indeed, the most frequent binding site is not necessarily the highest affinity binding site. In 1986, Schneider and colleagues quantified the sequence conservation at each position of $A$ using the information content \citep{schneider_information_1986}. In 1987, Berg and von Hippel demonstrated that this statistical approach was mathematically related to the estimation of binding affinities \citep{berg_selection_1988}. Given $A$, a PWM can be build using : \begin{equation} \begin{aligned} \lambda \varepsilon_{jb}^{obs} = ln \frac{f_{j0}^{obs}} {f_{jb}^{obs}} \end{aligned} \label{intro_eq_pwm_berg} \end{equation} where $\varepsilon_{lb}^{obs}$ is a dimension less positive number that expresses the decrease of binding energy by the binding of base $b$ at position $j$, $f_{jb}^{obs}$ is the probability of observing a base $b$ at position $j$ in the binding site, $f_{j0}^{obs}$ is the probability of the base present at position $j$ in the strongest binder present in $A$ and $\lambda$ is a dimensionless scaling factor. Stormo and Fields used a slightly different approach \citep{stormo_specificity_1998} and proposed to link the information content of $A$ to the estimation of the binding energy using : \begin{equation} \begin{aligned} W_{b,j} = log_{2} \frac {F_{b,j}} {p(b)} \end{aligned} \label{intro_eq_pwm_stormo} \end{equation} where $f_{b,j}$ is the number of occurrences of base $b$ at position $j$ in the alignment $A$, $p(b)$ the probability of a given base $b$ and $W$ is the PWM. Note that equations \ref{intro_eq_pwm_berg} and \ref{intro_eq_pwm_stormo} give inversly proportional results because of the inverted fractions. \subsection{Aligning binding sites} \label{intro_aligning_binding_sites} % obtaining the alignment The above solution request to have a set of aligned binding sites $A$ to compute the PWM. However, most of the times, we do not have access to this information. Rather, we have a set of longer sequences in which we know that the TF of interest binds, but not exactly where. Many algorithms - typically called "de novo motif discovery" algorithms - have been developed to construct a matrix from a set of unaligned sequences. The algorithm developed by Stormo and Hartzell \citep{stormo_identifying_1989} finds an optimal alignment by maximizing a modified version of the alignment information content \citep{schneider_information_1986}. Alternatively, the algorithms developed by Hertz and Stormo \citep{hertz_identification_1990} and Lawrence and Reilly \citep{lawrence_expectation_1990} build a LPM that maximize the likelihood of observing the data under the hypothesis that they have been generated from the discovered model. Lawrence and Reilly's algorithm solves the alignment problem using an Expectation-Maximization (EM) algorithm, which makes their algorithm a heuristic. This type of framework has also been used to develop MEME, excepted that it explicitly models the data as a mixture of binding and non-binding sites \citep{bailey_fitting_1994}. \subsection{Platitudes} \begin{figure} \begin{center} \includegraphics[scale=0.2]{images/ch_introduction/figure_pwm.png} \captionof{figure} {\textbf{Position weight matrix : } \textbf{A} Human JunD (JUND\textunderscore HUMAN.H10MO) PWM from the HOCOMOCO version 10 collection \cite{kulakovskiy_hocomoco:_2016}. \textbf{B} Corresponding PWM logo. The palindromic nature of the recognized motif is explained by the fact that JunD belongs to the basic helix-loop-helix family. As such, it is obligated to hetero- or homo-dimerize with another member of its family to bind DNA. Both \textbf{A} and \textbf{B} were taken from http://ccg.vital-it.ch/ssa/oprof.php}. \label{intro_pwm_logo} \end{center} \end{figure} % types of matrices, conversion possible LFMs and LPMs are often use to summarize an alignment $A$ and are often falsely referred to as PWMs. In all cases, LFMs and LPMs can always be converted to a PWM. % popularity Overall, PWMs (and related matrices) are conceptually straightforward to apprehend and easy to work with. For instance, they can be visualized as a sequence logo (\cite{schneider_sequence_1990}, Figure \ref{intro_pwm_logo}). Because of this and of the possibility to estimate affinity values from a sequence alignment, PWMs are popular and remain the most widely used type of model to represent TF specificity. % databases Nowadays, large collections of TF specificity matrices are available from publicly available libraries such as JASPAR \citep{khan_jaspar_2018}, HOCOMOCO \citep{kulakovskiy_hocomoco:_2018} or CIS-BP \citep{weirauch_determination_2014} to cite only the most famous. \subsection{Predicting binding sites} TF specificity models are typically used for classifications problems. The problem can be stated as follows : given a sequences $S$ of length $L$ and a PWM \textbf{W} of dimensions $Lx4$, predict whether $S$ will be bound. A common way is to define a threshold score $t$ and compute \begin{equation} \begin{aligned} score(S) = \sum_{i=1}^{L} W_{i,b} \end{aligned} \label{intro_eq_score_pwm} \end{equation} -If $score_{S} \geq t$ then $S$ is accepted as a binding site. The non-trivial part is to define a meaningful threshold score. A conceptually similar