diff --git a/head/abstracts.aux b/head/abstracts.aux index e147d55..79b87f8 100644 --- a/head/abstracts.aux +++ b/head/abstracts.aux @@ -1,53 +1,51 @@ \relax \providecommand\hyper@newdestlabel[2]{} -\@writefile{toc}{\contentsline {chapter}{Abstract (English/Fran\IeC {\c c}ais/Deutsch)}{v}{chapter*.3}} -\babel@aux{german}{} -\babel@aux{english}{} +\@writefile{toc}{\contentsline {chapter}{Abstract (English/Fran\IeC {\c c}ais/Deutsch)}{iii}{chapter*.2}} \babel@aux{french}{} \babel@aux{english}{} \@setckpt{head/abstracts}{ -\setcounter{page}{10} +\setcounter{page}{6} \setcounter{equation}{0} \setcounter{enumi}{0} \setcounter{enumii}{0} \setcounter{enumiii}{0} \setcounter{enumiv}{0} \setcounter{footnote}{0} \setcounter{mpfootnote}{0} \setcounter{part}{0} \setcounter{chapter}{0} \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}{2} \setcounter{lstnumber}{1} \setcounter{Item}{0} \setcounter{Hfootnote}{0} \setcounter{bookmark@seq@number}{0} \setcounter{AM@survey}{0} \setcounter{ttlp@side}{0} \setcounter{myparts}{0} \setcounter{parentequation}{0} \setcounter{AlgoLine}{0} \setcounter{algocfline}{0} \setcounter{algocfproc}{0} \setcounter{algocf}{0} \setcounter{float@type}{8} \setcounter{nlinenum}{0} \setcounter{lstlisting}{0} \setcounter{section@level}{0} } diff --git a/head/abstracts.tex b/head/abstracts.tex index 098a1f5..19e59e1 100644 --- a/head/abstracts.tex +++ b/head/abstracts.tex @@ -1,44 +1,55 @@ %\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 -Eukaryotic organisms face two crucial problems : expressing the correct genes at the right time and fiting their DNA inside their nuclei. Millions of years of evolution and a pinch of selection later, the chromatin offered an elegant solution to both problems. +Any living organism contains a whole set of instructions encoded as genes on the DNA. This set of instructions contains all the needed 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. -In human +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. -The regulation of gene expression is a crucial biological process. +In the nuclei, histones are assembled into octamers around which ~148bp of DNA are wrapped. 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 nuclei. However, this come at the cost of rendering the DNA sequence inaccessible to DNA readers, such as the transcriptional machinery 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 TF with a wide sequence specificity. Eventually, this allows the cell to recruit TFs at dedicated locations in the genome called regulatory elements (RE). +The action of TF at RE is crucial to gene expression. Indeed, TFs are involved in many processes such the opening of the chromatin structure or the recruitment of the transcriptional machinery. However if TFs can influence the chromatin structure, the reverse is also true as histones impede TFs binding on DNA. Thus the regulation of gene relies on a subtle and complex interaction between the chromatin and TFs. +To better understand how TF 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 TFs to bind together. The results suggest that regular nucleosome arrays are found near all TFs. It also points out two special cases. When CTCF binds with the cohesin complex, they seem to drive the nucleosome organization, which is a unique feature among all TFs investigates. Additionally I present evidences suggesting that EBF1 is a pioneer factor - a special class of TFs able to bind nucleosome. + +Second, I developed several unsupervised 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. + +Third, 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. + +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 remaining as specific and sensitive as 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} % put your text here \lipsum[1-2] \end{otherlanguage} %\endgroup %\vfill diff --git a/head/acknowledgements.aux b/head/acknowledgements.aux index 889be80..3f96ec6 100644 --- a/head/acknowledgements.aux +++ b/head/acknowledgements.aux @@ -1,49 +1,49 @@ \relax \providecommand\hyper@newdestlabel[2]{} \@writefile{toc}{\contentsline {chapter}{Acknowledgements}{i}{chapter*.1}} \@setckpt{head/acknowledgements}{ \setcounter{page}{2} \setcounter{equation}{0} \setcounter{enumi}{0} \setcounter{enumii}{0} \setcounter{enumiii}{0} \setcounter{enumiv}{0} \setcounter{footnote}{0} \setcounter{mpfootnote}{0} \setcounter{part}{0} \setcounter{chapter}{0} \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}{2} +\setcounter{lips@count}{0} \setcounter{lstnumber}{1} \setcounter{Item}{0} \setcounter{Hfootnote}{0} \setcounter{bookmark@seq@number}{0} \setcounter{AM@survey}{0} \setcounter{ttlp@side}{0} \setcounter{myparts}{0} \setcounter{parentequation}{0} \setcounter{AlgoLine}{0} \setcounter{algocfline}{0} \setcounter{algocfproc}{0} \setcounter{algocf}{0} \setcounter{float@type}{8} \setcounter{nlinenum}{0} \setcounter{lstlisting}{0} \setcounter{section@level}{0} } diff --git a/head/titlepage.tex b/head/titlepage.tex index eaad766..80a9d03 100644 --- a/head/titlepage.tex +++ b/head/titlepage.tex @@ -1,47 +1,48 @@ \begin{titlepage} \begin{otherlanguage}{french} \begin{center} %\large \sffamily \null\vspace{2cm} {\huge Computational study of transcription factor binding sites \\[12pt] SECOND LINE OF TITLE} \\[24pt] \textcolor{gray}{\small{THIS IS A TEMPORARY TITLE PAGE \\ It will be replaced for the final print by a version \\ provided by the registrar's office.}} \vfill \begin{tabular} {cc} \parbox{0.3\textwidth}{\includegraphics[width=4cm]{images/epfl}} & \parbox{0.7\textwidth}{% Thèse n. 1234 \the\year\\ présentée le \today\\ - à la Faculté des sciences de base\\ - laboratoire SuperScience\\ - programme doctoral en SuperScience\\ + à la Faculté des sciences de la vie\\ + laboratoire Computational Cancer Genomics\\ + programme doctoral en biologie computationnelle et quantitative\\ % % ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE\\ École polytechnique fédérale de Lausanne\\[6pt] pour l'obtention du grade de Docteur ès Sciences\\ par\\ [4pt] \null \hspace{3em} Paolino Paperino\\[9pt] % \small acceptée sur proposition du jury:\\[4pt] % - Prof Name Surname, président du jury\\ - Prof Name Surname, directeur de thèse\\ - Prof Name Surname, rapporteur\\ - Prof Name Surname, rapporteur\\ - Prof Name Surname, rapporteur\\[12pt] + Prof Radenovic Aleksandra, président du jury\\ + Prof Deplancke Bart, directeur de thèse\\ + Dr Bucher Philipp, directeur de thèse\\ + Prof Suter David, rapporteur\\ + Dr Guex Nicolas, rapporteur\\ + Dr Kulakovskiy Ivan, rapporteur\\[12pt] % Lausanne, EPFL, \the\year} \end{tabular} \end{center} \vspace{2cm} \end{otherlanguage} \end{titlepage} diff --git a/main/ch_atac-seq.aux b/main/ch_atac-seq.aux index 8a5676a..4a29e7e 100644 --- a/main/ch_atac-seq.aux +++ b/main/ch_atac-seq.aux @@ -1,142 +1,142 @@ \relax \providecommand\hyper@newdestlabel[2]{} \citation{neph_expansive_2012} \citation{berest_quantification_2018} \citation{grossman_positional_2018} \@writefile{toc}{\contentsline {chapter}{\numberline {7}Chromatin accessibility of monocytes}{83}{chapter.7}} \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} \@writefile{loa}{\addvspace {10\p@ }} \newlabel{atac_seq}{{7}{83}{Chromatin accessibility of monocytes}{chapter.7}{}} \@writefile{chapter}{\contentsline {toc}{Chromatin accessibility of monocytes}{83}{chapter.7}} \@writefile{toc}{\contentsline {section}{\numberline {7.1}Monitoring TF binding}{83}{section.7.1}} \citation{angerer_single_2017} \@writefile{toc}{\contentsline {section}{\numberline {7.2}The advent of single cell DGF}{84}{section.7.2}} \@writefile{toc}{\contentsline {section}{\numberline {7.3}Open issues}{84}{section.7.3}} \@writefile{toc}{\contentsline {section}{\numberline {7.4}Data}{84}{section.7.4}} \citation{hon_chromasig:_2008} \citation{nielsen_catchprofiles:_2012} \citation{kundaje_ubiquitous_2012} \citation{nair_probabilistic_2014} \citation{groux_spar-k:_2019} \citation{nair_probabilistic_2014} \citation{nair_probabilistic_2014} \citation{nair_probabilistic_2014} \citation{lawrence_expectation_1990} \citation{bailey_fitting_1994} \@writefile{toc}{\contentsline {section}{\numberline {7.5}Identifying over-represented signals}{85}{section.7.5}} \@writefile{toc}{\contentsline {subsection}{\numberline {7.5.1}ChIPPartitioning algorithm}{85}{subsection.7.5.1}} \@writefile{toc}{\contentsline {subsection}{\numberline {7.5.2}EMSequence algorithm}{85}{subsection.7.5.2}} -\@writefile{lof}{\contentsline {figure}{\numberline {7.1}{\ignorespaces \textbf {Illustration of the expectation-maximization algorithms} \textbf {A} illustration of ChIPPartitioning, an algorithm dedicated to the discovery of over-represented chromatin patterns, as described in \citep {nair_probabilistic_2014}. \textbf {B} illustration of EMSequence, an algorithm to discover over-represented DNA motifs. 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The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed lines indicate 20'000 and 40'000.\relax }}{32}{figure.caption.17}} -\newlabel{encode_peaks_gm12878_peak_number}{{3.1}{32}{\textbf {Number of peaks in GM12878} called by ENCODE for each TF ChIP-seq experiment. The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed lines indicate 20'000 and 40'000.\relax }{figure.caption.17}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {3.2}{\ignorespaces \textbf {Proportion of peaks with a motif in GM12878}, for each TF ChIP-seq experiment, in green. Assuming that a TF binds to DNA through its motif, the motif should be nearby the peak center. Thus the center of each peak was scanned using a PWM describing the TF binding specificity. Each TF was associated to a log-odd PWM contained either in JASPAR Core vertebrate 2014 \citep {mathelier_jaspar_2014}, HOCOMOCO v10 \citep {kulakovskiy_hocomoco:_2016} or Jolma \citep {jolma_dna-binding_2013} collection. If a motif instance with a score corresponding to a pvalue higher or equal to $1\cdot 10^{-4}$ could be found, the peak was considered bearing a motif. The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed line indicates 0.5.\relax }}{32}{figure.caption.18}} -\newlabel{encode_peaks_gm12878_motif_prop}{{3.2}{32}{\textbf {Proportion of peaks with a motif in GM12878}, for each TF ChIP-seq experiment, in green. Assuming that a TF binds to DNA through its motif, the motif should be nearby the peak center. Thus the center of each peak was scanned using a PWM describing the TF binding specificity. Each TF was associated to a log-odd PWM contained either in JASPAR Core vertebrate 2014 \citep {mathelier_jaspar_2014}, HOCOMOCO v10 \citep {kulakovskiy_hocomoco:_2016} or Jolma \citep {jolma_dna-binding_2013} collection. If a motif instance with a score corresponding to a pvalue higher or equal to $1\cdot 10^{-4}$ could be found, the peak was considered bearing a motif. The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed line indicates 0.5.\relax }{figure.caption.18}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.1}{\ignorespaces \textbf {Number of peaks in GM12878} called by ENCODE for each TF ChIP-seq experiment. The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed lines indicate 20'000 and 40'000.\relax }}{32}{figure.caption.15}} +\newlabel{encode_peaks_gm12878_peak_number}{{3.1}{32}{\textbf {Number of peaks in GM12878} called by ENCODE for each TF ChIP-seq experiment. The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed lines indicate 20'000 and 40'000.\relax }{figure.caption.15}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.2}{\ignorespaces \textbf {Proportion of peaks with a motif in GM12878}, for each TF ChIP-seq experiment, in green. Assuming that a TF binds to DNA through its motif, the motif should be nearby the peak center. Thus the center of each peak was scanned using a PWM describing the TF binding specificity. Each TF was associated to a log-odd PWM contained either in JASPAR Core vertebrate 2014 \citep {mathelier_jaspar_2014}, HOCOMOCO v10 \citep {kulakovskiy_hocomoco:_2016} or Jolma \citep {jolma_dna-binding_2013} collection. If a motif instance with a score corresponding to a pvalue higher or equal to $1\cdot 10^{-4}$ could be found, the peak was considered bearing a motif. The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed line indicates 0.5.\relax }}{32}{figure.caption.16}} +\newlabel{encode_peaks_gm12878_motif_prop}{{3.2}{32}{\textbf {Proportion of peaks with a motif in GM12878}, for each TF ChIP-seq experiment, in green. Assuming that a TF binds to DNA through its motif, the motif should be nearby the peak center. Thus the center of each peak was scanned using a PWM describing the TF binding specificity. Each TF was associated to a log-odd PWM contained either in JASPAR Core vertebrate 2014 \citep {mathelier_jaspar_2014}, HOCOMOCO v10 \citep {kulakovskiy_hocomoco:_2016} or Jolma \citep {jolma_dna-binding_2013} collection. If a motif instance with a score corresponding to a pvalue higher or equal to $1\cdot 10^{-4}$ could be found, the peak was considered bearing a motif. The different TFs are colored by type, as defined by \citep {cheng_understanding_2012} : sequence specific TF (TFSS), chromatin structure (ChromStr) and others. The horizontal dashed line indicates 0.5.\relax }{figure.caption.16}{}} \citation{wu_biogps:_2016} \citation{nair_probabilistic_2014} \@writefile{toc}{\contentsline {section}{\numberline {3.2}ChIPPartitioning : an algorithm to identify chromatin architectures}{33}{section.3.2}} \newlabel{encode_peaks_chippartitioning}{{3.2}{33}{ChIPPartitioning : an algorithm to identify chromatin architectures}{section.3.2}{}} \newlabel{encode_peaks_eq_em_data_model}{{3.1}{33}{ChIPPartitioning : an algorithm to identify chromatin architectures}{equation.3.2.1}{}} \citation{bailey_fitting_1994} \citation{nair_probabilistic_2014} \newlabel{encode_peaks_eq_em_update}{{3.2}{34}{ChIPPartitioning : an algorithm to identify chromatin architectures}{equation.3.2.2}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.2.1}Data realignment}{34}{subsection.3.2.1}} \newlabel{encode_peaks_data_realign}{{3.2.1}{34}{Data realignment}{subsection.3.2.1}{}} \citation{kundaje_ubiquitous_2012} \citation{zhang_canonical_2014} \@writefile{toc}{\contentsline {section}{\numberline {3.3}Nucleosome organization around transcription factor binding sites}{35}{section.3.3}} -\@writefile{lof}{\contentsline {figure}{\numberline {3.3}{\ignorespaces \textbf {Chromatin pattern around TF binding sites in GM12878 :} \textbf {A} For each peaklist, nucleosome occupancy was measured +/- 1kb around each individual TFBS using 10bp bins. The TFBS were then classified into 4 classes according to their nucleosome patterns using a ChIPPartitioning, allowing the patterns to be flipped and shifted. Each TF binding site was assigned a probability to belong to each of the 4 classes with a given values of shift and flip. To assess the extent of a given TF to i) display nucleosomes arrays on its flank and ii) to have nucleosome positioned with respect to its binding sites, array density and shift probability standard deviation have been measured for each class. Classes having a mean array density above 0.4 and a shift probability standard deviation under 3.5 and other custom classes are highlighted. Classes are named using the TF, the laboratory which produced the data and the class number (from 1 to 4). \textbf {B} Examples of class patterns corresponding to some of the highlighted classes for CTCF, ATF3, YY1, EBF1 and ZNF143. MNase profiles (red) were allowed to be shifted and flipped and DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The y-axis scale represent the proportion of the highest signal for each chromatin pattern.\relax }}{36}{figure.caption.19}} -\newlabel{encode_peaks_array_measure}{{3.3}{36}{\textbf {Chromatin pattern around TF binding sites in GM12878 :} \textbf {A} For each peaklist, nucleosome occupancy was measured +/- 1kb around each individual TFBS using 10bp bins. The TFBS were then classified into 4 classes according to their nucleosome patterns using a ChIPPartitioning, allowing the patterns to be flipped and shifted. Each TF binding site was assigned a probability to belong to each of the 4 classes with a given values of shift and flip. To assess the extent of a given TF to i) display nucleosomes arrays on its flank and ii) to have nucleosome positioned with respect to its binding sites, array density and shift probability standard deviation have been measured for each class. Classes having a mean array density above 0.4 and a shift probability standard deviation under 3.5 and other custom classes are highlighted. Classes are named using the TF, the laboratory which produced the data and the class number (from 1 to 4). \textbf {B} Examples of class patterns corresponding to some of the highlighted classes for CTCF, ATF3, YY1, EBF1 and ZNF143. MNase profiles (red) were allowed to be shifted and flipped and DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The y-axis scale represent the proportion of the highest signal for each chromatin pattern.\relax }{figure.caption.19}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.3}{\ignorespaces \textbf {Chromatin pattern around TF binding sites in GM12878 :} \textbf {A} For each peaklist, nucleosome occupancy was measured +/- 1kb around each individual TFBS using 10bp bins. The TFBS were then classified into 4 classes according to their nucleosome patterns using a ChIPPartitioning, allowing the patterns to be flipped and shifted. Each TF binding site was assigned a probability to belong to each of the 4 classes with a given values of shift and flip. To assess the extent of a given TF to i) display nucleosomes arrays on its flank and ii) to have nucleosome positioned with respect to its binding sites, array density and shift probability standard deviation have been measured for each class. Classes having a mean array density above 0.4 and a shift probability standard deviation under 3.5 and other custom classes are highlighted. Classes are named using the TF, the laboratory which produced the data and the class number (from 1 to 4). \textbf {B} Examples of class patterns corresponding to some of the highlighted classes for CTCF, ATF3, YY1, EBF1 and ZNF143. MNase profiles (red) were allowed to be shifted and flipped and DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The y-axis scale represent the proportion of the highest signal for each chromatin pattern.