diff --git a/head/settings_custom.tex b/head/settings_custom.tex index 663e8e3..cff0659 100644 --- a/head/settings_custom.tex +++ b/head/settings_custom.tex @@ -1,35 +1,39 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Thesis Settings % Custom settings % % 2011 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % % Use this file for your own custom packages, command-definitions, etc... % % % % % % Packages for references - cleverref must be last % \usepackage{nameref} % \usepackage{hyperref} % \usepackage{cleveref} % \usepackage[shortlabels]{enumitem} % % Reduce spacing in bibliography % \setlength{\bibsep}{0pt plus 0.3ex} % % Allow equations to break between pages % \allowdisplaybreaks % % Penalty for widow and orphan % \widowpenalty=9999 % \clubpenalty=9999 % %Penalty for relation and binary operation breaks in equations % \relpenalty=9999 % \binoppenalty=9999 \usepackage[labelfont=bf]{caption} \usepackage[linesnumbered,lined,boxed,commentsnumbered, ruled]{algorithm2e} \usepackage[]{float} \usepackage[]{caption} \usepackage{xr-hyper} % for cross references \usepackage{makecell} % for table formatting + +% defines += and -= operators for algorithms +\newcommand{\pluseq}{\mathrel{+}=} +\newcommand{\minuseq}{\mathrel{-}=} \ No newline at end of file diff --git a/main/ch_atac-seq.aux b/main/ch_atac-seq.aux index 7f05e4d..77e32dc 100644 --- a/main/ch_atac-seq.aux +++ b/main/ch_atac-seq.aux @@ -1,145 +1,145 @@ \relax \providecommand\hyper@newdestlabel[2]{} \citation{vierstra_genomic_2016} \citation{neph_expansive_2012} \citation{adey_rapid_2010,buenrostro_transposition_2013} \citation{barski_high-resolution_2007} \citation{vierstra_genomic_2016} \citation{vierstra_genomic_2016} \citation{adey_rapid_2010,buenrostro_transposition_2013} \citation{adey_rapid_2010} \citation{adey_rapid_2010} -\@writefile{toc}{\contentsline {chapter}{\numberline {4}Chromatin accessibility of monocytes}{43}{chapter.4}} +\@writefile{toc}{\contentsline {chapter}{\numberline {4}Chromatin accessibility of monocytes}{49}{chapter.4}} \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} \@writefile{loa}{\addvspace {10\p@ }} -\@writefile{chapter}{\contentsline {toc}{Chromatin accessibility of monocytes}{43}{chapter.4}} -\@writefile{toc}{\contentsline {section}{\numberline {4.1}ATAC-seq}{43}{section.4.1}} -\@writefile{lof}{\contentsline {figure}{\numberline {4.1}{\ignorespaces \textbf {ATAC-seq principle :} ATAC-seq uses a hyperactive Tn5 transposase to simultaneously cleave genomic DNA at accessible loci and ligate adaptors. 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Regions of interest are listed using peak calling on the the bulk data. The read densities in these regions (center of the peaks +/- a given offset) are measured. The regions are then clustered based on their signal shape to identify different chromatin architectures and create a catalog. These chromatin signatures can then be used to annotate each region of interest in each cell, based on the signal resemblance. The information can be stored as a matrix (M) that can be used for downstream analyses, such as sub-population identification.\relax }}{47}{figure.caption.28}} -\newlabel{atac_seq_pipeline}{{4.2}{47}{\textbf {framework to identify chromatin organization and use them to annotate cellular state :} the scATAC-seq data available in each individual cell are aggregated and used a if it was a bulk sequencing experiment. Regions of interest are listed using peak calling on the the bulk data. The read densities in these regions (center of the peaks +/- a given offset) are measured. 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The regions are then clustered based on their signal shape to identify different chromatin architectures and create a catalog. These chromatin signatures can then be used to annotate each region of interest in each cell, based on the signal resemblance. The information can be stored as a matrix (M) that can be used for downstream analyses, such as sub-population identification.\relax }}{53}{figure.caption.28}} +\newlabel{atac_seq_pipeline}{{4.2}{53}{\textbf {framework to identify chromatin organization and use them to annotate cellular state :} the scATAC-seq data available in each individual cell are aggregated and used a if it was a bulk sequencing experiment. Regions of interest are listed using peak calling on the the bulk data. The read densities in these regions (center of the peaks +/- a given offset) are measured. The regions are then clustered based on their signal shape to identify different chromatin architectures and create a catalog. 