thing can be done with a LPM \textbf{M}. It is possible to compute probability of observing the data given the model, the likelihood $p(S|M)$ : +If $score_{S} \geq t$ then $S$ is accepted as a binding site. The non-trivial part is to define a meaningful threshold score. A conceptually similar thing can be done with a LPM \textbf{M}. It is possible to compute the probability of observing the data given the model, the likelihood $p(S|M)$ : \begin{equation} \begin{aligned} p(S|M) = \prod_{i=1}^{L} M_{i,b} \end{aligned} \label{intro_eq_score_prob} \end{equation} In turn, using Bayes'theorem, the posterior probability can be computed : \begin{equation} \begin{aligned} p(M|S) = \frac{P(S|M) \times p(M)} {\sum_{i} P(S|M_{i}) \times p(M_{i})} \end{aligned} \label{intro_eq_post_prob} \end{equation} where $p(M)$ and $p(M_{i})$ are model probabilities, which can be interpreted as the prevalence of a given family of sites in the case of a mixtures of binding site families. \section{Over-represented patterns discovery} \label{intro_pattern_discovery} \begin{figure} \begin{center} \includegraphics[scale=0.3]{images/ch_introduction/shift_flip.png} \captionof{figure}{\textbf{Signal comparison} between two regions $r_{1}$ and $r_{2}$ bearing a nucleosome array, measured by MNase-seq, at their ending and beginning edges. The bars indicate the density of sequencing reads stacked at each position. \textbf{A} The regions are compared position-wise, as they are, which results in a strong dissimilarity as the arrays are not aligned. \textbf{B} Performing the comparison after flipping $r_{2}$ result in both regions being highly similar. \textbf{C} A shorter stretch of signal of length $L' < L$ from $r_{1}$, highlighted in red and corresponding to the array, is searched in $r_{2}$. In this case, because a highly ressembling strech of signal, in red, can be found in $r_{2}$, both regions are considered highly similar.} \label{intro_figure_comparison} \end{center} \end{figure} Next generation sequencing (NGS) technologies allowed to easily characterize many of the chromatin features genome wide (see section \ref{measuring_chromatin_features}). The wealth of such datasets rapidly required automated discovery procedures in order to extract leading trends. For instance, MNase-seq reveals the nucleosomes over a genome. Asking whether the nucleosome architecture is overall the same between two regions or how many different types of nucleosome architectures are present over a set of selected regions are quite reasonable questions. To answer them, automated discovery procedures of over-represented patterns are needed. % The need for procedures discovery of over-represented patterns in the genome - either DNA sequences or sequencing signal - is a crucial technical part of genomic bioinformatics as they allow to extract prominent signals. The de-novo discovery of archetypical chromatin architectures, from sequencing read densities, over a set of regions of interest is a long standing problem in bioinformatics and is related to the problem of binding site alignment discussed in section \ref{intro_aligning_binding_sites}. More formerly, given a matrix $R$ of dimensions $NxL$ containing $N$ vectors of read counts $r_{1}, r_{2}, ..., r_{N}$ of length $L$, each containing the number of reads mapping at a given position in a given region, find $K \leq N$ vectors of length $L' = L$ that contain archetypical signals found in the $N$ regions of $R$. This can actually be solved using clustering methods which group regions that look alike into $K$ groups. Partitioning methods compare pairs of regions in order to state whether they resemble each other or not and break down $R$ in $K$ groups. The summary of the signal inside each group - for instance the mean signal for the K-means algorithm - can then be interpreted as the archetypical chromatin architectures. Biologically, these different organizations may reflect different functions associated with the regions of interest. However, this can only be done properly if the positions in two regions are functionally similar. Indeed, a second issue is entangled with this heterogeneity problem : an alignment problem. Two stretches of signal, in two different regions, may be similar but located at different positions in the region. For instance two regions can be covered at 50\% by a nucleosome array, in one case over the first half, in the second case, over the second half (Figure \ref{intro_figure_comparison}A). In this case, detecting that both regions bear an array can be done in two ways : i) flipping one region such that both arrays are localized at the same edge (Figure \ref{intro_figure_comparison}B) or ii) by searching archetypical signals of length $L' \leq L$ by scanning the entire regions (Figure \ref{intro_figure_comparison}C). % Positional correlations are a usual way or representing data in bioinformatics. They consists of measuring a signal of interest (the target signal) around a set of positions of interest (the references) in order to shed light on their functional relationship. The data are usually represented as a $N$ rows and $L$ columns matrix in which each row represent the signal over a region