\relax }}{36}{figure.caption.17}} +\newlabel{encode_peaks_array_measure}{{3.3}{36}{\textbf {Chromatin pattern around TF binding sites in GM12878 :} \textbf {A} For each peaklist, nucleosome occupancy was measured +/- 1kb around each individual TFBS using 10bp bins. The TFBS were then classified into 4 classes according to their nucleosome patterns using a ChIPPartitioning, allowing the patterns to be flipped and shifted. Each TF binding site was assigned a probability to belong to each of the 4 classes with a given values of shift and flip. To assess the extent of a given TF to i) display nucleosomes arrays on its flank and ii) to have nucleosome positioned with respect to its binding sites, array density and shift probability standard deviation have been measured for each class. Classes having a mean array density above 0.4 and a shift probability standard deviation under 3.5 and other custom classes are highlighted. Classes are named using the TF, the laboratory which produced the data and the class number (from 1 to 4). \textbf {B} Examples of class patterns corresponding to some of the highlighted classes for CTCF, ATF3, YY1, EBF1 and ZNF143. MNase profiles (red) were allowed to be shifted and flipped and DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The y-axis scale represent the proportion of the highest signal for each chromatin pattern.\relax }{figure.caption.17}{}} \citation{kundaje_ubiquitous_2012,fu_insulator_2008} \@writefile{toc}{\contentsline {section}{\numberline {3.4}The case of CTCF, RAD21, SMC3, YY1 and ZNF143}{37}{section.3.4}} \newlabel{encode_peaks_section_ctcf_rad21_smc3_yy1_znf143}{{3.4}{37}{The case of CTCF, RAD21, SMC3, YY1 and ZNF143}{section.3.4}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {3.4}{\ignorespaces \textbf { Colocalization with CTCF peaks in GM12878 cells : } \textbf {A} Proportion of peaks for different TFs having a CTCF peak within 10bp, 50bp and 100bp. The colours indicate different TFs. The CTCF peaklist used as reference to assess CTCF presence was CTCF.Sydh (in red), the two RAD21 peaklists are RAD21.Haib and RAD21.Sydh respectively (in blue), the SMC3 peaklist is SMC3.Sydh (in green), the YY1 peaklist is YY1.Haib (in orange) and the ZNF143 peaklist is ZNF143.Sydh (in violet). \textbf {B} Venn diagrams showing the proportion of peaks for each TF with i) an instance of its own motif, ii) a CTCF.Sydh peak within 100bp, iii) both or iv) neither of them. RAD21 and SMC3 are not represented as there is no PWM available to describe their sequence specificity. \textbf {C} ChIPPartitioning classification with shift and flip of MNase patterns +/- 1kb of YY1.Haib peaks using 10bp bins. YY1 peaks with (upper row) and without (lower row) a CTCF peak within 100bp. Two classes were used to account for "typical" and "non-typical" looking MNase patterns. DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The number at the upper right corner of each plot indicate the overall class probability. The number of YY1 peaks is slightly smaller than in B) because peaks showing no MNase reads were not included in the classification analysis. Peaklists are named using the TF together with the laboratory which produced the data.\relax }}{38}{figure.caption.20}} -\newlabel{encode_peaks_colocalization_ctcf}{{3.4}{38}{\textbf { Colocalization with CTCF peaks in GM12878 cells : } \textbf {A} Proportion of peaks for different TFs having a CTCF peak within 10bp, 50bp and 100bp. The colours indicate different TFs. The CTCF peaklist used as reference to assess CTCF presence was CTCF.Sydh (in red), the two RAD21 peaklists are RAD21.Haib and RAD21.Sydh respectively (in blue), the SMC3 peaklist is SMC3.Sydh (in green), the YY1 peaklist is YY1.Haib (in orange) and the ZNF143 peaklist is ZNF143.Sydh (in violet). \textbf {B} Venn diagrams showing the proportion of peaks for each TF with i) an instance of its own motif, ii) a CTCF.Sydh peak within 100bp, iii) both or iv) neither of them. RAD21 and SMC3 are not represented as there is no PWM available to describe their sequence specificity. \textbf {C} ChIPPartitioning classification with shift and flip of MNase patterns +/- 1kb of YY1.Haib peaks using 10bp bins. YY1 peaks with (upper row) and without (lower row) a CTCF peak within 100bp. Two classes were used to account for "typical" and "non-typical" looking MNase patterns. DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The number at the upper right corner of each plot indicate the overall class probability. The number of YY1 peaks is slightly smaller than in B) because peaks showing no MNase reads were not included in the classification analysis. Peaklists are named using the TF together with the laboratory which produced the data.\relax }{figure.caption.20}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {3.5}{\ignorespaces \textbf {Nucleosome free region at CTCF binding sites} \textbf {a} The length are represented as boxplots. The CTCF binding sites are divided into subgroups according to additional presence of SCM3, RAD21, YY1 or ZNF143. The number of binding sites in each subgroup is indicated in red above the boxplots. The presence of SMC3 only, RAD21 only and SMC3 and RAD21 together are indicated in violet, blue and orange respectively. \textbf {B} The proportion of peaks (in green), in each subgroup, having a TSS within a 1kb.\relax }}{39}{figure.caption.21}} -\newlabel{encode_peaks_ctcf_ndr}{{3.5}{39}{\textbf {Nucleosome free region at CTCF binding sites} \textbf {a} The length are represented as boxplots. The CTCF binding sites are divided into subgroups according to additional presence of SCM3, RAD21, YY1 or ZNF143. The number of binding sites in each subgroup is indicated in red above the boxplots. The presence of SMC3 only, RAD21 only and SMC3 and RAD21 together are indicated in violet, blue and orange respectively. \textbf {B} The proportion of peaks (in green), in each subgroup, having a TSS within a 1kb.\relax }{figure.caption.21}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.4}{\ignorespaces \textbf { Colocalization with CTCF peaks in GM12878 cells : } \textbf {A} Proportion of peaks for different TFs having a CTCF peak within 10bp, 50bp and 100bp. The colours indicate different TFs. The CTCF peaklist used as reference to assess CTCF presence was CTCF.Sydh (in red), the two RAD21 peaklists are RAD21.Haib and RAD21.Sydh respectively (in blue), the SMC3 peaklist is SMC3.Sydh (in green), the YY1 peaklist is YY1.Haib (in orange) and the ZNF143 peaklist is ZNF143.Sydh (in violet). \textbf {B} Venn diagrams showing the proportion of peaks for each TF with i) an instance of its own motif, ii) a CTCF.Sydh peak within 100bp, iii) both or iv) neither of them. RAD21 and SMC3 are not represented as there is no PWM available to describe their sequence specificity. \textbf {C} ChIPPartitioning classification with shift and flip of MNase patterns +/- 1kb of YY1.Haib peaks using 10bp bins. YY1 peaks with (upper row) and without (lower row) a CTCF peak within 100bp. Two classes were used to account for "typical" and "non-typical" looking MNase patterns. DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The number at the upper right corner of each plot indicate the overall class probability. The number of YY1 peaks is slightly smaller than in B) because peaks showing no MNase reads were not included in the classification analysis. Peaklists are named using the TF together with the laboratory which produced the data.\relax }}{38}{figure.caption.18}} +\newlabel{encode_peaks_colocalization_ctcf}{{3.4}{38}{\textbf { Colocalization with CTCF peaks in GM12878 cells : } \textbf {A} Proportion of peaks for different TFs having a CTCF peak within 10bp, 50bp and 100bp. The colours indicate different TFs. The CTCF peaklist used as reference to assess CTCF presence was CTCF.Sydh (in red), the two RAD21 peaklists are RAD21.Haib and RAD21.Sydh respectively (in blue), the SMC3 peaklist is SMC3.Sydh (in green), the YY1 peaklist is YY1.Haib (in orange) and the ZNF143 peaklist is ZNF143.Sydh (in violet). \textbf {B} Venn diagrams showing the proportion of peaks for each TF with i) an instance of its own motif, ii) a CTCF.Sydh peak within 100bp, iii) both or iv) neither of them. RAD21 and SMC3 are not represented as there is no PWM available to describe their sequence specificity. \textbf {C} ChIPPartitioning classification with shift and flip of MNase patterns +/- 1kb of YY1.Haib peaks using 10bp bins. YY1 peaks with (upper row) and without (lower row) a CTCF peak within 100bp. Two classes were used to account for "typical" and "non-typical" looking MNase patterns. DNaseI (blue), TSS density (violet) and sequence conservation (green) were overlaid according to MNase classification (taking into account both shift and flip). The number at the upper right corner of each plot indicate the overall class probability. The number of YY1 peaks is slightly smaller than in B) because peaks showing no MNase reads were not included in the classification analysis. Peaklists are named using the TF together with the laboratory which produced the data.\relax }{figure.caption.18}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.5}{\ignorespaces \textbf {Nucleosome free region at CTCF binding sites} \textbf {a} The length are represented as boxplots. The CTCF binding sites are divided into subgroups according to additional presence of SCM3, RAD21, YY1 or ZNF143. The number of binding sites in each subgroup is indicated in red above the boxplots. The presence of SMC3 only, RAD21 only and SMC3 and RAD21 together are indicated in violet, blue and orange respectively. \textbf {B} The proportion of peaks (in green), in each subgroup, having a TSS within a 1kb.\relax }}{39}{figure.caption.19}} +\newlabel{encode_peaks_ctcf_ndr}{{3.5}{39}{\textbf {Nucleosome free region at CTCF binding sites} \textbf {a} The length are represented as boxplots. The CTCF binding sites are divided into subgroups according to additional presence of SCM3, RAD21, YY1 or ZNF143. The number of binding sites in each subgroup is indicated in red above the boxplots. The presence of SMC3 only, RAD21 only and SMC3 and RAD21 together are indicated in violet, blue and orange respectively. \textbf {B} The proportion of peaks (in green), in each subgroup, having a TSS within a 1kb.\relax }{figure.caption.19}{}} \citation{stedman_cohesins_2008} \citation{losada_cohesin_2014} \citation{donohoe_identification_2007} \citation{bailey_znf143_2015} \citation{ong_ctcf:_2014,ghirlando_ctcf:_2016} \citation{wang_sequence_2012,neph_expansive_2012,consortium_integrated_2012,guo_high_2012} \citation{chatr-aryamontri_biogrid_2017} \citation{wang_sequence_2012,neph_expansive_2012,consortium_integrated_2012,guo_high_2012} \citation{chatr-aryamontri_biogrid_2017} \citation{ghirlando_ctcf:_2016} \citation{ong_ctcf:_2014} \@writefile{toc}{\contentsline {section}{\numberline {3.5}CTCF and JunD interactomes}{41}{section.3.5}} -\@writefile{lof}{\contentsline {figure}{\numberline {3.6}{\ignorespaces \textbf {CTCF motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The CTCF dataset ORs are too high to be represented in this plot. \textbf {B} Density of CTCF motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif. The absence of CTCF motif within the first 70bp around CTCF binding sites is explained by the peak processing (see section \ref {encode_peaks_methods_data}).\relax }}{42}{figure.caption.22}} -\newlabel{encode_peaks_ctcf_association}{{3.6}{42}{\textbf {CTCF motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The CTCF dataset ORs are too high to be represented in this plot. \textbf {B} Density of CTCF motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif. The absence of CTCF motif within the first 70bp around CTCF binding sites is explained by the peak processing (see section \ref {encode_peaks_methods_data}).\relax }{figure.caption.22}{}} -\@writefile{lot}{\contentsline {table}{\numberline {3.1}{\ignorespaces \textbf {Identified associations : } Details of all the TF associations identified, as well as the possible molecular mechanisms explaining them. The columns 'TF${_A}$' and 'TF${_B}$' refer to the TF involved in the association, 'Motif.ass.' to whether both motif are associated together ('positive') or repel each other ('negative'), as measured by the Fisher test, 'Type' to the proposed interaction mechanism between both TFs, 'Binder' to the TF binding DNA in case of an indirect co-binding, the value 'both' means that both tethering complexes may exist, 'Reported' to whether this interaction has already been reported in one of the following study \cite {wang_sequence_2012, neph_expansive_2012, consortium_integrated_2012, guo_high_2012} and 'Validated' to whether this physical association is experimentally validated and reported in BioGRID v.3.4.145 \citep {chatr-aryamontri_biogrid_2017}.\relax }}{43}{table.caption.23}} -\newlabel{encode_peaks_association_table}{{3.1}{43}{\textbf {Identified associations : } Details of all the TF associations identified, as well as the possible molecular mechanisms explaining them. The columns 'TF${_A}$' and 'TF${_B}$' refer to the TF involved in the association, 'Motif.ass.' to whether both motif are associated together ('positive') or repel each other ('negative'), as measured by the Fisher test, 'Type' to the proposed interaction mechanism between both TFs, 'Binder' to the TF binding DNA in case of an indirect co-binding, the value 'both' means that both tethering complexes may exist, 'Reported' to whether this interaction has already been reported in one of the following study \cite {wang_sequence_2012, neph_expansive_2012, consortium_integrated_2012, guo_high_2012} and 'Validated' to whether this physical association is experimentally validated and reported in BioGRID v.3.4.145 \citep {chatr-aryamontri_biogrid_2017}.\relax }{table.caption.23}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.6}{\ignorespaces \textbf {CTCF motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The CTCF dataset ORs are too high to be represented in this plot. \textbf {B} Density of CTCF motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif. The absence of CTCF motif within the first 70bp around CTCF binding sites is explained by the peak processing (see section \ref {encode_peaks_methods_data}).\relax }}{42}{figure.caption.20}} +\newlabel{encode_peaks_ctcf_association}{{3.6}{42}{\textbf {CTCF motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The CTCF dataset ORs are too high to be represented in this plot. \textbf {B} Density of CTCF motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif. The absence of CTCF motif within the first 70bp around CTCF binding sites is explained by the peak processing (see section \ref {encode_peaks_methods_data}).\relax }{figure.caption.20}{}} +\@writefile{lot}{\contentsline {table}{\numberline {3.1}{\ignorespaces \textbf {Identified associations : } Details of all the TF associations identified, as well as the possible molecular mechanisms explaining them. The columns 'TF${_A}$' and 'TF${_B}$' refer to the TF involved in the association, 'Motif.ass.' to whether both motif are associated together ('positive') or repel each other ('negative'), as measured by the Fisher test, 'Type' to the proposed interaction mechanism between both TFs, 'Binder' to the TF binding DNA in case of an indirect co-binding, the value 'both' means that both tethering complexes may exist, 'Reported' to whether this interaction has already been reported in one of the following study \cite {wang_sequence_2012, neph_expansive_2012, consortium_integrated_2012, guo_high_2012} and 'Validated' to whether this physical association is experimentally validated and reported in BioGRID v.3.4.145 \citep {chatr-aryamontri_biogrid_2017}.\relax }}{43}{table.caption.21}} +\newlabel{encode_peaks_association_table}{{3.1}{43}{\textbf {Identified associations : } Details of all the TF associations identified, as well as the possible molecular mechanisms explaining them. 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The dyad distributions have been measured from two independent datasets : i) MNase-seq data released by the ENCODE Consortium (in red) and by Gaffney et al. (in blue) \citep {gaffney_controls_2012}. \textbf {B} Dinucleotide frequencies around the nucleosome dyads from the Gaffney dataset that have an EBF1 binding site within 100bp. \textbf {C} Motif frequency around the nucleosome dyads from the Gaffney dataset that have an EBF1 binding site within 100bp. The abrupt decrease of EBF1 motif frequency at +/- 100bp reflects the nucleosome selection process.\relax }}{46}{figure.caption.24}} -\newlabel{encode_peaks_ebf1}{{3.7}{46}{\textbf {EBF1 binding sites} stand on the edge of a nucleosome. \textbf {A} Nucleosome dyad distributions around the EBF1 binding sites (from the Haib dataset). The dyad distributions have been measured from two independent datasets : i) MNase-seq data released by the ENCODE Consortium (in red) and by Gaffney et al. (in blue) \citep {gaffney_controls_2012}. \textbf {B} Dinucleotide frequencies around the nucleosome dyads from the Gaffney dataset that have an EBF1 binding site within 100bp. \textbf {C} Motif frequency around the nucleosome dyads from the Gaffney dataset that have an EBF1 binding site within 100bp. The abrupt decrease of EBF1 motif frequency at +/- 100bp reflects the nucleosome selection process.\relax }{figure.caption.24}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.7}{\ignorespaces \textbf {EBF1 binding sites} stand on the edge of a nucleosome. \textbf {A} Nucleosome dyad distributions around the EBF1 binding sites (from the Haib dataset). The dyad distributions have been measured from two independent datasets : i) MNase-seq data released by the ENCODE Consortium (in red) and by Gaffney et al. (in blue) \citep {gaffney_controls_2012}. \textbf {B} Dinucleotide frequencies around the nucleosome dyads from the Gaffney dataset that have an EBF1 binding site within 100bp. \textbf {C} Motif frequency around the nucleosome dyads from the Gaffney dataset that have an EBF1 binding site within 100bp. The abrupt decrease of EBF1 motif frequency at +/- 100bp reflects the nucleosome selection process.\relax }}{46}{figure.caption.22}} +\newlabel{encode_peaks_ebf1}{{3.7}{46}{\textbf {EBF1 binding sites} stand on the edge of a nucleosome. \textbf {A} Nucleosome dyad distributions around the EBF1 binding sites (from the Haib dataset). The dyad distributions have been measured from two independent datasets : i) MNase-seq data released by the ENCODE Consortium (in red) and by Gaffney et al. 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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}.\relax }}{5}{figure.caption.6}} +\newlabel{intro_nucleosome_positioning}{{1.2}{5}{\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. 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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.\relax }}{8}{figure.caption.9}} -\newlabel{intro_tf_association}{{1.3}{8}{\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. 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Figure and legend taken and adapted from \citep {dreos_eukaryotic_2017}.