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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 }}{55}{figure.caption.30}} -\newlabel{atac_seq_fragment_size}{{4.4}{55}{\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. The 10bp oscillation reflect the DNA pitch.\\ 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). 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For each TF, the open chromatin architecture was measured by considering the corrected read edges (red) and the nucleosome occupancy (blue) by considering the center of the nucleosome fagments from the nucleosome fragment dataset. The motif location is indicated by the dashed lines.\relax }{figure.caption.32}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {4.6}{\ignorespaces \textbf {Signal around CTCF, SP1, myc and EBF1 motifs :} the human genome was scanned with using one PWM per TF. For each TF, the open chromatin architecture was measured by considering the corrected read edges (red) and the nucleosome occupancy (blue) by considering the center of the nucleosome fagments from the nucleosome fragment dataset. The motif location is indicated by the dashed lines.\relax }}{63}{figure.caption.32}} +\newlabel{atac_seq_ctcf_sp1_myc_ebf1_footprint}{{4.6}{63}{\textbf {Signal around CTCF, SP1, myc and EBF1 motifs :} the human genome was scanned with using one PWM per TF. For each TF, the open chromatin architecture was measured by considering the corrected read edges (red) and the nucleosome occupancy (blue) by considering the center of the nucleosome fagments from the nucleosome fragment dataset. The motif location is indicated by the dashed lines.\relax }{figure.caption.32}{}} \citation{adey_rapid_2010} \citation{buenrostro_transposition_2013,li_identification_2019} \citation{neph_expansive_2012} \citation{fu_insulator_2008} \citation{neph_expansive_2012} -\@writefile{toc}{\contentsline {subsection}{\numberline {4.8.2}Measuring open chromatin and nucleosome occupancy}{58}{subsection.4.8.2}} +\@writefile{toc}{\contentsline {subsection}{\numberline {4.8.2}Measuring open chromatin and nucleosome occupancy}{64}{subsection.4.8.2}} \citation{kundaje_ubiquitous_2012} \citation{nair_probabilistic_2014} -\@writefile{toc}{\contentsline {subsection}{\numberline {4.8.3}Evaluation of EMRead and EMSequence}{59}{subsection.4.8.3}} -\@writefile{lof}{\contentsline {figure}{\numberline {4.7}{\ignorespaces \textbf {Open chromatin classes around CTCF motifs :} EMRead was run 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 }}{60}{figure.caption.33}} -\newlabel{atac_seq_emread_ctcf_noshift_flip}{{4.7}{60}{\textbf {Open chromatin classes around CTCF motifs :} EMRead was run 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. 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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 }}{62}{figure.caption.35}} -\newlabel{atac_seq_emseq_auc_roc}{{4.9}{62}{\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. 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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 }}{66}{figure.caption.34}} +\newlabel{atac_seq_emread_ctcf_shift_flip}{{4.8}{66}{\textbf {Open chromatin classes around CTCF motifs :} EMRead was run 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. 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Arrows atop of the motifs indicates tandem arrangements of SP1 motifs.\relax }{figure.caption.36}{}} \citation{chatr-aryamontri_biogrid_2017} \citation{castro-mondragon_rsat_2017} \@setckpt{main/ch_atac-seq}{ -\setcounter{page}{65} +\setcounter{page}{71} \setcounter{equation}{6} \setcounter{enumi}{13} \setcounter{enumii}{0} \setcounter{enumiii}{0} \setcounter{enumiv}{0} \setcounter{footnote}{0} \setcounter{mpfootnote}{0} \setcounter{part}{0} \setcounter{chapter}{4} \setcounter{section}{8} \setcounter{subsection}{3} \setcounter{subsubsection}{0} \setcounter{paragraph}{0} \setcounter{subparagraph}{0} \setcounter{figure}{10} \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}{13} \setcounter{Hfootnote}{0} \setcounter{bookmark@seq@number}{0} \setcounter{AM@survey}{0} \setcounter{ttlp@side}{0} \setcounter{myparts}{0} \setcounter{parentequation}{0} \setcounter{AlgoLine}{17} -\setcounter{algocfline}{1} -\setcounter{algocfproc}{1} -\setcounter{algocf}{1} +\setcounter{algocfline}{2} +\setcounter{algocfproc}{2} +\setcounter{algocf}{2} \setcounter{float@type}{8} \setcounter{nlinenum}{0} \setcounter{lstlisting}{0} \setcounter{section@level}{0} } diff --git a/main/ch_encode_peaks.aux b/main/ch_encode_peaks.aux index