which contains a reference at a constant position. -This alignment problem has several roots. Let us assume that we measured nucleosome occupancy around TF binding sites. The nucleosome occupancy signal may not be aligned from one region to the next for at least three non-exclusive reasons. First, there may be an error in the estimation of the position of the TF binding site. For instance, ChIP-seq estimates binding +/-100bp. Assuming that a TF binding in the center of this region or where the signal is the highest do not guarantee to properly estimate the binding site. Secondly, the chromatin features can appear at a varying distance from the reference. For instance, two binding sites may be located at different distance from the closest nucleosome. Finally, the regions can show a functional orientation. For instance, TF binding sites have an upstream and a downstream. The same is true for TSSs. +This alignment problem has several roots. Let us assume that we measured nucleosome occupancy around TF binding sites. The nucleosome occupancy signal may not be aligned from one region to the next for at least three non-exclusive reasons. First, there may be an error in the estimation of the position of the TF binding site. For instance, ChIP-seq estimates binding +/-100bp. Assuming that a TF binding in the center of this region or where the signal is the highest do not guarantee to properly estimate the binding site. Secondly, the chromatin features can appear at a varying distance from the reference. For instance, two binding sites may be located at different distances from the closest nucleosome. Finally, the regions can show a functional orientation. For instance, TF binding sites have an upstream and a downstream. The same is true for TSSs. Finally, if both regions are aligned, it is worth mentioning that the signal over one region may be sparser because of a sub-optimal sequencing depth that has nothing to do with biology. The study of signal distribution over genomic regions has been a quite active field for sequencing experiments during the last decade. Dedicated algorithms and software have been developed to discover chromatin patterns, such as ChromaSig \citep{hon_chromasig:_2008}, ArchAlign \citep{lai_archalign:_2010}, CATCHProfiles \citep{nielsen_catchprofiles:_2012}, CAGT \citep{kundaje_ubiquitous_2012} and ChIPPartitioning\citep{nair_probabilistic_2014}. However, individual programs do not always handle all aspects of heterogeneity. 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321) defining Unicode char U+0142 (decimal 322) defining Unicode char U+0143 (decimal 323) defining Unicode char U+0144 (decimal 324) defining Unicode char U+0145 (decimal 325) defining Unicode char U+0146 (decimal 326) defining Unicode char U+0147 (decimal 327) defining Unicode char U+0148 (decimal 328) defining Unicode char U+014A (decimal 330) defining Unicode char U+014B (decimal 331) defining Unicode char U+014C (decimal 332) defining Unicode char U+014D (decimal 333) defining Unicode char U+014E (decimal 334) defining Unicode char U+014F (decimal 335) defining Unicode char U+0150 (decimal 336) defining Unicode char U+0151 (decimal 337) defining Unicode char U+0152 (decimal 338) defining Unicode char U+0153 (decimal 339) defining Unicode char U+0154 (decimal 340) defining Unicode char U+0155 (decimal 341) defining Unicode char U+0156 (decimal 342) defining Unicode char U+0157 (decimal 343) defining Unicode char U+0158 (decimal 344) defining Unicode char U+0159 (decimal 345) defining Unicode char U+015A (decimal 346) defining Unicode char U+015B (decimal 347) defining Unicode char U+015C (decimal 348) defining Unicode char U+015D (decimal 349) defining Unicode char U+015E (decimal 350) defining Unicode char U+015F (decimal 351) defining Unicode char U+0160 (decimal 352) defining Unicode char U+0161 (decimal 353) defining Unicode char U+0162 (decimal 354) defining Unicode char U+0163 (decimal 355) defining Unicode char U+0164 (decimal 356) defining Unicode char U+0165 (decimal 357) defining Unicode char U+0168 (decimal 360) defining Unicode char U+0169 (decimal 361) defining Unicode char U+016A (decimal 362) defining Unicode char U+016B (decimal 363) defining Unicode char U+016C (decimal 364) defining Unicode char U+016D (decimal 365) defining Unicode char U+016E (decimal 366) defining Unicode char U+016F (decimal 367) defining Unicode char U+0170 (decimal 368) defining Unicode char U+0171 (decimal 369) defining Unicode char U+0172 (decimal 370) defining 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LaTeX info: "xparse/define-command" . . Defining command \IfChemCompatibilityTF with sig. 'mm+m+m' on line 190. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \IfChemCompatibilityT with sig. 'mm+m' on line 193. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \IfChemCompatibilityF with sig. 'mm+m' on line 196. ................................................. (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros5.sty Package: chemmacros5 2017/08/28 v5.8b comprehensive support for typesetting che mistry documents (CN) \l__chemmacros_tmpa_dim=\dimen305 \l__chemmacros_tmpb_dim=\dimen306 \l__chemmacros_tmpc_dim=\dimen307 \l__chemmacros_tmpa_int=\count334 \l__chemmacros_tmpb_int=\count335 \l__chemmacros_tmpc_int=\count336 \l__chemmacros_tmpa_box=\box70 \l__chemmacros_tmpb_box=\box71 \l__chemmacros_tmpc_box=\box72 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemModule with sig. 