\relax }}{26}{figure.caption.12}} +\newlabel{lab_resources_epd_pipeline}{{2.2}{26}{\textbf {Schematic representation of the EPDnew pipeline} \textbf {A} Download of authoritative gene catalogs and primary TSS mapping data from public databases, data repositories and consortium websites. \textbf {B} Quality control (QC) of incoming data (e.g. read mapping efficiency, contaminations, etc.). \textbf {C} Data passing QC are reformatted and incorporated into the MGA repository. \textbf {D} Selection of a subset of TSS mapping experiments for generating a new organism-specific TSS collection. \textbf {E} Input data for a new module of EPDnew. \textbf {F} Organism-specific automatic database assembly pipeline tailored to the input data, see \citep {dreos_epd_2013} for a detailed description of the human EPDnew assembly pipeline. \textbf {G} Preliminary or final TSS collection \textbf {H} Manual sanity checks of individual randomly selected promoter entries using the corresponding entry viewer. \textbf {I} Automatic quality evaluation of the TSS collections as a whole by motif enrichment tests, see Figure \ref {lab_resources_epd_motifs} for an example. \textbf {L} Feedback is collected from quality evaluation steps H and I. This may lead to the exclusion, replacement or addition of source data sets or modifications (e.g. program parameter fine-tuning) of the computational database generation pipeline. Note that the development of a final, publicly released EPDnew module typically involves several evaluation-modification cycles. Figure and legend taken and adapted from \citep {dreos_eukaryotic_2017}.\relax }{figure.caption.12}{}} \@writefile{toc}{\contentsline {section}{\numberline {2.2}Eukaryotic Promoter Database}{26}{section.2.2}} \citation{dreos_eukaryotic_2017} \citation{dreos_eukaryotic_2015} \citation{dreos_eukaryotic_2017} \citation{ambrosini_signal_2003} \citation{ambrosini_signal_2003} -\@writefile{lot}{\contentsline {table}{\numberline {2.1}{\ignorespaces \textbf {Current contents of EPDnew} 'Promoters' indicate the number of TSS entries in EPDnew. 'Genes' indicates the number of genes having at least one TSS annotated in EPDnew. 'Genes' indicates the number of protein coding genes contained in the genome annotation (except for nc species). 'nc' stands for non-coding and indicates the long non-coding gene annotations. For 'nc' entries, 'genes' refers to the number of long non-coding genes present in the annotation. In parenthesis are indicated the percentages of genes having a at least one TSS annotated in EPDnew.\relax }}{27}{table.caption.15}} -\newlabel{lab_resources_epd_stats}{{2.1}{27}{\textbf {Current contents of EPDnew} 'Promoters' indicate the number of TSS entries in EPDnew. 'Genes' indicates the number of genes having at least one TSS annotated in EPDnew. 'Genes' indicates the number of protein coding genes contained in the genome annotation (except for nc species). 'nc' stands for non-coding and indicates the long non-coding gene annotations. For 'nc' entries, 'genes' refers to the number of long non-coding genes present in the annotation. In parenthesis are indicated the percentages of genes having a at least one TSS annotated in EPDnew.\relax }{table.caption.15}{}} +\@writefile{lot}{\contentsline {table}{\numberline {2.1}{\ignorespaces \textbf {Current contents of EPDnew} 'Promoters' indicate the number of TSS entries in EPDnew. 'Genes' indicates the number of genes having at least one TSS annotated in EPDnew. 'Genes' indicates the number of protein coding genes contained in the genome annotation (except for nc species). 'nc' stands for non-coding and indicates the long non-coding gene annotations. For 'nc' entries, 'genes' refers to the number of long non-coding genes present in the annotation. In parenthesis are indicated the percentages of genes having a at least one TSS annotated in EPDnew.\relax }}{27}{table.caption.13}} +\newlabel{lab_resources_epd_stats}{{2.1}{27}{\textbf {Current contents of EPDnew} 'Promoters' indicate the number of TSS entries in EPDnew. 'Genes' indicates the number of genes having at least one TSS annotated in EPDnew. 'Genes' indicates the number of protein coding genes contained in the genome annotation (except for nc species). 'nc' stands for non-coding and indicates the long non-coding gene annotations. For 'nc' entries, 'genes' refers to the number of long non-coding genes present in the annotation. In parenthesis are indicated the percentages of genes having a at least one TSS annotated in EPDnew.\relax }{table.caption.13}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {2.2.1}EPDnew now annotates (some of) your mushrooms and vegetables}{27}{subsection.2.2.1}} \citation{dreos_eukaryotic_2017} \citation{dreos_mga_2018} \citation{ambrosini_chip-seq_2016} \citation{ambrosini_signal_2003} \citation{dreos_epd_2013} \citation{dreos_eukaryotic_2017} \citation{raney_track_2014} -\@writefile{lof}{\contentsline {figure}{\numberline {2.3}{\ignorespaces \textbf {TSS Mapping precision} Occurrence of the TATA-box \textbf {A} and initiator \textbf {B} around \textit {H.sapiens} TSSs from EPDnew releases (004 and 006) and from a list of gene starts from UCSC Gene list, which was used as input for the generation of the EPDnew collection. This figure was created using Oprof from the SSA server \citep {ambrosini_signal_2003}. Detailed instructions to recreate the figure can be found in section \ref {lab_resources_epd_methods_oprof}.\relax }}{28}{figure.caption.16}} -\newlabel{lab_resources_epd_motifs}{{2.3}{28}{\textbf {TSS Mapping precision} Occurrence of the TATA-box \textbf {A} and initiator \textbf {B} around \textit {H.sapiens} TSSs from EPDnew releases (004 and 006) and from a list of gene starts from UCSC Gene list, which was used as input for the generation of the EPDnew collection. This figure was created using Oprof from the SSA server \citep {ambrosini_signal_2003}. Detailed instructions to recreate the figure can be found in section \ref {lab_resources_epd_methods_oprof}.\relax }{figure.caption.16}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {2.3}{\ignorespaces \textbf {TSS Mapping precision} Occurrence of the TATA-box \textbf {A} and initiator \textbf {B} around \textit {H.sapiens} TSSs from EPDnew releases (004 and 006) and from a list of gene starts from UCSC Gene list, which was used as input for the generation of the EPDnew collection. This figure was created using Oprof from the SSA server \citep {ambrosini_signal_2003}. Detailed instructions to recreate the figure can be found in section \ref {lab_resources_epd_methods_oprof}.\relax }}{28}{figure.caption.14}} +\newlabel{lab_resources_epd_motifs}{{2.3}{28}{\textbf {TSS Mapping precision} Occurrence of the TATA-box \textbf {A} and initiator \textbf {B} around \textit {H.sapiens} TSSs from EPDnew releases (004 and 006) and from a list of gene starts from UCSC Gene list, which was used as input for the generation of the EPDnew collection. This figure was created using Oprof from the SSA server \citep {ambrosini_signal_2003}. 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Letter probability matrices or count matrices are also accepted and are converted into PWMs. The score threshold can also be given as a p-value or a percentage of the maximum score, in which case it is converted into a threshold score. Based on the length of the PWM, Bowtie or pwm\_scan can be used to find the matches on the genome. If Bowtie is used, the set of k-mers achieving a better score than the threshold score is computed using branch-and-bound algorithm (mba) and mapped on the genome. On the other hand, if matrix\_scan is used, the PWM is used to score every possible sub-sequence in the genome. The regions corresponding to the sequences achieving a score at least as good as the threshold score are then returned under BED format. Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }}{78}{figure.caption.34}} -\newlabel{lab_resources_pwmscan_pipeline}{{6.1}{78}{\textbf {PWMScan workflow :} the input is composed of a PWM and a score threshold specifying the minimum score for a sequence to achieved to be considered as a match. Letter probability matrices or count matrices are also accepted and are converted into PWMs. The score threshold can also be given as a p-value or a percentage of the maximum score, in which case it is converted into a threshold score. Based on the length of the PWM, Bowtie or pwm\_scan can be used to find the matches on the genome. If Bowtie is used, the set of k-mers achieving a better score than the threshold score is computed using branch-and-bound algorithm (mba) and mapped on the genome. On the other hand, if matrix\_scan is used, the PWM is used to score every possible sub-sequence in the genome. The regions corresponding to the sequences achieving a score at least as good as the threshold score are then returned under BED format. Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }{figure.caption.34}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {6.1}{\ignorespaces \textbf {PWMScan workflow :} the input is composed of a PWM and a score threshold specifying the minimum score for a sequence to achieved to be considered as a match. Letter probability matrices or count matrices are also accepted and are converted into PWMs. The score threshold can also be given as a p-value or a percentage of the maximum score, in which case it is converted into a threshold score. Based on the length of the PWM, Bowtie or pwm\_scan can be used to find the matches on the genome. If Bowtie is used, the set of k-mers achieving a better score than the threshold score is computed using branch-and-bound algorithm (mba) and mapped on the genome. On the other hand, if matrix\_scan is used, the PWM is used to score every possible sub-sequence in the genome. The regions corresponding to the sequences achieving a score at least as good as the threshold score are then returned under BED format. Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }}{78}{figure.caption.32}} +\newlabel{lab_resources_pwmscan_pipeline}{{6.1}{78}{\textbf {PWMScan workflow :} the input is composed of a PWM and a score threshold specifying the minimum score for a sequence to achieved to be considered as a match. Letter probability matrices or count matrices are also accepted and are converted into PWMs. The score threshold can also be given as a p-value or a percentage of the maximum score, in which case it is converted into a threshold score. Based on the length of the PWM, Bowtie or pwm\_scan can be used to find the matches on the genome. 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Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }{figure.caption.32}{}} \citation{ambrosini_chip-seq_2016} \citation{ambrosini_signal_2003} \citation{ambrosini_pwmscan:_2018} \citation{ambrosini_pwmscan:_2018} \citation{ambrosini_pwmscan:_2018} \citation{ambrosini_pwmscan:_2018} \citation{hertz_identification_1990} \citation{beckstette_fast_2006} \citation{turatsinze_using_2008} \citation{heinz_simple_2010} \citation{grant_fimo:_2011} \citation{schones_statistical_2007} -\@writefile{lof}{\contentsline {figure}{\numberline {6.2}{\ignorespaces \textbf {Benchmark :} PWMScan speed performances were measured and compared with 6 other well known genome scanners. In all cases, the h19 genome sequence was scanned with a 19bp CTCF matrix and a 11bp STAT1 matrix, 10 times. The run times are represented as boxplots. For PWMScan, both pwm\_scan and Bowtie strategies were run. Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }}{79}{figure.caption.35}} -\newlabel{lab_resources_pwmscan_benchmark}{{6.2}{79}{\textbf {Benchmark :} PWMScan speed performances were measured and compared with 6 other well known genome scanners. In all cases, the h19 genome sequence was scanned with a 19bp CTCF matrix and a 11bp STAT1 matrix, 10 times. The run times are represented as boxplots. For PWMScan, both pwm\_scan and Bowtie strategies were run. Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }{figure.caption.35}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {6.2}{\ignorespaces \textbf {Benchmark :} PWMScan speed performances were measured and compared with 6 other well known genome scanners. In all cases, the h19 genome sequence was scanned with a 19bp CTCF matrix and a 11bp STAT1 matrix, 10 times. The run times are represented as boxplots. For PWMScan, both pwm\_scan and Bowtie strategies were run. Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }}{79}{figure.caption.33}} +\newlabel{lab_resources_pwmscan_benchmark}{{6.2}{79}{\textbf {Benchmark :} PWMScan speed performances were measured and compared with 6 other well known genome scanners. In all cases, the h19 genome sequence was scanned with a 19bp CTCF matrix and a 11bp STAT1 matrix, 10 times. The run times are represented as boxplots. For PWMScan, both pwm\_scan and Bowtie strategies were run. Figure and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }{figure.caption.33}{}} \@writefile{toc}{\contentsline {section}{\numberline {6.3}Benchmark}{79}{section.6.3}} \citation{aerts_toucan:_2003} \citation{fu_motifviz:_2004} \citation{zhao_tred:_2005} -\@writefile{lot}{\contentsline {table}{\numberline {6.1}{\ignorespaces \textbf {Motif scanning software comparison}. The performances of matrix\_scan were assessed by comparing how many of the regions listed by matrix\_scan were also returned by other programs and if the region scores were comparable. For the percentage of overlap with the match list returned by matrix\_scan, the shorter of the two lists always serves as the reference (100\%). For the score correlations with matrix\_scan scores, the Spearman correlation was used. Table and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }}{80}{table.caption.36}} -\newlabel{lab_resources_pwmscan_benchmark_table}{{6.1}{80}{\textbf {Motif scanning software comparison}. The performances of matrix\_scan were assessed by comparing how many of the regions listed by matrix\_scan were also returned by other programs and if the region scores were comparable. For the percentage of overlap with the match list returned by matrix\_scan, the shorter of the two lists always serves as the reference (100\%). For the score correlations with matrix\_scan scores, the Spearman correlation was used. Table and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }{table.caption.36}{}} +\@writefile{lot}{\contentsline {table}{\numberline {6.1}{\ignorespaces \textbf {Motif scanning software comparison}. The performances of matrix\_scan were assessed by comparing how many of the regions listed by matrix\_scan were also returned by other programs and if the region scores were comparable. For the percentage of overlap with the match list returned by matrix\_scan, the shorter of the two lists always serves as the reference (100\%). For the score correlations with matrix\_scan scores, the Spearman correlation was used. Table and legend taken and adapted from \citep {ambrosini_pwmscan:_2018}.\relax }}{80}{table.caption.34}} +\newlabel{lab_resources_pwmscan_benchmark_table}{{6.1}{80}{\textbf {Motif scanning software comparison}. The performances of matrix\_scan were assessed by comparing how many of the regions listed by matrix\_scan were also returned by other programs and if the region scores were comparable. For the percentage of overlap with the match list returned by matrix\_scan, the shorter of the two lists always serves as the reference (100\%). For the score correlations with matrix\_scan scores, the Spearman correlation was used. 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A snapshot of three units of the microfluidic device is shown. In vitro transcribed and translated bait TF, target dsDNA, and a nonspecific competitor poly-dIdC are mixed and pipetted in one of the wells of the microfluidic device. The mixtures are then passively pumped in the device (bottom panel). Newly formed TF\IeC {\textendash }DNA complexes are trapped under a flexible polydimethylsiloxane membrane, and unbound molecules as well as molecular complexes are washed away (upper panel). Left, schematic representation of three individual chambers. Right, corresponding snapshots of an individual chamber taken before and after mechanical trapping. \textbf {b} Data processing pipeline. The bound DNA is eluted from all the units of the device simultaneously and collected in one tube. Recovered DNA is amplified and sequenced. The sequencing reads are then demultiplexed, and a seed sequence is identified for each sample. This seed is then used to initialize a probability matrix representing the sequence specificity model for the given TF. The model parameters are then optimized using a Hidden Markov Model-based motif discovery pipeline. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}.\relax }}{68}{figure.caption.31}} -\newlabel{smile_seq_pipeline}{{5.1}{68}{\textbf {SMiLE-seq pipeline :} \textbf {a} Schematic representation of the experimental setup. A snapshot of three units of the microfluidic device is shown. In vitro transcribed and translated bait TF, target dsDNA, and a nonspecific competitor poly-dIdC are mixed and pipetted in one of the wells of the microfluidic device. The mixtures are then passively pumped in the device (bottom panel). Newly formed TF–DNA complexes are trapped under a flexible polydimethylsiloxane membrane, and unbound molecules as well as molecular complexes are washed away (upper panel). Left, schematic representation of three individual chambers. Right, corresponding snapshots of an individual chamber taken before and after mechanical trapping. \textbf {b} Data processing pipeline. The bound DNA is eluted from all the units of the device simultaneously and collected in one tube. Recovered DNA is amplified and sequenced. The sequencing reads are then demultiplexed, and a seed sequence is identified for each sample. This seed is then used to initialize a probability matrix representing the sequence specificity model for the given TF. The model parameters are then optimized using a Hidden Markov Model-based motif discovery pipeline. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}.\relax }{figure.caption.31}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {5.1}{\ignorespaces \textbf {SMiLE-seq pipeline :} \textbf {a} Schematic representation of the experimental setup. A snapshot of three units of the microfluidic device is shown. In vitro transcribed and translated bait TF, target dsDNA, and a nonspecific competitor poly-dIdC are mixed and pipetted in one of the wells of the microfluidic device. The mixtures are then passively pumped in the device (bottom panel). Newly formed TF\IeC {\textendash }DNA complexes are trapped under a flexible polydimethylsiloxane membrane, and unbound molecules as well as molecular complexes are washed away (upper panel). Left, schematic representation of three individual chambers. Right, corresponding snapshots of an individual chamber taken before and after mechanical trapping. \textbf {b} Data processing pipeline. The bound DNA is eluted from all the units of the device simultaneously and collected in one tube. Recovered DNA is amplified and sequenced. The sequencing reads are then demultiplexed, and a seed sequence is identified for each sample. This seed is then used to initialize a probability matrix representing the sequence specificity model for the given TF. The model parameters are then optimized using a Hidden Markov Model-based motif discovery pipeline. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}.\relax }}{68}{figure.caption.29}} +\newlabel{smile_seq_pipeline}{{5.1}{68}{\textbf {SMiLE-seq pipeline :} \textbf {a} Schematic representation of the experimental setup. A snapshot of three units of the microfluidic device is shown. In vitro transcribed and translated bait TF, target dsDNA, and a nonspecific competitor poly-dIdC are mixed and pipetted in one of the wells of the microfluidic device. The mixtures are then passively pumped in the device (bottom panel). Newly formed TF–DNA complexes are trapped under a flexible polydimethylsiloxane membrane, and unbound molecules as well as molecular complexes are washed away (upper panel). Left, schematic representation of three individual chambers. Right, corresponding snapshots of an individual chamber taken before and after mechanical trapping. \textbf {b} Data processing pipeline. The bound DNA is eluted from all the units of the device simultaneously and collected in one tube. Recovered DNA is amplified and sequenced. The sequencing reads are then demultiplexed, and a seed sequence is identified for each sample. This seed is then used to initialize a probability matrix representing the sequence specificity model for the given TF. The model parameters are then optimized using a Hidden Markov Model-based motif discovery pipeline. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}.\relax }{figure.caption.29}{}} \citation{isakova_smile-seq_2017} \citation{isakova_smile-seq_2017} \citation{weirauch_evaluation_2013} -\@writefile{lof}{\contentsline {figure}{\numberline {5.2}{\ignorespaces \textbf {Example of a Hidden Markov model :} initial HMM representation with a seed sequence 'ATGCC'. The upper Markov chain models + strand motif containing sequences, the middle one - strand motif containing sequences and the lower zero motif occurrence sequences. The FB, FE, RB and RE positions represents positions in the sequence that occur before and after the binding site on the forward and reverse strand. For these nodes, a self transition exist to allow the binding site to occur at a variable distance from the beginning and the end of the sequence. Once transiting toward the 1st position of the binding site, the next transition is forced toward the 2nd position in the binding site, and so on until the end of the binding site. The + strand and - strand Markov chains emission parameters are paired together (they have the same values), as represented by the grey dashed lines. The transition probabilities in red are not subjected to the Baum-Welch training. Finally, a binding model represented as a probability matrix is composed of the emission probabilities at the binding site positions. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}\relax }}{69}{figure.caption.32}} -\newlabel{smile_seq_hmm}{{5.2}{69}{\textbf {Example of a Hidden Markov model :} initial HMM representation with a seed sequence 'ATGCC'. The upper Markov chain models + strand motif containing sequences, the middle one - strand motif containing sequences and the lower zero motif occurrence sequences. The FB, FE, RB and RE positions represents positions in the sequence that occur before and after the binding site on the forward and reverse strand. For these nodes, a self transition exist to allow the binding site to occur at a variable distance from the beginning and the end of the sequence. Once transiting toward the 1st position of the binding site, the next transition is forced toward the 2nd position in the binding site, and so on until the end of the binding site. The + strand and - strand Markov chains emission parameters are paired together (they have the same values), as represented by the grey dashed lines. The transition probabilities in red are not subjected to the Baum-Welch training. Finally, a binding model represented as a probability matrix is composed of the emission probabilities at the binding site positions. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}\relax }{figure.caption.32}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {5.2}{\ignorespaces \textbf {Example of a Hidden Markov model :} initial HMM representation with a seed sequence 'ATGCC'. The upper Markov chain models + strand motif containing sequences, the middle one - strand motif containing sequences and the lower zero motif occurrence sequences. The FB, FE, RB and RE positions represents positions in the sequence that occur before and after the binding site on the forward and reverse strand. For these nodes, a self transition exist to allow the binding site to occur at a variable distance from the beginning and the end of the sequence. Once transiting toward the 1st position of the binding site, the next transition is forced toward the 2nd position in the binding site, and so on until the end of the binding site. The + strand and - strand Markov chains emission parameters are paired together (they have the same values), as represented by the grey dashed lines. The transition probabilities in red are not subjected to the Baum-Welch training. Finally, a binding model represented as a probability matrix is composed of the emission probabilities at the binding site positions. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}\relax }}{69}{figure.caption.30}} +\newlabel{smile_seq_hmm}{{5.2}{69}{\textbf {Example of a Hidden Markov model :} initial HMM representation with a seed sequence 'ATGCC'. The upper Markov chain models + strand motif containing sequences, the middle one - strand motif containing sequences and the lower zero motif occurrence sequences. The FB, FE, RB and RE positions represents positions in the sequence that occur before and after the binding site on the forward and reverse strand. For these nodes, a self transition exist to allow the binding site to occur at a variable distance from the beginning and the end of the sequence. Once transiting toward the 1st position of the binding site, the next transition is forced toward the 2nd position in the binding site, and so on until the end of the binding site. The + strand and - strand Markov chains emission parameters are paired together (they have the same values), as represented by the grey dashed lines. The transition probabilities in red are not subjected to the Baum-Welch training. Finally, a binding model represented as a probability matrix is composed of the emission probabilities at the binding site positions. Figure and legend taken and adapted from \citep {isakova_smile-seq_2017}\relax }{figure.caption.30}{}} \@writefile{toc}{\contentsline {section}{\numberline {5.2}Hidden Markov Model Motif discovery}{69}{section.5.2}} \newlabel{section_smileseq_hmm}{{5.2}{69}{Hidden Markov Model Motif discovery}{section.5.2}{}} \citation{schutz_mamot:_2008} \citation{orenstein_comparative_2014} \@writefile{toc}{\contentsline {section}{\numberline {5.3}Binding motif evaluation}{70}{section.5.3}} \newlabel{section_smileseq_pwmeval}{{5.3}{70}{Binding motif evaluation}{section.5.3}{}} \citation{jolma_dna-binding_2013} \citation{mathelier_jaspar_2014} \citation{kulakovskiy_hocomoco:_2016} \newlabel{smile_seq_pwmeval_score}{{5.1}{71}{Binding motif evaluation}{equation.5.3.1}{}} \@writefile{toc}{\contentsline {section}{\numberline {5.4}Results}{71}{section.5.4}} -\@writefile{lof}{\contentsline {figure}{\numberline {5.3}{\ignorespaces \textbf {Predictive power of SMiLE-seq :} \textbf {A} the motifs compared to that of previously reported motifs that are retrievable from the indicated databases. For each motif, the AUC-ROC values on the 500 top peaks of the ENCODE ChIP-seq data sets for the corresponding TF was computed. The heatmap represents the AUC values computed for each method on the respective ChIP-seq data sets that were selected based on the highest mean AUC values among all five models. \textbf {B} the predictive performances of MAX and YY1 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. The boxplots indicate the distribution of AUC-ROC obtained over all available peak-lists.\relax }}{72}{figure.caption.33}} -\newlabel{smileseq_auc}{{5.3}{72}{\textbf {Predictive power of SMiLE-seq :} \textbf {A} the motifs compared to that of previously reported motifs that are retrievable from the indicated databases. For each motif, the AUC-ROC values on the 500 top peaks of the ENCODE ChIP-seq data sets for the corresponding TF was computed. The heatmap represents the AUC values computed for each method on the respective ChIP-seq data sets that were selected based on the highest mean AUC values among all five models. \textbf {B} the predictive performances of MAX and YY1 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. The boxplots indicate the distribution of AUC-ROC obtained over all available peak-lists.\relax }{figure.caption.33}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {5.3}{\ignorespaces \textbf {Predictive power of SMiLE-seq :} \textbf {A} the motifs compared to that of previously reported motifs that are retrievable from the indicated databases. For each motif, the AUC-ROC values on the 500 top peaks of the ENCODE ChIP-seq data sets for the corresponding TF was computed. The heatmap represents the AUC values computed for each method on the respective ChIP-seq data sets that were selected based on the highest mean AUC values among all five models. \textbf {B} the predictive performances of MAX and YY1 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. The boxplots indicate the distribution of AUC-ROC obtained over all available peak-lists.\relax }}{72}{figure.caption.31}} +\newlabel{smileseq_auc}{{5.3}{72}{\textbf {Predictive power of SMiLE-seq :} \textbf {A} the motifs compared to that of previously reported motifs that are retrievable from the indicated databases. For each motif, the AUC-ROC values on the 500 top peaks of the ENCODE ChIP-seq data sets for the corresponding TF was computed. The heatmap represents the AUC values computed for each method on the respective ChIP-seq data sets that were selected based on the highest mean AUC values among all five models. \textbf {B} the predictive performances of MAX and YY1 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. 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The color ribbons on the side indicate the cluster assignments. \textbf {D} A synthetic dataset with a mean coverage of a 100 reads per region in average ($c$=100) and 90\% noise ($p_{s}$=0.1, $p_{b}$=0.9) and \textbf {E} one of the corresponding SPar-K partition, with shifting and flipping.\relax }}{59}{figure.caption.25}} -\newlabel{spark_simulated_data}{{4.1}{59}{Synthethic datasets : \textbf {A} The class signal densities. \textbf {B} A synthetic dataset with a mean coverage of a 100 reads per region in average ($c$=100) and 0\% noise ($p_{s}$=1, $p_{b}$=0) and \textbf {C} one of the corresponding SPar-K partition, with shifting and flipping. The color ribbons on the side indicate the cluster assignments. \textbf {D} A synthetic dataset with a mean coverage of a 100 reads per region in average ($c$=100) and 90\% noise ($p_{s}$=0.1, $p_{b}$=0.9) and \textbf {E} one of the corresponding SPar-K partition, with shifting and flipping.\relax }{figure.caption.25}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {4.1}{\ignorespaces Synthethic datasets : \textbf {A} The class signal densities. \textbf {B} A synthetic dataset with a mean coverage of a 100 reads per region in average ($c$=100) and 0\% noise ($p_{s}$=1, $p_{b}$=0) and \textbf {C} one of the corresponding SPar-K partition, with shifting and flipping. The color ribbons on the side indicate the cluster assignments. \textbf {D} A synthetic dataset with a mean coverage of a 100 reads per region in average ($c$=100) and 90\% noise ($p_{s}$=0.1, $p_{b}$=0.9) and \textbf {E} one of the corresponding SPar-K partition, with shifting and flipping.\relax }}{59}{figure.caption.23}} +\newlabel{spark_simulated_data}{{4.1}{59}{Synthethic datasets : \textbf {A} The class signal densities. \textbf {B} A synthetic dataset with a mean coverage of a 100 reads per region in average ($c$=100) and 0\% noise ($p_{s}$=1, $p_{b}$=0) and \textbf {C} one of the corresponding SPar-K partition, with shifting and flipping. The color ribbons on the side indicate the cluster assignments. \textbf {D} A synthetic dataset with a mean coverage of a 100 reads per region in average ($c$=100) and 90\% noise ($p_{s}$=0.1, $p_{b}$=0.9) and \textbf {E} one of the corresponding SPar-K partition, with shifting and flipping.\relax }{figure.caption.23}{}} \@writefile{toc}{\contentsline {section}{\numberline {4.3}Benchmarking}{59}{section.4.3}} \@writefile{toc}{\contentsline {subsection}{\numberline {4.3.1}K-means}{59}{subsection.4.3.1}} -\@writefile{lof}{\contentsline {figure}{\numberline {4.2}{\ignorespaces \textbf {Clustering accuracy using random seeding :} to compare the clustering accuracies of the different methods, several simulated dataset containing 3 classes, different coverages (10, 50 and 100 reads per region indicated as "cov10", "cov50" and "cov100") and noise proportions (no noise, 10\% noise, 50\% noise and 90\% noise indicated as "0.0", "0.1", "0.5" and "0.9") were generated. Each dataset was clustered 50 times with each method. The Adjusted Rand Index (ARI) was computed for each partition. The ARI values are displayed as boxplots. SPar-K and ChIPPartitioning were run allowing flipping and shifting. The ARI was measured on each of the resulting data partitions. For SPar-K, "smooth" indicates outlier smoothing. For the regular K-means, "eucl." and "corr." refer to the euclidean and correlation distances. "R" stands for "random" and indicates the ARI values obtained when comparing the true cluster labels with a randomly shuffled version of it, 100 times. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{60}{figure.caption.26}} -\newlabel{spark_ari}{{4.2}{60}{\textbf {Clustering accuracy using random seeding :} to compare the clustering accuracies of the different methods, several simulated dataset containing 3 classes, different coverages (10, 50 and 100 reads per region indicated as "cov10", "cov50" and "cov100") and noise proportions (no noise, 10\% noise, 50\% noise and 90\% noise indicated as "0.0", "0.1", "0.5" and "0.9") were generated. Each dataset was clustered 50 times with each method. The Adjusted Rand Index (ARI) was computed for each partition. The ARI values are displayed as boxplots. SPar-K and ChIPPartitioning were run allowing flipping and shifting. The ARI was measured on each of the resulting data partitions. For SPar-K, "smooth" indicates outlier smoothing. For the regular K-means, "eucl." and "corr." refer to the euclidean and correlation distances. "R" stands for "random" and indicates the ARI values obtained when comparing the true cluster labels with a randomly shuffled version of it, 100 times. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }{figure.caption.26}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {4.3}{\ignorespaces \textbf {Median SSE :} for the simulated ChIP-seq dataset containing 3 classes, with coverage 100 and no noise, partitioned into 2 to 5 clusters. To judge whether the elbow method could be used to estimate the optimal number of clusters, this dataset was partitioned with SPar-K, allowing flip and shifting, into 2 to 5 clusters, 50 times for each set of parameters. For each number of clusters, the median SSE is shown, +/- 1 standard deviation (bars). \textbf {A} Seeding done at random, \textbf {B} seeding done at random and outlier smoothing \textbf {C} seeding done with the K-means++ method \textbf {D} seeding done with the K-means++ method and outlier smoothing. In all cases, the optimal number of clusters seemed to be 3 (which was the expected value). Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{61}{figure.caption.27}} -\newlabel{spark_sse}{{4.3}{61}{\textbf {Median SSE :} for the simulated ChIP-seq dataset containing 3 classes, with coverage 100 and no noise, partitioned into 2 to 5 clusters. To judge whether the elbow method could be used to estimate the optimal number of clusters, this dataset was partitioned with SPar-K, allowing flip and shifting, into 2 to 5 clusters, 50 times for each set of parameters. For each number of clusters, the median SSE is shown, +/- 1 standard deviation (bars). \textbf {A} Seeding done at random, \textbf {B} seeding done at random and outlier smoothing \textbf {C} seeding done with the K-means++ method \textbf {D} seeding done with the K-means++ method and outlier smoothing. In all cases, the optimal number of clusters seemed to be 3 (which was the expected value). Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }{figure.caption.27}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {4.4}{\ignorespaces \textbf {Running times :} to compare the run times of each program, the synthetic dataset with coverage 100 and no noise was partitioned 20 times with each program. The run times (wall clock) in second were measured. For all SPar-K and the regular K-means, the partitions were initialized using a random and K-means++ (indicated as "k++"). For ChIPPartitioning, only a random seeding was used. The partitions were then optimized for 30 iterations at most. For SPar-K and ChIPPartitioning, a shifting of 71 bins and flipping were allowed. For SPar-K, only one thread was used and "smooth" indicates outlier smoothing. For the regular K-means, "eucl." and "corr." refer to the euclidean and correlation distances. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{61}{figure.caption.28}} -\newlabel{spark_time}{{4.4}{61}{\textbf {Running times :} to compare the run times of each program, the synthetic dataset with coverage 100 and no noise was partitioned 20 times with each program. The run times (wall clock) in second were measured. For all SPar-K and the regular K-means, the partitions were initialized using a random and K-means++ (indicated as "k++"). For ChIPPartitioning, only a random seeding was used. The partitions were then optimized for 30 iterations at most. For SPar-K and ChIPPartitioning, a shifting of 71 bins and flipping were allowed. For SPar-K, only one thread was used and "smooth" indicates outlier smoothing. For the regular K-means, "eucl." and "corr." refer to the euclidean and correlation distances. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }{figure.caption.28}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {4.2}{\ignorespaces \textbf {Clustering accuracy using random seeding :} to compare the clustering accuracies of the different methods, several simulated dataset containing 3 classes, different coverages (10, 50 and 100 reads per region indicated as "cov10", "cov50" and "cov100") and noise proportions (no noise, 10\% noise, 50\% noise and 90\% noise indicated as "0.0", "0.1", "0.5" and "0.9") were generated. Each dataset was clustered 50 times with each method. The Adjusted Rand Index (ARI) was computed for each partition. The ARI values are displayed as boxplots. SPar-K and ChIPPartitioning were run allowing flipping and shifting. The ARI was measured on each of the resulting data partitions. For SPar-K, "smooth" indicates outlier smoothing. For the regular K-means, "eucl." and "corr." refer to the euclidean and correlation distances. "R" stands for "random" and indicates the ARI values obtained when comparing the true cluster labels with a randomly shuffled version of it, 100 times. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{60}{figure.caption.24}} +\newlabel{spark_ari}{{4.2}{60}{\textbf {Clustering accuracy using random seeding :} to compare the clustering accuracies of the different methods, several simulated dataset containing 3 classes, different coverages (10, 50 and 100 reads per region indicated as "cov10", "cov50" and "cov100") and noise proportions (no noise, 10\% noise, 50\% noise and 90\% noise indicated as "0.0", "0.1", "0.5" and "0.9") were generated. Each dataset was clustered 50 times with each method. The Adjusted Rand Index (ARI) was computed for each partition. The ARI values are displayed as boxplots. SPar-K and ChIPPartitioning were run allowing flipping and shifting. The ARI was measured on each of the resulting data partitions. For SPar-K, "smooth" indicates outlier smoothing. For the regular K-means, "eucl." and "corr." refer to the euclidean and correlation distances. "R" stands for "random" and indicates the ARI values obtained when comparing the true cluster labels with a randomly shuffled version of it, 100 times. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }{figure.caption.24}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {4.3}{\ignorespaces \textbf {Median SSE :} for the simulated ChIP-seq dataset containing 3 classes, with coverage 100 and no noise, partitioned into 2 to 5 clusters. To judge whether the elbow method could be used to estimate the optimal number of clusters, this dataset was partitioned with SPar-K, allowing flip and shifting, into 2 to 5 clusters, 50 times for each set of parameters. For each number of clusters, the median SSE is shown, +/- 1 standard deviation (bars). \textbf {A} Seeding done at random, \textbf {B} seeding done at random and outlier smoothing \textbf {C} seeding done with the K-means++ method \textbf {D} seeding done with the K-means++ method and outlier smoothing. In all cases, the optimal number of clusters seemed to be 3 (which was the expected value). Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{61}{figure.caption.25}} +\newlabel{spark_sse}{{4.3}{61}{\textbf {Median SSE :} for the simulated ChIP-seq dataset containing 3 classes, with coverage 100 and no noise, partitioned into 2 to 5 clusters. To judge whether the elbow method could be used to estimate the optimal number of clusters, this dataset was partitioned with SPar-K, allowing flip and shifting, into 2 to 5 clusters, 50 times for each set of parameters. For each number of clusters, the median SSE is shown, +/- 1 standard deviation (bars). \textbf {A} Seeding done at random, \textbf {B} seeding done at random and outlier smoothing \textbf {C} seeding done with the K-means++ method \textbf {D} seeding done with the K-means++ method and outlier smoothing. In all cases, the optimal number of clusters seemed to be 3 (which was the expected value). Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }{figure.caption.25}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {4.4}{\ignorespaces \textbf {Running times :} to compare the run times of each program, the synthetic dataset with coverage 100 and no noise was partitioned 20 times with each program. The run times (wall clock) in second were measured. For all SPar-K and the regular K-means, the partitions were initialized using a random and K-means++ (indicated as "k++"). For ChIPPartitioning, only a random seeding was used. The partitions were then optimized for 30 iterations at most. For SPar-K and ChIPPartitioning, a shifting of 71 bins and flipping were allowed. For SPar-K, only one thread was used and "smooth" indicates outlier smoothing. For the regular K-means, "eucl." and "corr." refer to the euclidean and correlation distances. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{61}{figure.caption.26}} +\newlabel{spark_time}{{4.4}{61}{\textbf {Running times :} to compare the run times of each program, the synthetic dataset with coverage 100 and no noise was partitioned 20 times with each program. The run times (wall clock) in second were measured. For all SPar-K and the regular K-means, the partitions were initialized using a random and K-means++ (indicated as "k++"). For ChIPPartitioning, only a random seeding was used. The partitions were then optimized for 30 iterations at most. For SPar-K and ChIPPartitioning, a shifting of 71 bins and flipping were allowed. 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Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }{figure.caption.26}{}} \citation{groux_spar-k:_2019} \@writefile{toc}{\contentsline {subsection}{\numberline {4.3.2}ChIPPartitioning}{62}{subsection.4.3.2}} \@writefile{toc}{\contentsline {subsection}{\numberline {4.3.3}Data}{62}{subsection.4.3.3}} \citation{ambrosini_chip-seq_2016} \citation{ambrosini_chip-seq_2016} \citation{groux_spar-k:_2019} \citation{groux_spar-k:_2019} \citation{bailey_meme_2009} \citation{kundaje_ubiquitous_2012} \@writefile{toc}{\contentsline {subsection}{\numberline {4.3.4}Performances}{63}{subsection.4.3.4}} \@writefile{toc}{\contentsline {section}{\numberline {4.4}Partition of DNase and MNase data}{63}{section.4.4}} \@writefile{toc}{\contentsline {section}{\numberline {4.5}Conclusions}{63}{section.4.5}} -\@writefile{lof}{\contentsline {figure}{\numberline {4.5}{\ignorespaces Nucleosome occupancy, determined by MNase-seq, in bins of 10bp, +/- 1000bp around 79'957 CTCF binding sites in GM12878 cells. \textbf {A} MNaseI-seq read density around the CTCF binding sites. ChIP-seq peak summits are aligned at position 0. The regions (rows) are ordered according the their resemblance (correlation) to the overall aggregation pattern. \textbf {B} SPar-K data partition. The number of clusters (4) was determined using the elbow method. The cluster labels are indicated by the color ribbons on the left. Within each cluster, the data have been realigned according to the shift and flip informations returned by SPar-K and the regions have been ordered according the their resemblance (correlation) to the cluster aggregation pattern. Because of the realignment, ChIP-seq peak summits are not anymore aligned at position 0. \textbf {C} Corresponding DNaseI hypersensitivity measured by DNaseI-seq at the same loci and realigned as in B. \textbf {D} CTCF motif occurrences predicted using a motif scan, at the same loci and realigned as in B. Each predicted binding site, +/- 1kb around a peak, is represented as a point. \textbf {E} Transcription start site (TSS) density at the same loci and realigned as in B. \textbf {F} Cluster 1 (red) aggregation profiles. The original peak coordinates were modified accordingly to the shift and flip values returned by SPar-K and the read densities the different data types were measured using ChIP-Cor \citep {ambrosini_chip-seq_2016}. For the TSSs and the transcription initiation (CAGE), only the data mapping on the negative strand were used to monitor transcription firing towards the nucleosome array (towards the left). \textbf {G} Proportions of regions having at least one CTCF motif +/- 1kb (same motifs as in D), for each cluster. \textbf {H} Proportions of regions having at least one TSS +/- 1kb (same TSSs as in E), for each cluster.\relax }}{64}{figure.caption.29}} -\newlabel{spark_ctcf}{{4.5}{64}{Nucleosome occupancy, determined by MNase-seq, in bins of 10bp, +/- 1000bp around 79'957 CTCF binding sites in GM12878 cells. \textbf {A} MNaseI-seq read density around the CTCF binding sites. ChIP-seq peak summits are aligned at position 0. The regions (rows) are ordered according the their resemblance (correlation) to the overall aggregation pattern. \textbf {B} SPar-K data partition. The number of clusters (4) was determined using the elbow method. The cluster labels are indicated by the color ribbons on the left. Within each cluster, the data have been realigned according to the shift and flip informations returned by SPar-K and the regions have been ordered according the their resemblance (correlation) to the cluster aggregation pattern. Because of the realignment, ChIP-seq peak summits are not anymore aligned at position 0. \textbf {C} Corresponding DNaseI hypersensitivity measured by DNaseI-seq at the same loci and realigned as in B. \textbf {D} CTCF motif occurrences predicted using a motif scan, at the same loci and realigned as in B. Each predicted binding site, +/- 1kb around a peak, is represented as a point. \textbf {E} Transcription start site (TSS) density at the same loci and realigned as in B. \textbf {F} Cluster 1 (red) aggregation profiles. The original peak coordinates were modified accordingly to the shift and flip values returned by SPar-K and the read densities the different data types were measured using ChIP-Cor \citep {ambrosini_chip-seq_2016}. For the TSSs and the transcription initiation (CAGE), only the data mapping on the negative strand were used to monitor transcription firing towards the nucleosome array (towards the left). \textbf {G} Proportions of regions having at least one CTCF motif +/- 1kb (same motifs as in D), for each cluster. \textbf {H} Proportions of regions having at least one TSS +/- 1kb (same TSSs as in E), for each cluster.\relax }{figure.caption.29}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {4.6}{\ignorespaces Partitioning of DNaseI hypersensitivity profiles around SP1 binding sites in K562 cells. The optimal number of clusters was determined using the elbow method. \textbf {A.} Input data based on peak summits provided by ENCODE. \textbf {B.} Same regions clustered, re-aligned and oriented by SPar-K. Clusters 1, 2 and 3 are indicated by colored bars in red, blue, and green, respectively. \textbf {C.} MNase-seq read densities for the same regions, ordered, aligned and oriented as in B. \textbf {D.} Predicted SP1 binding motifs for the same regions, ordered, aligned and oriented as in B. \textbf {E.} Proportion of binding sites within each cluster having a confirmed promoter-associated TSS within +/- 300bp. \textbf {F.} Aggregations profiles for DNase-seq (red), MNase-seq (blue), promoter TSS (green) and CAGE-seq data (violet) for cluster 2 (aligned and oriented as in B). \textbf {G.} Motifs found by MEME-ChIP and Tomtom in the narrow footprints of each cluster. (*) known SP1 interactor, (c) central enrichment. Cluster 2 left and right refer to the left and right footprints seen in \textbf {B}. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{65}{figure.caption.30}} -\newlabel{spark_dnase}{{4.6}{65}{Partitioning of DNaseI hypersensitivity profiles around SP1 binding sites in K562 cells. The optimal number of clusters was determined using the elbow method. \textbf {A.} Input data based on peak summits provided by ENCODE. \textbf {B.} Same regions clustered, re-aligned and oriented by SPar-K. Clusters 1, 2 and 3 are indicated by colored bars in red, blue, and green, respectively. \textbf {C.} MNase-seq read densities for the same regions, ordered, aligned and oriented as in B. \textbf {D.} Predicted SP1 binding motifs for the same regions, ordered, aligned and oriented as in B. \textbf {E.} Proportion of binding sites within each cluster having a confirmed promoter-associated TSS within +/- 300bp. \textbf {F.} Aggregations profiles for DNase-seq (red), MNase-seq (blue), promoter TSS (green) and CAGE-seq data (violet) for cluster 2 (aligned and oriented as in B). \textbf {G.} Motifs found by MEME-ChIP and Tomtom in the narrow footprints of each cluster. (*) known SP1 interactor, (c) central enrichment. Cluster 2 left and right refer to the left and right footprints seen in \textbf {B}. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }{figure.caption.30}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {4.5}{\ignorespaces Nucleosome occupancy, determined by MNase-seq, in bins of 10bp, +/- 1000bp around 79'957 CTCF binding sites in GM12878 cells. \textbf {A} MNaseI-seq read density around the CTCF binding sites. ChIP-seq peak summits are aligned at position 0. The regions (rows) are ordered according the their resemblance (correlation) to the overall aggregation pattern. \textbf {B} SPar-K data partition. The number of clusters (4) was determined using the elbow method. The cluster labels are indicated by the color ribbons on the left. Within each cluster, the data have been realigned according to the shift and flip informations returned by SPar-K and the regions have been ordered according the their resemblance (correlation) to the cluster aggregation pattern. Because of the realignment, ChIP-seq peak summits are not anymore aligned at position 0. \textbf {C} Corresponding DNaseI hypersensitivity measured by DNaseI-seq at the same loci and realigned as in B. \textbf {D} CTCF motif occurrences predicted using a motif scan, at the same loci and realigned as in B. Each predicted binding site, +/- 1kb around a peak, is represented as a point. \textbf {E} Transcription start site (TSS) density at the same loci and realigned as in B. \textbf {F} Cluster 1 (red) aggregation profiles. The original peak coordinates were modified accordingly to the shift and flip values returned by SPar-K and the read densities the different data types were measured using ChIP-Cor \citep {ambrosini_chip-seq_2016}. For the TSSs and the transcription initiation (CAGE), only the data mapping on the negative strand were used to monitor transcription firing towards the nucleosome array (towards the left). \textbf {G} Proportions of regions having at least one CTCF motif +/- 1kb (same motifs as in D), for each cluster. \textbf {H} Proportions of regions having at least one TSS +/- 1kb (same TSSs as in E), for each cluster.\relax }}{64}{figure.caption.27}} +\newlabel{spark_ctcf}{{4.5}{64}{Nucleosome occupancy, determined by MNase-seq, in bins of 10bp, +/- 1000bp around 79'957 CTCF binding sites in GM12878 cells. \textbf {A} MNaseI-seq read density around the CTCF binding sites. ChIP-seq peak summits are aligned at position 0. The regions (rows) are ordered according the their resemblance (correlation) to the overall aggregation pattern. \textbf {B} SPar-K data partition. The number of clusters (4) was determined using the elbow method. The cluster labels are indicated by the color ribbons on the left. 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Clusters 1, 2 and 3 are indicated by colored bars in red, blue, and green, respectively. \textbf {C.} MNase-seq read densities for the same regions, ordered, aligned and oriented as in B. \textbf {D.} Predicted SP1 binding motifs for the same regions, ordered, aligned and oriented as in B. \textbf {E.} Proportion of binding sites within each cluster having a confirmed promoter-associated TSS within +/- 300bp. \textbf {F.} Aggregations profiles for DNase-seq (red), MNase-seq (blue), promoter TSS (green) and CAGE-seq data (violet) for cluster 2 (aligned and oriented as in B). \textbf {G.} Motifs found by MEME-ChIP and Tomtom in the narrow footprints of each cluster. (*) known SP1 interactor, (c) central enrichment. Cluster 2 left and right refer to the left and right footprints seen in \textbf {B}. Figure and legend taken and adapted from \citep {groux_spar-k:_2019}.\relax }}{65}{figure.caption.28}} +\newlabel{spark_dnase}{{4.6}{65}{Partitioning of DNaseI hypersensitivity profiles around SP1 binding sites in K562 cells. The optimal number of clusters was determined using the elbow method. \textbf {A.} Input data based on peak summits provided by ENCODE. \textbf {B.} Same regions clustered, re-aligned and oriented by SPar-K. Clusters 1, 2 and 3 are indicated by colored bars in red, blue, and green, respectively. \textbf {C.} MNase-seq read densities for the same regions, ordered, aligned and oriented as in B. \textbf {D.} Predicted SP1 binding motifs for the same regions, ordered, aligned and oriented as in B. \textbf {E.} Proportion of binding sites within each cluster having a confirmed promoter-associated TSS within +/- 300bp. \textbf {F.} Aggregations profiles for DNase-seq (red), MNase-seq (blue), promoter TSS (green) and CAGE-seq data (violet) for cluster 2 (aligned and oriented as in B). \textbf {G.} Motifs found by MEME-ChIP and Tomtom in the narrow footprints of each cluster. (*) known SP1 interactor, (c) central enrichment. Cluster 2 left and right refer to the left and right footprints seen in \textbf {B}. 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(/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. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemNucleophile with sig. 'mm' on line 111. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemNucleophile with sig. 'mm' on line 111. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemNucleophile with sig. 'mm' on line 111. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \Nuc with sig. 'o' on line 130. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ba with sig. '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" . . Defining command \NewChemIUPACShorthand with sig. 'mm' on line 611. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemIUPACShorthand with sig. 'mm' on line 617. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemIUPACShorthand with sig. 'mm' on line 624. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RemoveChemIUPACShorthand with sig. 'm' on line 627. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \iupac with sig. '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'. ................................................. (/usr/share/texlive/texmf-dist/tex/latex/chemformula/chemformula.sty (/usr/share/texlive/texmf-dist/tex/latex/l3packages/xfrac/xfrac.sty (/usr/share/texlive/texmf-dist/tex/latex/l3packages/xtemplate/xtemplate.sty Package: xtemplate 2018/02/21 L3 Experimental prototype document functions \l__xtemplate_tmp_dim=\dimen322 \l__xtemplate_tmp_int=\count341 \l__xtemplate_tmp_muskip=\muskip18 \l__xtemplate_tmp_skip=\skip103 ) Package: xfrac 2018/02/21 L3 Experimental split-level fractions \l__xfrac_slash_box=\box74 \l__xfrac_tmp_box=\box75 \l__xfrac_denominator_bot_sep_dim=\dimen323 \l__xfrac_numerator_bot_sep_dim=\dimen324 \l__xfrac_numerator_top_sep_dim=\dimen325 \l__xfrac_slash_left_sep_dim=\dimen326 \l__xfrac_slash_right_sep_dim=\dimen327 \l__xfrac_slash_left_muskip=\muskip19 \l__xfrac_slash_right_muskip=\muskip20 ................................................. . xtemplate info: "declare-object-type" . . 