e527c32..369b127 100644 --- a/main/ch_encode_peaks.aux +++ b/main/ch_encode_peaks.aux @@ -1,101 +1,117 @@ \relax \providecommand\hyper@newdestlabel[2]{} \citation{cheng_understanding_2012} \citation{cheng_understanding_2012} \citation{mathelier_jaspar_2014} \citation{kulakovskiy_hocomoco:_2016} \citation{jolma_dna-binding_2013} \citation{cheng_understanding_2012} \citation{mathelier_jaspar_2014} \citation{kulakovskiy_hocomoco:_2016} \citation{jolma_dna-binding_2013} \citation{cheng_understanding_2012} \citation{gerstein_architecture_2012} \citation{wu_biogps:_2016} \citation{ghirlando_ctcf:_2016} \@writefile{toc}{\contentsline {chapter}{\numberline {2}ENCODE peaks analysis}{23}{chapter.2}} \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} \@writefile{loa}{\addvspace {10\p@ }} \@writefile{toc}{\contentsline {chapter}{ENCODE peaks analysis}{23}{chapter.2}} \@writefile{toc}{\contentsline {section}{\numberline {2.1}Data}{23}{section.2.1}} \@writefile{lof}{\contentsline {figure}{\numberline {2.1}{\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), non specific TF (TFNS), chromatin structure (ChromStr), chromatin modifier (ChromModif), RNAPII associated factors (Pol2), RNAPIII associated factors (Pol3) and others. The horizontal dashed lines indicate 20'000 and 40'000.\relax }}{24}{figure.caption.19}} \newlabel{encode_peaks_gm12878_peak_number}{{2.1}{24}{\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), non specific TF (TFNS), chromatin structure (ChromStr), chromatin modifier (ChromModif), RNAPII associated factors (Pol2), RNAPIII associated factors (Pol3) and others. The horizontal dashed lines indicate 20'000 and 40'000.\relax }{figure.caption.19}{}} \@writefile{lof}{\contentsline {figure}{\numberline {2.2}{\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), non specific TF (TFNS), chromatin structure (ChromStr), chromatin modifier (ChromModif), RNAPII associated factors (Pol2), RNAPIII associated factors (Pol3) and others. The horizontal dashed line indicates 0.5.\relax }}{24}{figure.caption.20}} \newlabel{encode_peaks_gm12878_motif_prop}{{2.2}{24}{\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. 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The horizontal dashed line indicates 0.5.\relax }{figure.caption.20}{}} \citation{hon_chromasig:_2008,nielsen_catchprofiles:_2012,kundaje_ubiquitous_2012,nair_probabilistic_2014,groux_spar-k:_2019} \citation{nair_probabilistic_2014} \@writefile{toc}{\contentsline {section}{\numberline {2.2}ChIPPartitioning : an algorithm to identify chromatin architectures}{25}{section.2.2}} +\newlabel{encode_peaks_chippartitioning}{{2.2}{25}{ChIPPartitioning : an algorithm to identify chromatin architectures}{section.2.2}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {2.2.1}Data realignment}{26}{subsection.2.2.1}} \citation{zhang_canonical_2014} \@writefile{toc}{\contentsline {section}{\numberline {2.3}Nucleosome organization around transcription factor binding sites}{27}{section.2.3}} \@writefile{lof}{\contentsline {figure}{\numberline {2.3}{\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 }}{28}{figure.caption.21}} \newlabel{encode_peaks_array_measure}{{2.3}{28}{\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.21}{}} \citation{kundaje_ubiquitous_2012,fu_insulator_2008} \citation{stedman_cohesins_2008} \citation{donohoe_identification_2007} \citation{bailey_znf143_2015} \@writefile{toc}{\contentsline {section}{\numberline {2.4}The case of CTCF, RAD21, SMC3, YY1 and ZNF143}{29}{section.2.4}} \@writefile{lof}{\contentsline {figure}{\numberline {2.4}{\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 }}{30}{figure.caption.22}} \newlabel{encode_peaks_colocalization_ctcf}{{2.4}{30}{\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.22}{}} \@writefile{lof}{\contentsline {figure}{\numberline {2.5}{\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 }}{31}{figure.caption.23}} \newlabel{encode_peaks_ctcf_ndr}{{2.5}{31}{\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.23}{}} \citation{dreos_mga_2018} \citation{gerstein_architecture_2012} \citation{mathelier_jaspar_2014} \citation{kulakovskiy_hocomoco:_2016} \citation{jolma_dna-binding_2013} \citation{gaffney_controls_2012} \@writefile{toc}{\contentsline {section}{\numberline {2.5}Study of CTCF interactor motifs}{33}{section.2.5}} \@writefile{toc}{\contentsline {section}{\numberline {2.6}The EBF1 case}{33}{section.2.6}} \@writefile{toc}{\contentsline {section}{\numberline {2.7}Methods}{33}{section.2.7}} \@writefile{toc}{\contentsline {subsection}{\numberline {2.7.1}Data and