'smmO{5.0}' on line 258. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \usechemmodule with sig. 'm' on line 262. ................................................. \g__file_internal_ior=\read2 (chemmacros) Loading module `base'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.base.code .tex File: chemmacros.module.base.code.tex 2017/08/28 v5.8b chemmacros module `base' 2017/08/28 basic chemmacros module (/usr/share/texlive/texmf-dist/tex/latex/etoolbox/etoolbox.sty Package: etoolbox 2018/02/11 v2.5e e-TeX tools for LaTeX (JAW) \etb@tempcnta=\count337 ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemDeprecated with sig. 'mm' on line 53. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemMacroset with sig. 'smmm' on line 151. ................................................. (/usr/share/texlive/texmf-dist/tex/latex/koma-script/scrlfile.sty Package: scrlfile 2017/09/07 v3.24 KOMA-Script package (loading files) ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemCleverefSupport with sig. 'mmomo' on line 356. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemFancyrefSupport with sig. 'mmo' on line 356. ................................................. (/usr/share/texlive/texmf-dist/tex/latex/tools/bm.sty Package: bm 2017/01/16 v1.2c Bold Symbol Support (DPC/FMi) \symboldoperators=\mathgroup8 \symboldletters=\mathgroup9 \symboldotherletters=\mathgroup10 LaTeX Font Info: Redeclaring math alphabet \mathbf on input line 141. LaTeX Info: Redefining \bm on input line 207. ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chemsetup with sig. 'om' on line 428. ................................................. (chemmacros) Loading module `errorcheck'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.errorchec k.code.tex File: chemmacros.module.errorcheck.code.tex 2017/08/28 v5.8b chemmacros module `errorcheck' 2016/10/05 error checking for unloaded modules )) (chemmacros) Loading module `lang'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.lang.code .tex File: chemmacros.module.lang.code.tex 2017/08/28 v5.8b chemmacros module `lang' 2016/05/31 language settings for chemmacros (/usr/share/texlive/texmf-dist/tex/latex/translations/translations.sty Package: translations 2017/08/31 v1.7a internationalization of LaTeX2e packages (CN) ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemTranslate with sig. 'm' on line 68. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemTranslations with sig. 'mm' on line 140. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemTranslation with sig. 'mmm' on line 144. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ForAllChemTranslationsDo with sig. '+m' on line 162. ................................................. ) (chemmacros) Loading module `greek'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.greek.cod e.tex File: chemmacros.module.greek.code.tex 2017/08/28 v5.8b chemmacros module `gree k' 2015/06/09 upright greek symbols (/usr/share/texlive/texmf-dist/tex/latex/chemgreek/chemgreek.sty Package: chemgreek 2016/12/20 v1.1 interfaceforuprightgreeklettersforuseinchemi stry (CN) \l__chemgreek_tmpa_int=\count338 \g__chemgreek_tmpa_int=\count339 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \newchemgreekmapping with sig. 'O{}mm' on line 336. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \renewchemgreekmapping with sig. 'O{}mm' on line 339. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \declarechemgreekmapping with sig. 'O{}mm' on line 342. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \newchemgreekmappingalias with sig. 'mm' on line 347. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \renewchemgreekmappingalias with sig. 'mm' on line 350. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \declarechemgreekmappingalias with sig. 'mm' on line 353. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \changechemgreeksymbol with sig. 'mmmm' on line 383. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chemgreekmappingsymbol with sig. 'mm' on line 477. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \activatechemgreekmapping with sig. 'sm' on line 486. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \selectchemgreekmapping with sig. 'm' on line 491. ................................................. )) (chemmacros) Loading module `chemformula'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.chemformu la.code.tex File: chemmacros.module.chemformula.code.tex 2017/08/28 v5.8b chemmacros module `chemformula' 2016/05/03 integration of chemical formulas (chemmacros) Loading module `charges'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.charges.c ode.tex File: chemmacros.module.charges.code.tex 2017/08/28 v5.8b chemmacros module `ch arges' 2015/07/30 charges ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \mch with sig. 'o' on line 146. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pch with sig. 'o' on line 147. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \fmch with sig. 'o' on line 148. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \fpch with sig. 'o' on line 149. ................................................. )) (chemmacros) Loading module `acid-base'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.acid-base .code.tex File: chemmacros.module.acid-base.code.tex 2017/08/28 v5.8b chemmacros module ` acid-base' 2016/05/31 acid/base ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \p with sig. 'm' on line 119. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pH with sig. '' on line 120. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pOH with sig. '' on line 121. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pKa with sig. 'o' on line 130. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pKb with sig. 'o' on line 139. ................................................. ) (chemmacros) Loading module `symbols'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.symbols.c ode.tex File: chemmacros.module.symbols.code.tex 2017/08/28 v5.8b chemmacros module `sy mbols' 2015/06/09 symbols ................................................. . LaTeX info: "xparse/define-command" . . Defining command \standardstate with sig. '' on line 67. ................................................. ) (chemmacros) Loading module `particles'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.particles .code.tex File: chemmacros.module.particles.code.tex 2017/08/28 v5.8b chemmacros module ` particles' 2016/04/02 particles ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemParticle with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemParticle with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemParticle with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemParticle with sig. 'mm' on line 45. ................................................. \l__chemmacros_nucleophile_dim=\dimen308 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemNucleophile with sig. 'mm' on line 111. ................................................. ................................................. . 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'o' on line 131. ................................................. ) (chemmacros) Loading module `phases'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.phases.co de.tex File: chemmacros.module.phases.code.tex 2017/08/28 v5.8b chemmacros module `pha ses' 2016/05/31 phase descriptors \l__chemmacros_phases_space_dim=\dimen309 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemPhase with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemPhase with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemPhase with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemPhase with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \phase with sig. 'm' on line 93. ................................................. ................................................. . LaTeX info: "xparse/redefine-command" . . Redefining command \sld with sig. 'o' on line 95. ................................................. ................................................. . LaTeX info: "xparse/redefine-command" . . Redefining command \lqd with sig. 'o' on line 96. ................................................. ................................................. . LaTeX info: "xparse/redefine-command" . . Redefining command \gas with sig. 'o' on line 97. ................................................. ................................................. . LaTeX info: "xparse/redefine-command" . . Redefining command \aq with sig. 'o' on line 98. ................................................. ) (chemmacros) Loading module `nomenclature'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.nomenclat ure.code.tex File: chemmacros.module.nomenclature.code.tex 2017/08/28 v5.8b chemmacros modul e `nomenclature' 2017/06/11 chemical names (chemmacros) Loading module `tikz'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.tikz.code .tex File: chemmacros.module.tikz.code.tex 2017/08/28 v5.8b chemmacros module `tikz' 2015/10/26 upright greek symbols (/usr/share/texlive/texmf-dist/tex/generic/pgf/frontendlayer/tikz/libraries/tik zlibrarycalc.code.tex File: tikzlibrarycalc.code.tex 2013/07/15 v3.0.1a (rcs-revision 1.9) ) (/usr/share/texlive/texmf-dist/tex/generic/pgf/frontendlayer/tikz/libraries/tik zlibrarydecorations.pathmorphing.code.tex (/usr/share/texlive/texmf-dist/tex/generic/pgf/frontendlayer/tikz/libraries/tik zlibrarydecorations.code.tex (/usr/share/texlive/texmf-dist/tex/generic/pgf/modules/pgfmoduledecorations.cod e.tex \pgfdecoratedcompleteddistance=\dimen310 \pgfdecoratedremainingdistance=\dimen311 \pgfdecoratedinputsegmentcompleteddistance=\dimen312 \pgfdecoratedinputsegmentremainingdistance=\dimen313 \pgf@decorate@distancetomove=\dimen314 \pgf@decorate@repeatstate=\count340 \pgfdecorationsegmentamplitude=\dimen315 \pgfdecorationsegmentlength=\dimen316 ) \tikz@lib@dec@box=\box73 ) (/usr/share/texlive/texmf-dist/tex/generic/pgf/libraries/decorations/pgflibrary decorations.pathmorphing.code.tex)) \l__chemmacros_el_length_dim=\dimen317 ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemIUPAC with sig. 