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 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \charrow with sig. 'mO{}O{}' on line 823. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemArrow with sig. 'mm' on line 896. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemArrow with sig. 'mm' on line 904. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemArrow with sig. 'mm' on line 911. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemArrow with sig. 'mm' on line 921. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ShowChemArrow with sig. 'm' on line 931. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ch with sig. 'O{}m' on line 1176. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chcpd with sig. 'O{}m' on line 1198. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chname with sig. 'R(){}R(){}' on line 1276. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemCompoundProperty with sig. 'mm' on line 1361. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemCompoundProperty with sig. 'mm' on line 1364. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemCompoundProperty with sig. 'mm' on line 1367. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemCompoundProperty with sig. 'mm' on line 1370. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RemoveChemCompoundProperty with sig. 'm' on line 1373. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemBond with sig. 'mm' on line 1571. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemBond with sig. 'mm' on line 1574. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemBond with sig. 'mm' on line 1577. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemBond with sig. 'mm' on line 1580. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemBondAlias with sig. 'mm' on line 1583. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemBondAlias with sig. 'mm' on line 1586. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ShowChemBond with sig. 'm' on line 1589. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \bond with sig. 'm' on line 1592. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chstoich with sig. 'm' on line 2191. ................................................. \l__chemformula_additions_symbol_space_skip=\skip109 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemAdditionSymbol with sig. 'mmm' on line 2697. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemAdditionSymbol with sig. 'mmm' on line 2706. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemAdditionSymbol with sig. 'mmm' on line 2715. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemAdditionSymbol with sig. 'mmm' on line 2718. ................................................. \l__chemformula_plus_space_skip=\skip110 \l__chemformula_minus_space_skip=\skip111 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemSymbol with sig. 'mm' on line 2763. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemSymbol with sig. 'mm' on line 2769. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemSymbol with sig. 'mm' on line 2776. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemSymbol with sig. 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{chapter}{Acknowledgements}{i}{chapter*.1} -\contentsline {chapter}{Preface}{iii}{chapter*.2} -\contentsline {chapter}{Abstract (English/Fran\IeC {\c c}ais/Deutsch)}{v}{chapter*.3} -\babel@toc {german}{} -\babel@toc {english}{} +\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 nucleosome occupancy}{12}{subsection.1.4.1} \contentsline {subsection}{\numberline {1.4.2}Digital footprinting}{13}{subsection.1.4.2} \contentsline {subsection}{\numberline {1.4.3}Measuring TF binding in vivo}{15}{subsection.1.4.3} \contentsline {subsection}{\numberline {1.4.4}Measuring TF binding in vitro}{16}{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}{19}{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}{23}{chapter.2} \contentsline {chapter}{Laboratory resources}{23}{chapter.2} \contentsline {section}{\numberline {2.1}Mass Genome Annotation repository}{23}{section.2.1} \contentsline {subsection}{\numberline {2.1.1}MGA content and organization}{24}{subsection.2.1.1} \contentsline {subsection}{\numberline {2.1.2}Conclusions}{25}{subsection.2.1.2} \contentsline {section}{\numberline {2.2}Eukaryotic Promoter Database}{26}{section.2.2} \contentsline {subsection}{\numberline {2.2.1}EPDnew now annotates (some of) your mushrooms and vegetables}{27}{subsection.2.2.1} \contentsline {subsection}{\numberline {2.2.2}Increased mapping precision in human}{28}{subsection.2.2.2} \contentsline {subsection}{\numberline {2.2.3}Integration of EPDnew with other resources}{28}{subsection.2.2.3} \contentsline {subsection}{\numberline {2.2.4}Conclusions}{29}{subsection.2.2.4} \contentsline {subsection}{\numberline {2.2.5}Methods}{29}{subsection.2.2.5} \contentsline {subsubsection}{Motif occurrence profiles}{29}{subsection.2.2.5} \contentsline {chapter}{\numberline {3}ENCODE peaks analysis}{31}{chapter.3} \contentsline {chapter}{ENCODE peaks analysis}{31}{chapter.3} \contentsline {section}{\numberline {3.1}Data}{31}{section.3.1} \contentsline {section}{\numberline {3.2}ChIPPartitioning : an algorithm to identify chromatin architectures}{33}{section.3.2} \contentsline {subsection}{\numberline {3.2.1}Data realignment}{34}{subsection.3.2.1} \contentsline {section}{\numberline {3.3}Nucleosome organization around transcription factor binding sites}{35}{section.3.3} \contentsline {section}{\numberline {3.4}The case of CTCF, RAD21, SMC3, YY1 and ZNF143}{37}{section.3.4} \contentsline {section}{\numberline {3.5}CTCF and JunD interactomes}{41}{section.3.5} \contentsline {section}{\numberline {3.6}EBF1 binds nucleosomes}{45}{section.3.6} \contentsline {section}{\numberline {3.7}Discussion}{48}{section.3.7} \contentsline {section}{\numberline {3.8}Methods}{48}{section.3.8} \contentsline {subsection}{\numberline {3.8.1}Data and data processing}{48}{subsection.3.8.1} \contentsline {subsection}{\numberline {3.8.2}Classification of MNase patterns}{49}{subsection.3.8.2} \contentsline {subsection}{\numberline {3.8.3}Quantifying nucleosome array intensity from classification results}{50}{subsection.3.8.3} \contentsline {subsection}{\numberline {3.8.4}Peak colocalization}{51}{subsection.3.8.4} \contentsline {subsection}{\numberline {3.8.5}NDR detection}{52}{subsection.3.8.5} \contentsline {subsection}{\numberline {3.8.6}CTCF and JunD interactors}{54}{subsection.3.8.6} \contentsline {subsection}{\numberline {3.8.7}EBF1 and nucleosome}{55}{subsection.3.8.7} \contentsline {chapter}{\numberline {4}SPar-K}{57}{chapter.4} \contentsline {section}{\numberline {4.1}Algorithm}{57}{section.4.1} \contentsline {section}{\numberline {4.2}Implementation}{58}{section.4.2} \contentsline {section}{\numberline {4.3}Benchmarking}{59}{section.4.3} \contentsline {subsection}{\numberline {4.3.1}K-means}{59}{subsection.4.3.1} \contentsline {subsection}{\numberline {4.3.2}ChIPPartitioning}{62}{subsection.4.3.2} \contentsline {subsection}{\numberline {4.3.3}Data}{62}{subsection.4.3.3} \contentsline {subsection}{\numberline {4.3.4}Performances}{63}{subsection.4.3.4} \contentsline {section}{\numberline {4.4}Partition of DNase and MNase data}{63}{section.4.4} \contentsline {section}{\numberline {4.5}Conclusions}{63}{section.4.5} \contentsline {chapter}{\numberline {5}SMiLE-seq data analysis}{67}{chapter.5} \contentsline {chapter}{SMiLE-seq data analysis}{67}{chapter.5} \contentsline {section}{\numberline {5.1}Introduction}{67}{section.5.1} \contentsline {section}{\numberline {5.2}Hidden Markov Model Motif discovery}{69}{section.5.2} \contentsline {section}{\numberline {5.3}Binding motif evaluation}{70}{section.5.3} \contentsline {section}{\numberline {5.4}Results}{71}{section.5.4} \contentsline {section}{\numberline {5.5}Conclusions}{73}{section.5.5} \contentsline {chapter}{\numberline {6}PWMScan}{75}{chapter.6} \contentsline {section}{\numberline {6.1}Algorithms}{75}{section.6.1} \contentsline {subsection}{\numberline {6.1.1}Scanner algorithm}{76}{subsection.6.1.1} \contentsline {subsection}{\numberline {6.1.2}Matches enumeration and mapping}{76}{subsection.6.1.2} \contentsline {section}{\numberline {6.2}PMWScan architecture}{77}{section.6.2} \contentsline {section}{\numberline {6.3}Benchmark}{79}{section.6.3} \contentsline {section}{\numberline {6.4}Conclusions}{81}{section.6.4} \contentsline {chapter}{\numberline {7}Chromatin accessibility of monocytes}{83}{chapter.7} \contentsline {section}{\numberline {7.1}Monitoring TF binding}{83}{section.7.1} \contentsline {section}{\numberline {7.2}The advent of single cell DGF}{84}{section.7.2} \contentsline {section}{\numberline {7.3}Open issues}{84}{section.7.3} \contentsline {section}{\numberline {7.4}Data}{84}{section.7.4} \contentsline {section}{\numberline {7.5}Identifying over-represented signals}{85}{section.7.5} \contentsline {subsection}{\numberline {7.5.1}ChIPPartitioning algorithm}{85}{subsection.7.5.1} \contentsline {subsection}{\numberline {7.5.2}EMSequence algorithm}{85}{subsection.7.5.2} -\contentsline {subsubsection}{without shift and flip}{87}{figure.caption.37} +\contentsline {subsubsection}{without shift and flip}{87}{figure.caption.35} \contentsline {subsubsection}{with shift and flip}{87}{equation.7.5.2} \contentsline {subsection}{\numberline {7.5.3}EMJoint algorithm}{89}{subsection.7.5.3} \contentsline {subsection}{\numberline {7.5.4}Data realignment}{90}{subsection.7.5.4} \contentsline {section}{\numberline {7.6}Results}{90}{section.7.6} \contentsline {subsection}{\numberline {7.6.1}Data processing}{90}{subsection.7.6.1} \contentsline {subsection}{\numberline {7.6.2}Aligning the binding sites}{91}{subsection.7.6.2} \contentsline {subsection}{\numberline {7.6.3}Exploring individual TF classes}{93}{subsection.7.6.3} \contentsline {section}{\numberline {7.7}Discussions}{94}{section.7.7} \contentsline {section}{\numberline {7.8}Perspectives}{94}{section.7.8} \contentsline {section}{\numberline {7.9}Methods}{95}{section.7.9} \contentsline {subsection}{\numberline {7.9.1}Data sources}{95}{subsection.7.9.1} \contentsline {subsection}{\numberline {7.9.2}Data post-processing}{96}{subsection.7.9.2} \contentsline {subsection}{\numberline {7.9.3}Model extension}{96}{subsection.7.9.3} \contentsline {subsection}{\numberline {7.9.4}Extracting data assigned to a class}{97}{subsection.7.9.4} \contentsline {subsection}{\numberline {7.9.5}Programs}{99}{subsection.7.9.5} \contentsline {subsection}{\numberline {7.9.6}Fragment classes}{100}{subsection.7.9.6} \contentsline {subsection}{\numberline {7.9.7}Simulated sequences}{101}{subsection.7.9.7} \contentsline {subsection}{\numberline {7.9.8}Binding site prediction}{101}{subsection.7.9.8} \contentsline {subsection}{\numberline {7.9.9}Realignment using JASPAR motifs}{101}{subsection.7.9.9} \contentsline {subsection}{\numberline {7.9.10}Per TF sub-classes}{104}{subsection.7.9.10} \contentsline {chapter}{\numberline {8}Discussion}{107}{chapter.8} \contentsline {chapter}{Discussions}{107}{chapter.8} \vspace {\normalbaselineskip } \contentsline {chapter}{\numberline {A}Supplementary material}{111}{appendix.A} \contentsline {section}{\numberline {A.1}ENCODE peaks analysis supplementary material}{112}{section.A.1} \contentsline {section}{\numberline {A.2}SPar-K supplementary material}{122}{section.A.2} \contentsline {section}{\numberline {A.3}SMiLE-seq supplementary material}{135}{section.A.3} \contentsline {section}{\numberline {A.4}Chromatin accessibility of monocytes supplementary material}{135}{section.A.4} \contentsline {subsection}{\numberline {A.4.1}Fragment size analysis}{135}{subsection.A.4.1} \contentsline {subsection}{\numberline {A.4.2}Measuring open chromatin and nucleosome occupancy}{136}{subsection.A.4.2} \contentsline {subsection}{\numberline {A.4.3}Evaluation of EMSequence and ChIPPartitioning}{139}{subsection.A.4.3} \contentsline {subsubsection}{EMSequence}{139}{subsection.A.4.3} -\contentsline {subsubsection}{ChIPPartitioning}{142}{figure.caption.58} +\contentsline 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The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }}{116}{figure.caption.45}} -\newlabel{suppl_encode_peaks_em_brca1}{{A.5}{116}{\textbf {Chromatine architectures around BRCA1 binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }{figure.caption.45}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.6}{\ignorespaces \textbf {Nucleosome occupancy around CTCF peaks } measured by MNase-seq, in bins of 10bp. The nucleosome depleted region is displayed in blue.\relax }}{117}{figure.caption.46}} -\newlabel{suppl_encode_peaks_ctcf_ndr}{{A.6}{117}{\textbf {Nucleosome occupancy around CTCF peaks } measured by MNase-seq, in bins of 10bp. The nucleosome depleted region is displayed in blue.\relax }{figure.caption.46}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.1}{\ignorespaces \textbf {Chromatine architectures around CTCF binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }}{112}{figure.caption.39}} +\newlabel{suppl_encode_peaks_em_ctcf}{{A.1}{112}{\textbf {Chromatine architectures around CTCF binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }{figure.caption.39}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.2}{\ignorespaces \textbf {Chromatine architectures around NRF1 binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }}{113}{figure.caption.40}} +\newlabel{suppl_encode_peaks_em_nrf1}{{A.2}{113}{\textbf {Chromatine architectures around NRF1 binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }{figure.caption.40}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.3}{\ignorespaces \textbf {Chromatine architectures around cFOS binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }}{114}{figure.caption.41}} +\newlabel{suppl_encode_peaks_em_cfos}{{A.3}{114}{\textbf {Chromatine architectures around cFOS binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }{figure.caption.41}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.4}{\ignorespaces \textbf {Chromatine architectures around max binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }}{115}{figure.caption.42}} +\newlabel{suppl_encode_peaks_em_max}{{A.4}{115}{\textbf {Chromatine architectures around max binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }{figure.caption.42}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.5}{\ignorespaces \textbf {Chromatine architectures around BRCA1 binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }}{116}{figure.caption.43}} +\newlabel{suppl_encode_peaks_em_brca1}{{A.5}{116}{\textbf {Chromatine architectures around BRCA1 binding sites} discovered using ChIPPartitioning. The partition was done with respect to the MNase reads (red), +/- 1kb around the peaks, in bins of 10bp, that were allowed to be shifted and flipped. DNaseI (blue), TSS density (violet) and sequence conservation (green) were realigned according to MNase classification and overlaid. The y-axis scale represent the proportion of the highest signal for each chromatin pattern. The first row contains the aggregated signal over all sites. The number of binding sites (peaks) is indicated in parenthesis. The following rows contains the 4 classes discovered. Their overall probability is indicated atop of the class signal, on the right. The y-axis indicates the min/max signal for all densities.\relax }{figure.caption.43}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.6}{\ignorespaces \textbf {Nucleosome occupancy around CTCF peaks } measured by MNase-seq, in bins of 10bp. The nucleosome depleted region is displayed in blue.\relax }}{117}{figure.caption.44}} +\newlabel{suppl_encode_peaks_ctcf_ndr}{{A.6}{117}{\textbf {Nucleosome occupancy around CTCF peaks } measured by MNase-seq, in bins of 10bp. The nucleosome depleted region is displayed in blue.\relax }{figure.caption.44}{}} \citation{khan_jaspar_2018} \citation{khan_jaspar_2018} -\@writefile{lof}{\contentsline {figure}{\numberline {A.7}{\ignorespaces \textbf {JunD motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The JunD and cFos dataset ORs are too high to be represented in this plot. \textbf {B} Density of JunD motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif.\relax }}{118}{figure.caption.47}} -\newlabel{suppl_encode_peaks_jund_association}{{A.7}{118}{\textbf {JunD motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The JunD and cFos dataset ORs are too high to be represented in this plot. \textbf {B} Density of JunD motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif.\relax }{figure.caption.47}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.8}{\ignorespaces \textbf {EBF1 binding sites} around the dyad of nucleosomes having an occupied EBF1 motif within 100bp (in red) and of all nucleosomes (in blue). The abrupt decrease of EBF1 motif frequency at +/- 100bp reflects the nucleosome selection process.\relax }}{119}{figure.caption.48}} -\newlabel{suppl_encode_peaks_ebf1_nucl}{{A.8}{119}{\textbf {EBF1 binding sites} around the dyad of nucleosomes having an occupied EBF1 motif within 100bp (in red) and of all nucleosomes (in blue). The abrupt decrease of EBF1 motif frequency at +/- 100bp reflects the nucleosome selection process.\relax }{figure.caption.48}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.9}{\ignorespaces \textbf {EBF1 logo} from JASPAR binding model MA0154.3 \citep {khan_jaspar_2018}.\relax }}{119}{figure.caption.49}} -\newlabel{suppl_encode_peaks_ebf1_logo}{{A.9}{119}{\textbf {EBF1 logo} from JASPAR binding model MA0154.3 \citep {khan_jaspar_2018}.\relax }{figure.caption.49}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.10}{\ignorespaces \textbf {EBF1 binding sites} chromatin features. \textbf {A} Chromatin accessibility around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {B} H3K4me2 deposition around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {C} Sequence conservation around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue).\relax }}{120}{figure.caption.50}} -\newlabel{suppl_encode_peaks_ebf1_chrom}{{A.10}{120}{\textbf {EBF1 binding sites} chromatin features. \textbf {A} Chromatin accessibility around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {B} H3K4me2 deposition around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {C} Sequence conservation around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue).\relax }{figure.caption.50}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.7}{\ignorespaces \textbf {JunD motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The JunD and cFos dataset ORs are too high to be represented in this plot. \textbf {B} Density of JunD motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif.\relax }}{118}{figure.caption.45}} +\newlabel{suppl_encode_peaks_jund_association}{{A.7}{118}{\textbf {JunD motif association} measured around the binding sites of different TFs. For a each TF, its binding sites, +/- 500bp, were searched for the presence of i) the TF motif and ii) CTCF motif. For each TF, a 2x2 contingency table was created with the number of peaks having i) both motifs, ii) the TF motif only, iii) CTCF motif only and iv) no motif. \textbf {A} Odd ratio (OR) of the exact Fisher test performed on each TF contingency table. The ORs are displayed with their 95\% confidence interval (CI). ORs > 1 - that is, with 1 not part of the 95\%CI - are labeled in green and indicate an association of both motifs more frequent than expected by chance. ORs < 1 are labeled in red and indicate a repulsion of both motifs more frequence than expected by chance. The JunD and cFos dataset ORs are too high to be represented in this plot. \textbf {B} Density of JunD motif occurrence at the absolute distance of different TF binding sites (peak centers) which also have their own motif present (at distance 0). The rows were standardized and aggregated using the Euclidean distance. \textbf {C} Same as in (B) but for TF binding sites that does not have their own motif.\relax }{figure.caption.45}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.8}{\ignorespaces \textbf {EBF1 binding sites} around the dyad of nucleosomes having an occupied EBF1 motif within 100bp (in red) and of all nucleosomes (in blue). The abrupt decrease of EBF1 motif frequency at +/- 100bp reflects the nucleosome selection process.\relax }}{119}{figure.caption.46}} +\newlabel{suppl_encode_peaks_ebf1_nucl}{{A.8}{119}{\textbf {EBF1 binding sites} around the dyad of nucleosomes having an occupied EBF1 motif within 100bp (in red) and of all nucleosomes (in blue). The abrupt decrease of EBF1 motif frequency at +/- 100bp reflects the nucleosome selection process.\relax }{figure.caption.46}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.9}{\ignorespaces \textbf {EBF1 logo} from JASPAR binding model MA0154.3 \citep {khan_jaspar_2018}.\relax }}{119}{figure.caption.47}} +\newlabel{suppl_encode_peaks_ebf1_logo}{{A.9}{119}{\textbf {EBF1 logo} from JASPAR binding model MA0154.3 \citep {khan_jaspar_2018}.\relax }{figure.caption.47}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.10}{\ignorespaces \textbf {EBF1 binding sites} chromatin features. \textbf {A} Chromatin accessibility around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {B} H3K4me2 deposition around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {C} Sequence conservation around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue).\relax }}{120}{figure.caption.48}} +\newlabel{suppl_encode_peaks_ebf1_chrom}{{A.10}{120}{\textbf {EBF1 binding sites} chromatin features. \textbf {A} Chromatin accessibility around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {B} H3K4me2 deposition around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue). \textbf {C} Sequence conservation around nucleosomes that have an EBF1 binding site within 100bp (red) and all nucleosomes (blue).\relax }{figure.caption.48}{}} \@writefile{toc}{\contentsline {section}{\numberline {A.2}SPar-K supplementary material}{122}{section.A.2}} \newlabel{algo_spark}{{3}{122}{SPar-K supplementary material}{algocfline.3}{}} \@writefile{loa}{\contentsline {algocf}{\numberline {3}{\ignorespaces SPar-K algorithm.\relax }}{122}{algocf.3}} \newlabel{algo_smooth_outliers}{{4}{123}{SPar-K supplementary material}{algocfline.4}{}} \@writefile{loa}{\contentsline {algocf}{\numberline {4}{\ignorespaces Smooth the data matrix by removing outliers.\relax }}{123}{algocf.4}} \newlabel{algo_distance_fast}{{5}{125}{SPar-K supplementary material}{algocfline.5}{}} \@writefile{loa}{\contentsline {algocf}{\numberline {5}{\ignorespaces Fast algorithm to compute the correlation distance with shift and flip\relax }}{125}{algocf.5}} \newlabel{initialize_algo}{{6}{126}{SPar-K supplementary material}{algocfline.6}{}} \@writefile{loa}{\contentsline {algocf}{\numberline {6}{\ignorespaces A routine of distanceFast() that initializes all the necessary variables. This function can access and modify variables in distanceFast().\relax }}{126}{algocf.6}} \@writefile{loa}{\contentsline {algocf}{\numberline {7}{\ignorespaces A routine of distanceFast() computing all distances with $X$ having a shift of 0. This function can access and modify all variables declared in distanceFast().\relax }}{128}{algocf.7}} \@writefile{loa}{\contentsline {algocf}{\numberline {8}{\ignorespaces A routine of distanceFast() computing all distances with $Y$ having a shift of 0. This function is can access and modify all variables declared in distanceFast().\relax }}{130}{algocf.8}} \@writefile{loa}{\contentsline {algocf}{\numberline {9}{\ignorespaces A routine of distanceFast() computing all remaining distances between $X$ and $Y$. This function can access and modify all variables declared in distanceFast().\relax }}{132}{algocf.9}} \newlabel{algo_seed_random}{{10}{133}{SPar-K supplementary material}{algocfline.10}{}} \@writefile{loa}{\contentsline {algocf}{\numberline {10}{\ignorespaces Random seeding algorithm\relax }}{133}{algocf.10}} \citation{jolma_dna-binding_2013} \citation{jolma_dna-binding_2013} \newlabel{algo_seed_kmeans++}{{11}{134}{SPar-K supplementary material}{algocfline.11}{}} \@writefile{loa}{\contentsline {algocf}{\numberline {11}{\ignorespaces Kmeans++ seeding algorithm.\relax }}{134}{algocf.11}} \citation{buenrostro_transposition_2013} \citation{buenrostro_transposition_2013} \@writefile{toc}{\contentsline {section}{\numberline {A.3}SMiLE-seq supplementary material}{135}{section.A.3}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.11}{\ignorespaces \textbf {Predictive power of SMiLE-seq :} \textbf {A} binding models were derived de novo from HT-SELEX 1st cycle data using the HMM discovery method (labelled HT-SELEX cycle 1 HMM) and their performances were assessed using the AUC-ROC. AUC-ROC values for the corresponding TF models derived from SMiLe-seq data (labelled SMiLE-seq) and reported by Jolma and colleagues (labelled HT-SELEX reported matrices, \cite {jolma_dna-binding_2013}) are also displayed. \textbf {B} the predictive performances of CEBPb, CTCF and TCF7 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. The boxplots indicate the distribution of AUC-ROC obtained over all available peak-lists.\relax }}{135}{figure.caption.51}} -\newlabel{suppl_smileseq_auc_2}{{A.11}{135}{\textbf {Predictive power of SMiLE-seq :} \textbf {A} binding models were derived de novo from HT-SELEX 1st cycle data using the HMM discovery method (labelled HT-SELEX cycle 1 HMM) and their performances were assessed using the AUC-ROC. AUC-ROC values for the corresponding TF models derived from SMiLe-seq data (labelled SMiLE-seq) and reported by Jolma and colleagues (labelled HT-SELEX reported matrices, \cite {jolma_dna-binding_2013}) are also displayed. \textbf {B} the predictive performances of CEBPb, CTCF and TCF7 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. The boxplots indicate the distribution of AUC-ROC obtained over all available peak-lists.\relax }{figure.caption.51}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.11}{\ignorespaces \textbf {Predictive power of SMiLE-seq :} \textbf {A} binding models were derived de novo from HT-SELEX 1st cycle data using the HMM discovery method (labelled HT-SELEX cycle 1 HMM) and their performances were assessed using the AUC-ROC. AUC-ROC values for the corresponding TF models derived from SMiLe-seq data (labelled SMiLE-seq) and reported by Jolma and colleagues (labelled HT-SELEX reported matrices, \cite {jolma_dna-binding_2013}) are also displayed. \textbf {B} the predictive performances of CEBPb, CTCF and TCF7 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. The boxplots indicate the distribution of AUC-ROC obtained over all available peak-lists.\relax }}{135}{figure.caption.49}} +\newlabel{suppl_smileseq_auc_2}{{A.11}{135}{\textbf {Predictive power of SMiLE-seq :} \textbf {A} binding models were derived de novo from HT-SELEX 1st cycle data using the HMM discovery method (labelled HT-SELEX cycle 1 HMM) and their performances were assessed using the AUC-ROC. AUC-ROC values for the corresponding TF models derived from SMiLe-seq data (labelled SMiLE-seq) and reported by Jolma and colleagues (labelled HT-SELEX reported matrices, \cite {jolma_dna-binding_2013}) are also displayed. \textbf {B} the predictive performances of CEBPb, CTCF and TCF7 binding models were assessed using subsets of binding sites of decreasing affinities. Inside each peak list, the peaks were ranked by score and subsets of 500 peaks were selected. Peaks 1-500 have the highest affinity, then peaks 501-1000, and so on. The boxplots indicate the distribution of AUC-ROC obtained over all available peak-lists.\relax }{figure.caption.49}{}} \@writefile{toc}{\contentsline {section}{\numberline {A.4}Chromatin accessibility of monocytes supplementary material}{135}{section.A.4}} \@writefile{toc}{\contentsline {subsection}{\numberline {A.4.1}Fragment size analysis}{135}{subsection.A.4.1}} \newlabel{suppl_atac_seq_fragment_size}{{A.4.1}{135}{Fragment size analysis}{subsection.A.4.1}{}} \citation{adey_rapid_2010} \citation{buenrostro_transposition_2013,li_identification_2019} -\@writefile{lof}{\contentsline {figure}{\numberline {A.12}{\ignorespaces \textbf {Fragment size analysis} \textbf {A} sequenced fragment size density. The three peaks, from left to right, indicate i) the open chromatin fragments, ii) the mono-nucleosome fragments and iii) the di-nucleosome fragments. A mixture model composed of three Gaussian distributions was fitted to the data in order to model the fragment sizes. The class fit is shown as dashed lines : open chromatin (red), mono-nucleosomes (blue) and di-nucleosomes (green). The violet dashed line show the sum of the three classes. \textbf {B :} probability that a fragment belongs to any of the three fragment classes, given its size i) open chromatin (red), ii) mono-nucleosomes (blue) and iii) di-nucleosomes (green). The vertical dashed lines indicates, for each class, the size limit at which the class probability drops below 0.9. With these limites, the class spans are i) 30-84bp for open chromatin (red), ii) 133-266bp for mono-nucleosomes (blue) and iii) 341-500bp for di-nucleosomes (green). The upper limit of the di-nucleosome class was arbitrarily set to 500bp. \textbf {C :} final fragment classes. Each fragments which size overlapped the size range spanned by a class, was assigned to that class. This ensured a high confidence assignment for more than 134 million fragments, leaving 46 millions of ambiguous and long fragments (>500bp) unassigned.\relax }}{136}{figure.caption.52}} -\newlabel{atac_seq_fragment_size}{{A.12}{136}{\textbf {Fragment size analysis} \textbf {A} sequenced fragment size density. The three peaks, from left to right, indicate i) the open chromatin fragments, ii) the mono-nucleosome fragments and iii) the di-nucleosome fragments. A mixture model composed of three Gaussian distributions was fitted to the data in order to model the fragment sizes. The class fit is shown as dashed lines : open chromatin (red), mono-nucleosomes (blue) and di-nucleosomes (green). The violet dashed line show the sum of the three classes. \textbf {B :} probability that a fragment belongs to any of the three fragment classes, given its size i) open chromatin (red), ii) mono-nucleosomes (blue) and iii) di-nucleosomes (green). The vertical dashed lines indicates, for each class, the size limit at which the class probability drops below 0.9. With these limites, the class spans are i) 30-84bp for open chromatin (red), ii) 133-266bp for mono-nucleosomes (blue) and iii) 341-500bp for di-nucleosomes (green). The upper limit of the di-nucleosome class was arbitrarily set to 500bp. \textbf {C :} final fragment classes. Each fragments which size overlapped the size range spanned by a class, was assigned to that class. This ensured a high confidence assignment for more than 134 million fragments, leaving 46 millions of ambiguous and long fragments (>500bp) unassigned.\relax }{figure.caption.52}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.12}{\ignorespaces \textbf {Fragment size analysis} \textbf {A} sequenced fragment size density. The three peaks, from left to right, indicate i) the open chromatin fragments, ii) the mono-nucleosome fragments and iii) the di-nucleosome fragments. A mixture model composed of three Gaussian distributions was fitted to the data in order to model the fragment sizes. The class fit is shown as dashed lines : open chromatin (red), mono-nucleosomes (blue) and di-nucleosomes (green). The violet dashed line show the sum of the three classes. \textbf {B :} probability that a fragment belongs to any of the three fragment classes, given its size i) open chromatin (red), ii) mono-nucleosomes (blue) and iii) di-nucleosomes (green). The vertical dashed lines indicates, for each class, the size limit at which the class probability drops below 0.9. With these limites, the class spans are i) 30-84bp for open chromatin (red), ii) 133-266bp for mono-nucleosomes (blue) and iii) 341-500bp for di-nucleosomes (green). The upper limit of the di-nucleosome class was arbitrarily set to 500bp. \textbf {C :} final fragment classes. Each fragments which size overlapped the size range spanned by a class, was assigned to that class. This ensured a high confidence assignment for more than 134 million fragments, leaving 46 millions of ambiguous and long fragments (>500bp) unassigned.\relax }}{136}{figure.caption.50}} +\newlabel{atac_seq_fragment_size}{{A.12}{136}{\textbf {Fragment size analysis} \textbf {A} sequenced fragment size density. The three peaks, from left to right, indicate i) the open chromatin fragments, ii) the mono-nucleosome fragments and iii) the di-nucleosome fragments. A mixture model composed of three Gaussian distributions was fitted to the data in order to model the fragment sizes. The class fit is shown as dashed lines : open chromatin (red), mono-nucleosomes (blue) and di-nucleosomes (green). The violet dashed line show the sum of the three classes. \textbf {B :} probability that a fragment belongs to any of the three fragment classes, given its size i) open chromatin (red), ii) mono-nucleosomes (blue) and iii) di-nucleosomes (green). The vertical dashed lines indicates, for each class, the size limit at which the class probability drops below 0.9. With these limites, the class spans are i) 30-84bp for open chromatin (red), ii) 133-266bp for mono-nucleosomes (blue) and iii) 341-500bp for di-nucleosomes (green). The upper limit of the di-nucleosome class was arbitrarily set to 500bp. \textbf {C :} final fragment classes. Each fragments which size overlapped the size range spanned by a class, was assigned to that class. This ensured a high confidence assignment for more than 134 million fragments, leaving 46 millions of ambiguous and long fragments (>500bp) unassigned.\relax }{figure.caption.50}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {A.4.2}Measuring open chromatin and nucleosome occupancy}{136}{subsection.A.4.2}} \newlabel{suppl_atac_seq_measuring_signal}{{A.4.2}{136}{Measuring open chromatin and nucleosome occupancy}{subsection.A.4.2}{}} \citation{neph_expansive_2012} \citation{fu_insulator_2008} \citation{neph_expansive_2012} -\@writefile{lof}{\contentsline {figure}{\numberline {A.13}{\ignorespaces \textbf {Signal around CTCF motifs : } the human genome was scanned with a CTCF PWM and different aggregated signal densities were measured for open chromatin (red lines), mono nucleosome (blue lines), di-nucleosomes (green lines) and for a pool of mono-nucleosome fragments with di-nucleosomes fragments cut in two at their center position (violet line). \textbf {Top row :} each position of the fragments, from the start of the first read to the end of the second, were used. \textbf {Middle row :} each position of the reads were used. \textbf {Bottom row :} only one position at the read edges for open chromatin fragment and the central position of nucleosome fragment were used. The open chromatin read edges were modified by +4bp and -5bp for +strand and -strand reads respectively. The aggregated densities were measured using bin sizes of 1 (left column), 2 (middle column) and 10bp (right column).\relax }}{137}{figure.caption.53}} -\newlabel{atac_seq_ctcf_all_data}{{A.13}{137}{\textbf {Signal around CTCF motifs : } the human genome was scanned with a CTCF PWM and different aggregated signal densities were measured for open chromatin (red lines), mono nucleosome (blue lines), di-nucleosomes (green lines) and for a pool of mono-nucleosome fragments with di-nucleosomes fragments cut in two at their center position (violet line). \textbf {Top row :} each position of the fragments, from the start of the first read to the end of the second, were used. \textbf {Middle row :} each position of the reads were used. \textbf {Bottom row :} only one position at the read edges for open chromatin fragment and the central position of nucleosome fragment were used. The open chromatin read edges were modified by +4bp and -5bp for +strand and -strand reads respectively.\\ The aggregated densities were measured using bin sizes of 1 (left column), 2 (middle column) and 10bp (right column).\relax }{figure.caption.53}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.14}{\ignorespaces \textbf {Signal around CTCF, SP1, myc and EBF1 motifs :} the human genome was scanned with one PWM per TF to predict their binding sites (see section \ref {atac_seq_method_pwmscan}). For each TF, the open chromatin accessibility was measured (red) as well as and the nucleosome occupancy (blue) around their predicted binding sites. For the chromatin accessibility, the corrected read edges were considered and for nucleosomes, the center of the fragments. The motif location is indicated by the dashed lines.\relax }}{138}{figure.caption.54}} -\newlabel{atac_seq_ctcf_sp1_myc_ebf1_footprint}{{A.14}{138}{\textbf {Signal around CTCF, SP1, myc and EBF1 motifs :} the human genome was scanned with one PWM per TF to predict their binding sites (see section \ref {atac_seq_method_pwmscan}). For each TF, the open chromatin accessibility was measured (red) as well as and the nucleosome occupancy (blue) around their predicted binding sites. For the chromatin accessibility, the corrected read edges were considered and for nucleosomes, the center of the fragments. The motif location is indicated by the dashed lines.