data processing}{33}{subsection.2.7.1}} +\newlabel{encode_peaks_methods_data}{{2.7.1}{33}{Data and data processing}{subsection.2.7.1}{}} \citation{boyle_high-resolution_2008} \citation{dreos_eukaryotic_2017} \citation{siepel_evolutionarily_2005} +\citation{ambrosini_chip-seq_2016} +\citation{nair_probabilistic_2014} +\@writefile{toc}{\contentsline {subsection}{\numberline {2.7.2}Classification of MNase patterns}{34}{subsection.2.7.2}} +\newlabel{encode_peaks_em_mnase}{{2.7.2}{34}{Classification of MNase patterns}{subsection.2.7.2}{}} +\citation{zhang_canonical_2014} +\citation{ambrosini_chip-seq_2016} +\@writefile{toc}{\contentsline {subsection}{\numberline {2.7.3}Quantifying nucleosome array intensity from classification results}{35}{subsection.2.7.3}} +\newlabel{encode_peaks_equation_shift_density1}{{2.1}{35}{Quantifying nucleosome array intensity from classification results}{equation.2.7.1}{}} +\citation{ambrosini_chip-seq_2016} +\newlabel{encode_peaks_equation_shift_density2}{{2.2}{36}{Quantifying nucleosome array intensity from classification results}{equation.2.7.2}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {2.7.4}Peak colocalization}{36}{subsection.2.7.4}} +\@writefile{toc}{\contentsline {subsection}{\numberline {2.7.5}NDR detection}{36}{subsection.2.7.5}} +\newlabel{encode_peaks_algo_ndr_extend}{{1}{38}{NDR detection}{algocfline.1}{}} +\@writefile{loa}{\contentsline {algocf}{\numberline {1}{\ignorespaces Searches the coordinates of the NDR using the posterior nucleosome and nucleosome free class probabilities, for a region $R_i$, from its central position.\relax }}{38}{algocf.1}} \@setckpt{main/ch_encode_peaks}{ -\setcounter{page}{35} -\setcounter{equation}{0} +\setcounter{page}{40} +\setcounter{equation}{6} \setcounter{enumi}{13} \setcounter{enumii}{0} \setcounter{enumiii}{0} \setcounter{enumiv}{0} \setcounter{footnote}{0} \setcounter{mpfootnote}{0} \setcounter{part}{0} \setcounter{chapter}{2} \setcounter{section}{7} -\setcounter{subsection}{1} +\setcounter{subsection}{5} \setcounter{subsubsection}{0} \setcounter{paragraph}{0} \setcounter{subparagraph}{0} \setcounter{figure}{5} \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}{13} \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{AlgoLine}{28} +\setcounter{algocfline}{1} +\setcounter{algocfproc}{1} +\setcounter{algocf}{1} \setcounter{float@type}{8} \setcounter{nlinenum}{0} \setcounter{lstlisting}{0} \setcounter{section@level}{0} } diff --git a/main/ch_encode_peaks.tex b/main/ch_encode_peaks.tex index a357edf..3ee7a0f 100644 --- a/main/ch_encode_peaks.tex +++ b/main/ch_encode_peaks.tex @@ -1,161 +1,288 @@ \cleardoublepage \chapter{ENCODE peaks analysis} \markboth{ENCODE peaks analysis}{ENCODE peaks analysis} \addcontentsline{toc}{chapter}{ENCODE peaks analysis} % Modeling a TF sequence specificity only allows to partially understand how a TF binds a region. Indeed, scanning a genome using a PWM for putative binding sites often returns tens of thousands of sites with only a subset of them being really occupied within a cell. Other elements such as chromatin organization and composition are likely to drive TF binding. Thus gaining a better understanding about the chromat % The exact mechanisms at play remain unclear but nucleosome occupancy is thought to shelter DNA sequence - as some bases are facing the core octamer or to distort the DNA structure - impeding sequence recognition by TFs. In vivo, evidences for competition between TFs and nucleosomes have been collected. Computational simulations accounting for simultaneous multiple factor binding on DNA suggested that nucleosome occupancy and TFs binding influence each other and that TF binds nucleosome depleted regions \cite{wasson_ensemble_2009}. As discussed above, the organization of chromatin has a deep impact on TF binding. Nucleosomes and TFs are in competition to bind DNA. Because TFs are ultimate forces driving gene expression, understanding how chromatin influence them, or at least how chromatin is organized around them, is crucial. It is now clear that nucleosome occupancy fulfills more than a packaging role. It can also acts as a barrier to impede DNA reading processes and compete with TFs for sequence occupancy. Thus gaining a better understanding of how chromatin is organized around TF binding sites is crucial to understand TF binding beyond their sequence specificity only. In an effort to better understand how the genome is organized and how its functions are fulfilled, the ENCODE Consortium which released an impressive collection of coherent data representing