'mm' on line 209. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemIUPAC with sig. 'mm' on line 212. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemIUPAC with sig. 'mm' on line 215. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemIUPAC with sig. 'mm' on line 218. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \LetChemIUPAC with sig. 'mm' on line 221. ................................................. \l__chemmacros_cip_kern_dim=\dimen318 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \Sconf with sig. 'O{S}' on line 349. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \Rconf with sig. 'O{R}' on line 350. ................................................. \l__chemmacros_iupac_hyphen_pre_dim=\dimen319 \l__chemmacros_iupac_hyphen_post_dim=\dimen320 \l__chemmacros_iupac_break_dim=\dimen321 \l__chemmacros_iupac_break_skip=\skip102 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemIUPACShorthand with sig. 'mm' on line 604. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . 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'O{}m' on line 673. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemLatin with sig. 'mm' on line 755. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemLatin with sig. 'mm' on line 755. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemLatin with sig. 'mm' on line 755. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemLatin with sig. 'mm' on line 755. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \latin with sig. 'O{}m' on line 826. ................................................. )))) ................................................. . chemmacros info: "default-formula-method" . . You haven't chosen a formula method so I'm assuming the default method . `chemformula'. ................................................. 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Declaring object type 'xfrac' taking 3 argument(s) on line 80. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \sfrac with sig. 'omom' on line 420. ................................................. ) (/usr/share/texlive/texmf-dist/tex/latex/units/nicefrac.sty Package: nicefrac 1998/08/04 v0.9b Nice fractions \L@UnitsRaiseDisplaystyle=\skip104 \L@UnitsRaiseTextstyle=\skip105 \L@UnitsRaiseScriptstyle=\skip106 ) (/usr/share/texlive/texmf-dist/tex/generic/pgf/libraries/pgflibraryarrows.meta. code.tex File: pgflibraryarrows.meta.code.tex 2015/05/13 v3.0.1a (rcs-revision 1.13) \pgfarrowinset=\dimen328 \pgfarrowlength=\dimen329 \pgfarrowwidth=\dimen330 \pgfarrowlinewidth=\dimen331 ) Package: chemformula 2017/03/23 v4.15e typeset chemical compounds and reactions (CN) \l__chemformula_tmpa_dim=\dimen332 \l__chemformula_tmpb_dim=\dimen333 \l__chemformula_tmpc_dim=\dimen334 \l__chemformula_tmpa_int=\count342 \l__chemformula_tmpb_int=\count343 \l__chemformula_tmpc_int=\count344 \l__chemformula_tmpa_box=\box76 \l__chemformula_tmpb_box=\box77 \l__chemformula_arrow_length_dim=\dimen335 \l__chemformula_arrow_label_height_dim=\dimen336 \l__chemformula_arrow_label_offset_dim=\dimen337 \l__chemformula_arrow_minimum_length_dim=\dimen338 \l__chemformula_arrow_shortage_dim=\dimen339 \l__chemformula_arrow_offset_dim=\dimen340 \l__chemformula_arrow_yshift_dim=\dimen341 \l__chemformula_radical_radius_dim=\dimen342 \l__chemformula_radical_hshift_dim=\dimen343 \l__chemformula_radical_vshift_dim=\dimen344 \l__chemformula_radical_space_dim=\dimen345 \l__chemformula_arrow_head_dim=\dimen346 \l__chemformula_name_dim=\dimen347 \l__chemformula_adduct_space_dim=\dimen348 \l__chemformula_charge_shift_dim=\dimen349 \l__chemformula_subscript_shift_dim=\dimen350 \l__chemformula_superscript_shift_dim=\dimen351 \l__chemformula_subscript_dim=\dimen352 \l__chemformula_superscript_dim=\dimen353 \l__chemformula_bond_dim=\dimen354 \l__chemformula_bond_space_dim=\dimen355 \l__chemformula_elspec_pair_distance_dim=\dimen356 \l__chemformula_elspec_pair_line_length_dim=\dimen357 \l__chemformula_elspec_pair_width_dim=\dimen358 \l__chemformula_kroegervink_positive_radius_dim=\dimen359 \l__chemformula_kroegervink_positive_hshift_dim=\dimen360 \l__chemformula_kroegervink_positive_vshift_dim=\dimen361 \l__chemformula_kroegervink_positive_space_dim=\dimen362 \l__chemformula_stoich_space_skip=\skip107 \l__chemformula_math_space_skip=\skip108 \l__chemformula_count_tokens_int=\count345 \g__chemformula_lewis_int=\count346 \l__chemformula_arrow_arg_i_box=\box78 \l__chemformula_arrow_arg_ii_box=\box79 \l__chemformula_superscript_box=\box80 \l__chemformula_subscript_box=\box81 ................................................. . 