\relax }{figure.caption.54}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.13}{\ignorespaces \textbf {Signal around CTCF motifs : } the human genome was scanned with a CTCF PWM and different aggregated signal densities were measured for open chromatin (red lines), mono nucleosome (blue lines), di-nucleosomes (green lines) and for a pool of mono-nucleosome fragments with di-nucleosomes fragments cut in two at their center position (violet line). \textbf {Top row :} each position of the fragments, from the start of the first read to the end of the second, were used. \textbf {Middle row :} each position of the reads were used. \textbf {Bottom row :} only one position at the read edges for open chromatin fragment and the central position of nucleosome fragment were used. The open chromatin read edges were modified by +4bp and -5bp for +strand and -strand reads respectively. The aggregated densities were measured using bin sizes of 1 (left column), 2 (middle column) and 10bp (right column).\relax }}{137}{figure.caption.51}} +\newlabel{atac_seq_ctcf_all_data}{{A.13}{137}{\textbf {Signal around CTCF motifs : } the human genome was scanned with a CTCF PWM and different aggregated signal densities were measured for open chromatin (red lines), mono nucleosome (blue lines), di-nucleosomes (green lines) and for a pool of mono-nucleosome fragments with di-nucleosomes fragments cut in two at their center position (violet line). \textbf {Top row :} each position of the fragments, from the start of the first read to the end of the second, were used. \textbf {Middle row :} each position of the reads were used. \textbf {Bottom row :} only one position at the read edges for open chromatin fragment and the central position of nucleosome fragment were used. The open chromatin read edges were modified by +4bp and -5bp for +strand and -strand reads respectively.\\ The aggregated densities were measured using bin sizes of 1 (left column), 2 (middle column) and 10bp (right column).\relax }{figure.caption.51}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.14}{\ignorespaces \textbf {Signal around CTCF, SP1, myc and EBF1 motifs :} the human genome was scanned with one PWM per TF to predict their binding sites (see section \ref {atac_seq_method_pwmscan}). For each TF, the open chromatin accessibility was measured (red) as well as and the nucleosome occupancy (blue) around their predicted binding sites. For the chromatin accessibility, the corrected read edges were considered and for nucleosomes, the center of the fragments. The motif location is indicated by the dashed lines.\relax }}{138}{figure.caption.52}} +\newlabel{atac_seq_ctcf_sp1_myc_ebf1_footprint}{{A.14}{138}{\textbf {Signal around CTCF, SP1, myc and EBF1 motifs :} the human genome was scanned with one PWM per TF to predict their binding sites (see section \ref {atac_seq_method_pwmscan}). For each TF, the open chromatin accessibility was measured (red) as well as and the nucleosome occupancy (blue) around their predicted binding sites. For the chromatin accessibility, the corrected read edges were considered and for nucleosomes, the center of the fragments. The motif location is indicated by the dashed lines.\relax }{figure.caption.52}{}} \citation{kundaje_ubiquitous_2012} \citation{ou_motifstack_2018} \citation{ou_motifstack_2018} \@writefile{toc}{\contentsline {subsection}{\numberline {A.4.3}Evaluation of EMSequence and ChIPPartitioning}{139}{subsection.A.4.3}} \newlabel{suppl_eval_emseq_chippartitioning}{{A.4.3}{139}{Evaluation of EMSequence and ChIPPartitioning}{subsection.A.4.3}{}} \@writefile{toc}{\contentsline {subsubsection}{EMSequence}{139}{subsection.A.4.3}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.15}{\ignorespaces \textbf {Simulated data motifs :} motifs used for the data generation (labeled "True motif") and the best scoring - based on the AUC - partition motifs (labeled "Found motif"). The partition with EMSequence was run such that it was searching for motifs of 11bp, slightly longer than those used for the data generation. "RC" stands for reverse complement. The motifs tree and alignment was build using the motifStack R package \citep {ou_motifstack_2018}.\relax }}{140}{figure.caption.55}} -\newlabel{suppl_atac_seq_emseq_best_motifs}{{A.15}{140}{\textbf {Simulated data motifs :} motifs used for the data generation (labeled "True motif") and the best scoring - based on the AUC - partition motifs (labeled "Found motif"). The partition with EMSequence was run such that it was searching for motifs of 11bp, slightly longer than those used for the data generation. "RC" stands for reverse complement. The motifs tree and alignment was build using the motifStack R package \citep {ou_motifstack_2018}.\relax }{figure.caption.55}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.15}{\ignorespaces \textbf {Simulated data motifs :} motifs used for the data generation (labeled "True motif") and the best scoring - based on the AUC - partition motifs (labeled "Found motif"). The partition with EMSequence was run such that it was searching for motifs of 11bp, slightly longer than those used for the data generation. "RC" stands for reverse complement. The motifs tree and alignment was build using the motifStack R package \citep {ou_motifstack_2018}.\relax }}{140}{figure.caption.53}} +\newlabel{suppl_atac_seq_emseq_best_motifs}{{A.15}{140}{\textbf {Simulated data motifs :} motifs used for the data generation (labeled "True motif") and the best scoring - based on the AUC - partition motifs (labeled "Found motif"). The partition with EMSequence was run such that it was searching for motifs of 11bp, slightly longer than those used for the data generation. "RC" stands for reverse complement. The motifs tree and alignment was build using the motifStack R package \citep {ou_motifstack_2018}.\relax }{figure.caption.53}{}} \citation{kent_blatblast-like_2002} \citation{chatr-aryamontri_biogrid_2017} \citation{castro-mondragon_rsat_2017} -\@writefile{lof}{\contentsline {figure}{\numberline {A.16}{\ignorespaces \textbf {Classification performances on simulated data :} \textbf {Left} 50 different data partitions were run using EMSequence. The discovered models were then used to assign a class label to each sequence. These assigned labels were then compared to the true labels using the AUC under the ROC curve. The red line indicates the AUC value achieved by the true motifs. \textbf {Right} the 50 ROC curves corresponding to each partition. The red lines indicates the true motifs ROC curve. The curves under the diagonal are the cases where the 1st discovered class corresponded to the 2nd true class and vice-versa. For these cases, the AUC is actually the area over the curve.\relax }}{141}{figure.caption.56}} -\newlabel{suppl_atac_seq_emseq_auc_roc}{{A.16}{141}{\textbf {Classification performances on simulated data :} \textbf {Left} 50 different data partitions were run using EMSequence. The discovered models were then used to assign a class label to each sequence. These assigned labels were then compared to the true labels using the AUC under the ROC curve. The red line indicates the AUC value achieved by the true motifs. \textbf {Right} the 50 ROC curves corresponding to each partition. The red lines indicates the true motifs ROC curve. The curves under the diagonal are the cases where the 1st discovered class corresponded to the 2nd true class and vice-versa. For these cases, the AUC is actually the area over the curve.\relax }{figure.caption.56}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.16}{\ignorespaces \textbf {Classification performances on simulated data :} \textbf {Left} 50 different data partitions were run using EMSequence. The discovered models were then used to assign a class label to each sequence. These assigned labels were then compared to the true labels using the AUC under the ROC curve. The red line indicates the AUC value achieved by the true motifs. \textbf {Right} the 50 ROC curves corresponding to each partition. The red lines indicates the true motifs ROC curve. The curves under the diagonal are the cases where the 1st discovered class corresponded to the 2nd true class and vice-versa. For these cases, the AUC is actually the area over the curve.\relax }}{141}{figure.caption.54}} +\newlabel{suppl_atac_seq_emseq_auc_roc}{{A.16}{141}{\textbf {Classification performances on simulated data :} \textbf {Left} 50 different data partitions were run using EMSequence. The discovered models were then used to assign a class label to each sequence. These assigned labels were then compared to the true labels using the AUC under the ROC curve. The red line indicates the AUC value achieved by the true motifs. \textbf {Right} the 50 ROC curves corresponding to each partition. The red lines indicates the true motifs ROC curve. The curves under the diagonal are the cases where the 1st discovered class corresponded to the 2nd true class and vice-versa. For these cases, the AUC is actually the area over the curve.\relax }{figure.caption.54}{}} \citation{nair_probabilistic_2014} -\@writefile{lof}{\contentsline {figure}{\numberline {A.17}{\ignorespaces \textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates tandem arrangements of SP1 motifs.\relax }}{142}{figure.caption.57}} -\newlabel{suppl_atac_seq_emseq_sp1_7class}{{A.17}{142}{\textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates tandem arrangements of SP1 motifs.\relax }{figure.caption.57}{}} -\@writefile{toc}{\contentsline {subsubsection}{ChIPPartitioning}{142}{figure.caption.58}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.18}{\ignorespaces \textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. These sequences were classified by EMSequence to search for 10 different 30bp long motifs ($801 - 30 = 771$ of shifting freedom). The optimization was run for 20 iterations. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates head-to-tail arrangements of SP1 motifs.\relax }}{143}{figure.caption.58}} -\newlabel{suppl_atac_seq_emseq_sp1_10class}{{A.18}{143}{\textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. These sequences were classified by EMSequence to search for 10 different 30bp long motifs ($801 - 30 = 771$ of shifting freedom). The optimization was run for 20 iterations. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates head-to-tail arrangements of SP1 motifs.\relax }{figure.caption.58}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.19}{\ignorespaces \textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning without shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{144}{figure.caption.59}} -\newlabel{suppl_atac_seq_emread_ctcf_noshift_flip}{{A.19}{144}{\textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning without shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }{figure.caption.59}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.20}{\ignorespaces \textbf {Open chromatin classes around SP1 motifs :} EMRead was run without shifing (+/- 10bp) but with flipping to identify different classes of footprints around 15'883 SP1 motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{145}{figure.caption.60}} -\newlabel{suppl_atac_seq_emread_sp1_noshift_flip}{{A.20}{145}{\textbf {Open chromatin classes around SP1 motifs :} EMRead was run without shifing (+/- 10bp) but with flipping to identify different classes of footprints around 15'883 SP1 motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }{figure.caption.60}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.17}{\ignorespaces \textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates tandem arrangements of SP1 motifs.\relax }}{142}{figure.caption.55}} +\newlabel{suppl_atac_seq_emseq_sp1_7class}{{A.17}{142}{\textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates tandem arrangements of SP1 motifs.\relax }{figure.caption.55}{}} +\@writefile{toc}{\contentsline {subsubsection}{ChIPPartitioning}{142}{figure.caption.56}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.18}{\ignorespaces \textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. These sequences were classified by EMSequence to search for 10 different 30bp long motifs ($801 - 30 = 771$ of shifting freedom). The optimization was run for 20 iterations. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates head-to-tail arrangements of SP1 motifs.\relax }}{143}{figure.caption.56}} +\newlabel{suppl_atac_seq_emseq_sp1_10class}{{A.18}{143}{\textbf {SP1 motifs :} partition of 15'883 801bp sequences centered on a SP1 binding site using EMSequence. These sequences were classified by EMSequence to search for 10 different 30bp long motifs ($801 - 30 = 771$ of shifting freedom). The optimization was run for 20 iterations. The different classes are ordered by decreasing overall probability. Arrows atop of the motifs indicates head-to-tail arrangements of SP1 motifs.\relax }{figure.caption.56}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.19}{\ignorespaces \textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning without shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{144}{figure.caption.57}} +\newlabel{suppl_atac_seq_emread_ctcf_noshift_flip}{{A.19}{144}{\textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning without shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }{figure.caption.57}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.20}{\ignorespaces \textbf {Open chromatin classes around SP1 motifs :} EMRead was run without shifing (+/- 10bp) but with flipping to identify different classes of footprints around 15'883 SP1 motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{145}{figure.caption.58}} +\newlabel{suppl_atac_seq_emread_sp1_noshift_flip}{{A.20}{145}{\textbf {Open chromatin classes around SP1 motifs :} EMRead was run without shifing (+/- 10bp) but with flipping to identify different classes of footprints around 15'883 SP1 motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }{figure.caption.58}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {A.4.4}Other supplementary figures}{145}{subsection.A.4.4}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.23}{\ignorespaces \textbf {Extended sequence and chromatin models} found in monocytes regulatory regions. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }}{145}{figure.caption.63}} -\newlabel{suppl_atac_seq_23class}{{A.23}{145}{\textbf {Extended sequence and chromatin models} found in monocytes regulatory regions. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }{figure.caption.63}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.21}{\ignorespaces \textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning with shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{146}{figure.caption.61}} -\newlabel{suppl_atac_seq_emread_ctcf_shift_flip}{{A.21}{146}{\textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning with shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }{figure.caption.61}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.24}{\ignorespaces \textbf {PU.1 sub-classes} obtained by extracting PU.1 class data and subjecting them to a ChIPPartitioning classification into 2 classes. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }}{146}{figure.caption.64}} -\newlabel{suppl_atac_seq_pu1_subclass}{{A.24}{146}{\textbf {PU.1 sub-classes} obtained by extracting PU.1 class data and subjecting them to a ChIPPartitioning classification into 2 classes. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }{figure.caption.64}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.22}{\ignorespaces \textbf {Open chromatin classes around SP1 motifs :} EMRead was run with shifing (+/- 10bp) flipping to identify different classes of footprints around 15'883 SP1 motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{147}{figure.caption.62}} -\newlabel{suppl_atac_seq_emread_sp1_shift_flip}{{A.22}{147}{\textbf {Open chromatin classes around SP1 motifs :} EMRead was run with shifing (+/- 10bp) flipping to identify different classes of footprints around 15'883 SP1 motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }{figure.caption.62}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.25}{\ignorespaces \textbf {AP1 sub-classes} obtained by extracting AP1 class data and subjecting them to a ChIPPartitioning classification into 3 classes. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }}{147}{figure.caption.65}} -\newlabel{suppl_atac_seq_ap1_subclass}{{A.25}{147}{\textbf {AP1 sub-classes} obtained by extracting AP1 class data and subjecting them to a ChIPPartitioning classification into 3 classes. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }{figure.caption.65}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.23}{\ignorespaces \textbf {Extended sequence and chromatin models} found in monocytes regulatory regions. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }}{145}{figure.caption.61}} +\newlabel{suppl_atac_seq_23class}{{A.23}{145}{\textbf {Extended sequence and chromatin models} found in monocytes regulatory regions. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }{figure.caption.61}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.21}{\ignorespaces \textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning with shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{146}{figure.caption.59}} +\newlabel{suppl_atac_seq_emread_ctcf_shift_flip}{{A.21}{146}{\textbf {Open chromatin classes around CTCF motifs} found by ChIPPartitioning with shifing but with flipping to identify different classes of footprints around 26'650 CTCF motifs. The aggregation signal around the 6 different classes found are shown by decreasing class probability. The open chromatin patterns are displayed in red, the nucleosomes are displayed in blue. The aggregated DNA sequence is displayed as a logo. The y-axis ranges from the minimum to the maximum signal observed. For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }{figure.caption.59}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.24}{\ignorespaces \textbf {PU.1 sub-classes} obtained by extracting PU.1 class data and subjecting them to a ChIPPartitioning classification into 2 classes. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. A zoom over the central part of each class aggregation is shown in the top right inlet.\relax }}{146}{figure.caption.62}} +\newlabel{suppl_atac_seq_pu1_subclass}{{A.24}{146}{\textbf {PU.1 sub-classes} obtained by extracting PU.1 class data and subjecting them to a ChIPPartitioning classification into 2 classes. The displayed logos correspond to each class sequence aggregation. The corresponding chromatin accessibility (red) and nucleosome occupancy (blue) are displayed atop of the logos. The classes are displayed by overall decreasing probability. 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