an unprecedented picture of the chromatin in human cell lines. The GM12878 cells were chosen as one of the highest priority cell line. GM12878 are widely-used lymphoblastoids. Because of their ability to divide and of their normal karyotype - unlike HeLa cells - these cells are a good model for genomic studies. \section{Data} - % number of peaks per dataset \begin{figure} \begin{center} \includegraphics[scale=0.3]{images/ch_encode_peaks/peaklist_peaknumber_GM12878.png} \captionof{figure}{\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), non specific TF (TFNS), chromatin structure (ChromStr), chromatin modifier (ChromModif), RNAPII associated factors (Pol2), RNAPIII associated factors (Pol3) and others. The horizontal dashed lines indicate 20'000 and 40'000.} \label{encode_peaks_gm12878_peak_number} \end{center} \end{figure} % proportion of peaks with motif per dataset \begin{figure} \begin{center} \includegraphics[scale=0.3]{images/ch_encode_peaks/peaklist_proportions_GM12878.png} \captionof{figure}{\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 $1\cdot10^{-4}$ 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), non specific TF (TFNS), chromatin structure (ChromStr), chromatin modifier (ChromModif), RNAPII associated factors (Pol2), RNAPIII associated factors (Pol3) and others. The horizontal dashed line indicates 0.5.} \label{encode_peaks_gm12878_motif_prop} \end{center} \end{figure} In these cells, the ENCODE Consortium released ChIP-seq data 53 different TFs. Additionally, nucleosome occupancy (MNase-seq) and chromatin accessiblity (DNasI-seq) data were generated with a depth of coverage. Furthermore, the ENCODE Consortium also released peaks called using their uniform processing pipeline \cite{gerstein_architecture_2012}. These peaks are interesting because i) they are called from technical replicate ChIP-seq samples and ii) several peak callers are used and the different results are integrated. These peaks are thus reproducible [REFERENCE IDR] and robust to peak caller discrepancies and can be considered an excellent standard. The number of peaks called for each TF was highly variable and likely reflects each factor activity in this cell line (Figure \ref{encode_peaks_gm12878_peak_number}). The most abundant factor in terms of peaks was RUNX3 followed by CTCF. This observation fits to BioGPS \citep{wu_biogps:_2016} data which indicates that both RUNX3 and CTCF have a higher expression in lymphoblast and in B cells compared to other tissues. Regarding CTCF, it is involved in chromatin looping \citep{ghirlando_ctcf:_2016}. Because it implies that two CTCF molecules form an homodimer dued to the genome 3D conformation, it potential multiply by 2 the number of CTCF peaks. Moreover, the propensity of each TF to bind through their motifs was also variable, with again CTCF being showing the highest values \ref{encode_peaks_gm12878_motif_prop}. \section{ChIPPartitioning : an algorithm to identify chromatin architectures} +\label{encode_peaks_chippartitioning} Discovering archetypical chromatin architectures over a set of regions of interest - let's say containing a TF binding site in their middle - is a long standing problem in bioinformatics. More formerly, given a matrix $R$ of dimensions $NxL$ containing $N$ vectors of read counts $r_{1}, r_{2}, ..., r_{N}$ of length $L$, each containing the number of reads mapping at a given position in a given region, find $K \leq N$ vectors of length $L' \leq L$ that contain archetypical signals found in the $N$ regions of $R$. This can actually be solved using clustering methods which groups regions that look alike into $K$ groups. The summary of the signal inside each group - for instance the mean signal for the K-means algorithm - can then be interpreted as the archetypical chromatin architectures. Biologically, different organization may reflect different functions. First, the $N$ regions of interest are usually aligned with respect to a feature of interest, for instance a TF binding sites. However, he chromatin features of interest - for instance the nucleosomes - may not be aligned from one region to the next. This can originate because i) of the true binding sites being fuzzely distributed around the center of the regions, ii) the chromatin features appear at a varying distance from the region centers or iii) both. Comparing two regions then necessitate to first realign the chromatin features. Second, the regions can show a functional orientation. For instance, TF binding sites have an upstream and a downstream with respect to the bound