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{chapter}{Acknowledgements}{i}{chapter*.1} \contentsline {chapter}{Abstract (English/Fran\IeC {\c c}ais/Deutsch)}{iii}{chapter*.2} \babel@toc {french}{} \babel@toc {english}{} \contentsline {chapter}{\numberline {1}Introduction}{1}{chapter.1} \contentsline {chapter}{Introduction}{1}{chapter.1} \contentsline {section}{\numberline {1.1}About chromatin}{1}{section.1.1} \contentsline {subsection}{\numberline {1.1.1}The chromatin structure}{2}{subsection.1.1.1} \contentsline {subsection}{\numberline {1.1.2}The chromatin is dynamic}{2}{subsection.1.1.2} \contentsline {subsection}{\numberline {1.1.3}About nucleosome positioning}{4}{subsection.1.1.3} \contentsline {section}{\numberline {1.2}About transcription factors}{7}{section.1.2} \contentsline {subsection}{\numberline {1.2.1}TF co-binding}{7}{subsection.1.2.1} \contentsline {section}{\numberline {1.3}Gene regulation in a nutshell}{9}{section.1.3} \contentsline {subsection}{\numberline {1.3.1}The chromatin barrier}{9}{subsection.1.3.1} \contentsline {subsection}{\numberline {1.3.2}TFs cooperative binding}{9}{subsection.1.3.2} \contentsline {subsection}{\numberline {1.3.3}Pioneer TFs}{10}{subsection.1.3.3} \contentsline {subsection}{\numberline {1.3.4}Regulatory elements}{10}{subsection.1.3.4} \contentsline {subsection}{\numberline {1.3.5}The genome goes 3D}{11}{subsection.1.3.5} \contentsline {section}{\numberline {1.4}Measuring chromatin features}{12}{section.1.4} \contentsline {subsection}{\numberline {1.4.1}Measuring TF binding in vivo}{12}{subsection.1.4.1} \contentsline {subsection}{\numberline {1.4.2}Measuring TF binding in vitro}{13}{subsection.1.4.2} \contentsline {subsection}{\numberline {1.4.3}Measuring nucleosome occupancy}{14}{subsection.1.4.3} \contentsline {subsection}{\numberline {1.4.4}Digital footprinting}{15}{subsection.1.4.4} \contentsline {section}{\numberline {1.5}Modeling sequence specificity}{17}{section.1.5} \contentsline {subsubsection}{The physics approach to PWMs}{17}{section.1.5} \contentsline {subsubsection}{The statistical mechanic approach to PWMs}{18}{equation.1.5.2} \contentsline {subsection}{\numberline {1.5.1}Aligning binding sites}{19}{subsection.1.5.1} \contentsline {subsection}{\numberline {1.5.2}Platitudes}{20}{subsection.1.5.2} \contentsline {subsection}{\numberline {1.5.3}Predicting binding sites}{20}{subsection.1.5.3} \contentsline {section}{\numberline {1.6}Over-represented patterns discovery}{21}{section.1.6} \contentsline {chapter}{\numberline {2}Laboratory resources}{25}{chapter.2} \contentsline {chapter}{Laboratory resources}{25}{chapter.2} \contentsline {section}{\numberline {2.1}Mass Genome Annotation repository}{25}{section.2.1} \contentsline {subsection}{\numberline {2.1.1}MGA content and organization}{26}{subsection.2.1.1} \contentsline {subsection}{\numberline {2.1.2}Conclusions}{27}{subsection.2.1.2} \contentsline {section}{\numberline {2.2}Eukaryotic Promoter Database}{28}{section.2.2} \contentsline {subsection}{\numberline {2.2.1}EPDnew now annotates (some of) your mushrooms and vegetables}{29}{subsection.2.2.1} \contentsline {subsection}{\numberline {2.2.2}Increased mapping precision in human}{30}{subsection.2.2.2} \contentsline {subsection}{\numberline {2.2.3}Integration of EPDnew with other resources}{30}{subsection.2.2.3} \contentsline {subsection}{\numberline {2.2.4}Conclusions}{31}{subsection.2.2.4} \contentsline {subsection}{\numberline {2.2.5}Methods}{31}{subsection.2.2.5} \contentsline {subsubsection}{Motif occurrence profiles}{31}{subsection.2.2.5} \contentsline {chapter}{\numberline {3}ENCODE peaks analysis}{33}{chapter.3} \contentsline {chapter}{ENCODE peaks analysis}{33}{chapter.3} \contentsline {section}{\numberline {3.1}Data}{33}{section.3.1} \contentsline {section}{\numberline {3.2}ChIPPartitioning : an algorithm to identify chromatin architectures}{35}{section.3.2} \contentsline {subsection}{\numberline {3.2.1}Data realignment}{36}{subsection.3.2.1} \contentsline {section}{\numberline {3.3}Nucleosome organization around transcription factor binding sites}{37}{section.3.3} \contentsline {section}{\numberline {3.4}The case of CTCF, RAD21, SMC3, YY1 and ZNF143}{42}{section.3.4} \contentsline {section}{\numberline {3.5}CTCF and JunD interactomes}{43}{section.3.5} \contentsline {section}{\numberline {3.6}EBF1 binds nucleosomes}{47}{section.3.6} \contentsline {section}{\numberline {3.7}Discussion}{50}{section.3.7} \contentsline {section}{\numberline {3.8}Methods}{50}{section.3.8} \contentsline {subsection}{\numberline {3.8.1}Data and data processing}{50}{subsection.3.8.1} \contentsline {subsection}{\numberline {3.8.2}Classification of MNase patterns}{51}{subsection.3.8.2} \contentsline {subsection}{\numberline {3.8.3}Quantifying nucleosome array intensity from classification results}{52}{subsection.3.8.3} \contentsline {subsection}{\numberline {3.8.4}Peak colocalization}{53}{subsection.3.8.4} \contentsline {subsection}{\numberline {3.8.5}NDR detection}{54}{subsection.3.8.5} \contentsline {subsection}{\numberline {3.8.6}CTCF and JunD interactors}{56}{subsection.3.8.6} \contentsline {subsection}{\numberline {3.8.7}EBF1 and nucleosome}{57}{subsection.3.8.7} \contentsline {chapter}{\numberline {4}SPar-K}{59}{chapter.4} \contentsline {section}{\numberline {4.1}Algorithm}{59}{section.4.1} \contentsline {section}{\numberline {4.2}Implementation}{60}{section.4.2} \contentsline {section}{\numberline {4.3}Benchmarking}{64}{section.4.3} \contentsline {subsection}{\numberline {4.3.1}K-means}{64}{subsection.4.3.1} \contentsline {subsection}{\numberline {4.3.2}ChIPPartitioning}{64}{subsection.4.3.2} \contentsline {subsection}{\numberline {4.3.3}Data}{64}{subsection.4.3.3} \contentsline {subsection}{\numberline {4.3.4}Performances}{65}{subsection.4.3.4} \contentsline {section}{\numberline {4.4}Partition of DNase and MNase data}{65}{section.4.4} \contentsline {section}{\numberline {4.5}Conclusions}{68}{section.4.5} \contentsline {chapter}{\numberline {5}SMiLE-seq data analysis}{69}{chapter.5} \contentsline {chapter}{SMiLE-seq data analysis}{69}{chapter.5} \contentsline {section}{\numberline {5.1}Introduction}{69}{section.5.1} \contentsline {section}{\numberline {5.2}Hidden Markov Model Motif discovery}{71}{section.5.2} \contentsline {section}{\numberline {5.3}Binding motif evaluation}{72}{section.5.3} \contentsline {section}{\numberline {5.4}Results}{73}{section.5.4} \contentsline {section}{\numberline {5.5}Conclusions}{75}{section.5.5} \contentsline {chapter}{\numberline {6}PWMScan}{77}{chapter.6} \contentsline {section}{\numberline {6.1}Algorithms}{77}{section.6.1} \contentsline {subsection}{\numberline {6.1.1}Scanner algorithm}{78}{subsection.6.1.1} \contentsline {subsection}{\numberline {6.1.2}Matches enumeration and mapping}{78}{subsection.6.1.2} \contentsline {section}{\numberline {6.2}PMWScan architecture}{79}{section.6.2} \contentsline {section}{\numberline {6.3}Benchmark}{81}{section.6.3} \contentsline {section}{\numberline {6.4}Conclusions}{83}{section.6.4} \contentsline {chapter}{\numberline {7}Chromatin accessibility of monocytes}{85}{chapter.7} \contentsline {section}{\numberline {7.1}Monitoring TF binding}{85}{section.7.1} \contentsline {section}{\numberline {7.2}The advent of single cell DGF}{86}{section.7.2} \contentsline {section}{\numberline {7.3}Open issues}{86}{section.7.3} \contentsline {section}{\numberline {7.4}Data}{86}{section.7.4} \contentsline {section}{\numberline {7.5}Identifying over-represented signals}{87}{section.7.5} \contentsline {subsection}{\numberline {7.5.1}ChIPPartitioning algorithm}{87}{subsection.7.5.1} \contentsline {subsection}{\numberline {7.5.2}EMSequence algorithm}{87}{subsection.7.5.2} \contentsline {subsubsection}{without shift and flip}{89}{figure.caption.35} \contentsline {subsubsection}{with shift and flip}{89}{equation.7.5.2} \contentsline {subsection}{\numberline {7.5.3}EMJoint algorithm}{91}{subsection.7.5.3} \contentsline {subsection}{\numberline {7.5.4}Data realignment}{92}{subsection.7.5.4} \contentsline {subsection}{\numberline {7.5.5}Soft aggregation plots}{92}{subsection.7.5.5} \contentsline {section}{\numberline {7.6}Data processing}{93}{section.7.6} \contentsline {section}{\numberline {7.7}Results}{93}{section.7.7} \contentsline {subsection}{\numberline {7.7.1}Aligning the binding sites}{93}{subsection.7.7.1} \contentsline {subsection}{\numberline {7.7.2}Exploring individual TF classes}{95}{subsection.7.7.2} \contentsline {section}{\numberline {7.8}Discussions}{97}{section.7.8} \contentsline {section}{\numberline {7.9}Perspectives}{97}{section.7.9} \contentsline {section}{\numberline {7.10}Methods}{98}{section.7.10} \contentsline {subsection}{\numberline {7.10.1}Code availability}{98}{subsection.7.10.1} \contentsline {subsection}{\numberline {7.10.2}Data sources}{99}{subsection.7.10.2} \contentsline {subsection}{\numberline {7.10.3}Data post-processing}{99}{subsection.7.10.3} \contentsline {subsection}{\numberline {7.10.4}Model extension}{100}{subsection.7.10.4} \contentsline {subsection}{\numberline {7.10.5}Extracting data assigned to a class}{100}{subsection.7.10.5} \contentsline {subsection}{\numberline {7.10.6}Programs}{103}{subsection.7.10.6} \contentsline {subsection}{\numberline {7.10.7}Fragment classes}{104}{subsection.7.10.7} \contentsline {subsection}{\numberline {7.10.8}Simulated sequences}{105}{subsection.7.10.8} \contentsline {subsection}{\numberline {7.10.9}Binding site prediction}{105}{subsection.7.10.9} \contentsline {subsection}{\numberline {7.10.10}Realignment using JASPAR motifs}{106}{subsection.7.10.10} \contentsline {subsection}{\numberline {7.10.11}Per TF sub-classes}{108}{subsection.7.10.11} \contentsline {chapter}{\numberline {8}Discussion}{111}{chapter.8} -\contentsline {chapter}{Discussions}{111}{chapter.8} +\contentsline {chapter}{Discussion}{111}{chapter.8} \vspace {\normalbaselineskip } \contentsline {chapter}{\numberline {A}Supplementary material}{115}{appendix.A} \contentsline {section}{\numberline {A.1}ENCODE peaks analysis supplementary material}{116}{section.A.1} \contentsline {section}{\numberline {A.2}SPar-K supplementary material}{126}{section.A.2} \contentsline {section}{\numberline {A.3}SMiLE-seq supplementary material}{139}{section.A.3} \contentsline {section}{\numberline {A.4}Chromatin accessibility of monocytes supplementary material}{139}{section.A.4} \contentsline {subsection}{\numberline {A.4.1}Fragment size analysis}{139}{subsection.A.4.1} \contentsline {subsection}{\numberline {A.4.2}Measuring open chromatin and nucleosome occupancy}{140}{subsection.A.4.2} \contentsline {subsection}{\numberline {A.4.3}Evaluation of EMSequence and ChIPPartitioning}{143}{subsection.A.4.3} \contentsline {subsubsection}{EMSequence}{143}{subsection.A.4.3} \contentsline {subsubsection}{ChIPPartitioning}{146}{figure.caption.56} \contentsline {subsection}{\numberline {A.4.4}Other supplementary figures}{149}{subsection.A.4.4} 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