sequence. Orienting properly the regions is also required to properly compare the chromatin organizations in two regions. Finally, the signal over some regions may be sparse because of a sub-optimal sequencing depth. The study of signal distribution over genomic regions has been a quite active field for bulk sequencing experiments during the last decade. Dedicated algorithms \citep{hon_chromasig:_2008,nielsen_catchprofiles:_2012,kundaje_ubiquitous_2012,nair_probabilistic_2014,groux_spar-k:_2019} have been developed to cluster genomic regions based on their distribution of reads. Most of these algorithms and softwares deal with some of these issues cited above. However, the algorithm developed by \citep{nair_probabilistic_2014} - which I will call ChIPPartitioning - is probably the best. ChIPPartitioning is a probabilistic partitioning method that softly clusters a sets of genomic regions based on their signal shape (as opposed to the absolute values) resemblance. To ensure proper comparisons between the regions, the algorithm allows to offset one region compare to the other to retrieve a similar signal at different offsets and to flip the signal orientation. Finally, it has been demonstrated to be really robust to sparse data. This algorithm models the signal over a region of length $L$ has having being sampled from a mixture of $K$ signal models, using $L$ independent Poisson distributions. The number of reads sequenced over this region is then the result of this sampling process. The entire set of regions is assumed to have been generated from a mixture of $K$ different signal models (classes). Each class is represented by a vector of $L' \leq L$ values that represent the expected number of reads at each position for that class. These values are thus the Poisson distribution parameters. In order to discover the $K$ different chromatin signatures in the data, the algorithm proceed to a maximum likelihood estimation of the Poisson distribution parameters using an expectation-maximization (EM) framework. Given a set of $K$ models, the likelihoods of each region given each class is computed. A posterior probability of each class given each region can, in turn, be computed. These probabilities can be interpreted as a soft clustering. The parameters of the classes are updated using a weighted aggregation of the signal. Since each region is computed a probability to belong to each class, it participates to the update of all the classes, with different weights. If the length of the chromatin signature searched $L' m_{i}^{free}$). + +The binding sites - located in the center of the regions, at position $s = L/2$ - were assumed to be within the NDR. From that point, the NDR was extended using the following procedure : + +\SetKwProg{Fn}{}{\{}{}\SetKwFunction{Function}{float NDRextend}% +\begin{algorithm}[H] + \label{encode_peaks_algo_ndr_extend} + \Fn{\Function{}} + { \KwData{The posterior probabilities obtained for each position of $r_{i}$.} + \KwResult{the left and right coordinates of the NDR} + + \tcp{NDR only covers the central location} + $left = s$ \; + $right = s$ \; + + \While{$left \ne 2$ and $right \ne L-1$} + { $p.free.l = P(free|r_{i,left})$ \; + $p.free.r = P(free|r_{i,right})$ \; + $p.nucl.l = P(nucl|r_{i,left})$ \; + $p.nucl.r = P(nucl|r_{i,right})$ \; + + \tcp{bidirectional extension} + \If{$prob.free.l > p.nucl.l$ and $p.prob.free.r > p.nucl.r$} + { $left \minuseq 1$ \; + $right \pluseq 1$ \; + } + + \tcp{extension to left} + \ElseIf{$prob.free.l > p.nucl.l$} + { $left \minuseq 1$ \; } + + \tcp{extension to right} + \ElseIf{$p.prob.free.r > p.nucl.r$} + { $right \pluseq 1$ \; } + + \tcp{no more extension possible} + \Else + { break \; } + } + + \Return{$left$, $right$} + } + \caption{Searches the coordinates of the NDR using the posterior nucleosome and nucleosome free class probabilities, for a region $R_i$, from its central position.} +\end{algorithm} + +The nucleosome occupancy around CTCF binding sites was measured using ChIP-extract with "wgEncodeAwgTfbsSydhGm12878Ctcfsc15914sc20UniPk" peak list as reference - because it was the CTCF peak list with the most peaks and with the highest proportion of peaks with a CTCF motif -, the ENCODE MNase-seq data described in section \ref{encode_peaks_methods_data} as targets and the following parameters : from -999bp, to 1000bp and window size 10bp. + +This matrix was subjected to a ChIPPartitioning partitioning, as described in section \ref{encode_peaks_em_mnase}, to find 4 nucleosome architectures, using shifting and flipping. The resulting posterior probabilities were used to re-orient the data. If the major - with the highest probability - shift state, for a given region, was the "reverse" state, then the row was reversed. The re-oriented matrix was then subjected to the NDR detection. \ No newline at end of file diff --git a/main/ch_smile-seq.aux b/main/ch_smile-seq.aux index d5ab390..9f735c5 100644 --- a/main/ch_smile-seq.aux +++ b/main/ch_smile-seq.aux @@ -1,84 +1,84 @@ \relax \providecommand\hyper@newdestlabel[2]{} \citation{isakova_smile-seq_2017} \citation{isakova_smile-seq_2017} \citation{isakova_smile-seq_2017} \citation{maerkl_systems_2007} \citation{berger_universal_2009} \citation{zhao_inferring_2009,jolma_multiplexed_2010} \citation{isakova_smile-seq_2017} -\@writefile{toc}{\contentsline {chapter}{\numberline {3}SMiLE-seq data analysis}{35}{chapter.3}} +\@writefile{toc}{\contentsline {chapter}{\numberline {3}SMiLE-seq data analysis}{41}{chapter.3}} \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} \@writefile{loa}{\addvspace {10\p@ }} -\@writefile{toc}{\contentsline {chapter}{SMiLE-seq data analysis}{35}{chapter.3}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.1}Introduction}{35}{subsection.3.0.1}} -\@writefile{lof}{\contentsline {figure}{\numberline {3.1}{\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 }}{36}{figure.caption.24}} -\newlabel{smile_seq_pipeline}{{3.1}{36}{\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. 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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 }}{42}{figure.caption.24}} +\newlabel{smile_seq_pipeline}{{3.1}{42}{\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.24}{}} \citation{isakova_smile-seq_2017} \citation{isakova_smile-seq_2017} \citation{weirauch_evaluation_2013} -\@writefile{lof}{\contentsline {figure}{\numberline {3.2}{\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 }}{37}{figure.caption.25}} -\newlabel{smile_seq_hmm}{{3.2}{37}{\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. 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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 }}{43}{figure.caption.25}} +\newlabel{smile_seq_hmm}{{3.2}{43}{\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.25}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.2}Hidden Markov Model Motif discovery}{43}{subsection.3.0.2}} +\newlabel{section_smileseq_hmm}{{3.0.2}{43}{Hidden Markov Model Motif discovery}{subsection.3.0.2}{}} \citation{schutz_mamot:_2008} \citation{orenstein_comparative_2014} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.3}Binding motif evaluation}{38}{subsection.3.0.3}} -\newlabel{section_smileseq_pwmeval}{{3.0.3}{38}{Binding motif evaluation}{subsection.3.0.3}{}} -\newlabel{smile_seq_pwmeval_score}{{3.1}{39}{Binding motif evaluation}{equation.3.0.1}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.3}Binding motif evaluation}{44}{subsection.3.0.3}} +\newlabel{section_smileseq_pwmeval}{{3.0.3}{44}{Binding motif evaluation}{subsection.3.0.3}{}} +\newlabel{smile_seq_pwmeval_score}{{3.1}{45}{Binding motif evaluation}{equation.3.0.1}{}} \citation{jolma_dna-binding_2013} \citation{mathelier_jaspar_2014} \citation{kulakovskiy_hocomoco:_2016} -\newlabel{smile_seq_algo_auc}{{1}{40}{Binding motif evaluation}{algocfline.1}{}} -\@writefile{loa}{\contentsline {algocf}{\numberline {1}{\ignorespaces Computes the AUC-ROC\relax }}{40}{algocf.1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.4}Results}{40}{subsection.3.0.4}} -\@writefile{lof}{\contentsline {figure}{\numberline {3.3}{\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 }}{41}{figure.caption.26}} -\newlabel{smileseq_auc}{{3.3}{41}{\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.26}{}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.5}Conclusions}{42}{subsection.3.0.5}} +\newlabel{smile_seq_algo_auc}{{2}{46}{Binding motif evaluation}{algocfline.2}{}} +\@writefile{loa}{\contentsline {algocf}{\numberline {2}{\ignorespaces Computes the AUC-ROC\relax }}{46}{algocf.2}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.4}Results}{46}{subsection.3.0.4}} +\@writefile{lof}{\contentsline {figure}{\numberline {3.3}{\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 }}{47}{figure.caption.26}} +\newlabel{smileseq_auc}{{3.3}{47}{\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.26}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.5}Conclusions}{48}{subsection.3.0.5}} \@setckpt{main/ch_smile-seq}{ -\setcounter{page}{43} +\setcounter{page}{49} \setcounter{equation}{1} \setcounter{enumi}{13} \setcounter{enumii}{0} \setcounter{enumiii}{0} \setcounter{enumiv}{0} \setcounter{footnote}{0} \setcounter{mpfootnote}{0} \setcounter{part}{0} \setcounter{chapter}{3} \setcounter{section}{0} \setcounter{subsection}{5} \setcounter{subsubsection}{0} \setcounter{paragraph}{0} \setcounter{subparagraph}{0} \setcounter{figure}{3} \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} 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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}{} \babel@toc {french}{} \babel@toc {english}{} \contentsline {chapter}{Introduction}{1}{chapter*.7} \contentsline {chapter}{\numberline {1}Published laboratory projects}{3}{chapter.1} \contentsline {chapter}{Published laboratory projects}{3}{chapter.1} \contentsline {section}{\numberline {1.1}Mass Genome Annotation repository}{3}{section.1.1} \contentsline {subsection}{\numberline {1.1.1}Introduction}{3}{subsection.1.1.1} \contentsline {subsection}{\numberline {1.1.2}MGA content and organization}{3}{subsection.1.1.2} \contentsline {subsection}{\numberline {1.1.3}Conclusions}{5}{subsection.1.1.3} \contentsline {section}{\numberline {1.2}Eukaryotic Promoter Database}{6}{section.1.2} \contentsline {subsection}{\numberline {1.2.1}Introduction}{7}{subsection.1.2.1} \contentsline {subsection}{\numberline {1.2.2}EPDnew now annotates (some of) your mushrooms and vegetables}{7}{subsection.1.2.2} \contentsline {subsection}{\numberline {1.2.3}Increased mapping precision in human}{7}{subsection.1.2.3} \contentsline {subsection}{\numberline {1.2.4}Integration of EPDnew with other resources}{9}{subsection.1.2.4} \contentsline {subsection}{\numberline {1.2.5}Conclusions}{10}{subsection.1.2.5} \contentsline {subsection}{\numberline {1.2.6}Methods}{10}{subsection.1.2.6} \contentsline {subsubsection}{Motif occurrence profiles}{10}{subsection.1.2.6} \contentsline {section}{\numberline {1.3}PWMScan}{11}{section.1.3} \contentsline {subsection}{\numberline {1.3.1}Introduction}{11}{subsection.1.3.1} \contentsline {subsection}{\numberline {1.3.2}Data and methods}{13}{subsection.1.3.2} \contentsline {subsection}{\numberline {1.3.3}Benchmark}{14}{subsection.1.3.3} \contentsline {subsection}{\numberline {1.3.4}Conclusions}{16}{subsection.1.3.4} \contentsline {section}{\numberline {1.4}SPar-K}{17}{section.1.4} \contentsline {subsection}{\numberline {1.4.1}Introduction}{17}{subsection.1.4.1} \contentsline {subsection}{\numberline {1.4.2}Methods}{17}{subsection.1.4.2} \contentsline {subsection}{\numberline {1.4.3}Results}{21}{subsection.1.4.3} \contentsline {subsection}{\numberline {1.4.4}Conclusion}{21}{subsection.1.4.4} \contentsline {chapter}{\numberline {2}ENCODE peaks analysis}{23}{chapter.2} \contentsline {chapter}{ENCODE peaks analysis}{23}{chapter.2} \contentsline {section}{\numberline {2.1}Data}{23}{section.2.1} \contentsline {section}{\numberline {2.2}ChIPPartitioning : an algorithm to identify chromatin architectures}{25}{section.2.2} \contentsline {subsection}{\numberline {2.2.1}Data realignment}{26}{subsection.2.2.1} \contentsline {section}{\numberline {2.3}Nucleosome organization around transcription factor binding sites}{27}{section.2.3} \contentsline {section}{\numberline {2.4}The case of CTCF, RAD21, SMC3, YY1 and ZNF143}{29}{section.2.4} \contentsline {section}{\numberline {2.5}Study of CTCF interactor motifs}{33}{section.2.5} \contentsline {section}{\numberline {2.6}The EBF1 case}{33}{section.2.6} \contentsline {section}{\numberline {2.7}Methods}{33}{section.2.7} \contentsline {subsection}{\numberline {2.7.1}Data and data processing}{33}{subsection.2.7.1} -\contentsline {chapter}{\numberline {3}SMiLE-seq data analysis}{35}{chapter.3} -\contentsline {chapter}{SMiLE-seq data analysis}{35}{chapter.3} -\contentsline {subsection}{\numberline {3.0.1}Introduction}{35}{subsection.3.0.1} -\contentsline {subsection}{\numberline {3.0.2}Hidden Markov Model Motif discovery}{37}{subsection.3.0.2} -\contentsline {subsection}{\numberline {3.0.3}Binding motif evaluation}{38}{subsection.3.0.3} -\contentsline {subsection}{\numberline {3.0.4}Results}{40}{subsection.3.0.4} -\contentsline {subsection}{\numberline {3.0.5}Conclusions}{42}{subsection.3.0.5} -\contentsline {chapter}{\numberline {4}Chromatin accessibility of monocytes}{43}{chapter.4} -\contentsline {section}{\numberline {4.1}ATAC-seq}{43}{section.4.1} -\contentsline {section}{\numberline {4.2}Monitoring TF binding}{45}{section.4.2} -\contentsline {section}{\numberline {4.3}The advent of single cell DGF}{46}{section.4.3} -\contentsline {section}{\numberline {4.4}A quick overview of scATAC-seq data analysis}{46}{section.4.4} -\contentsline {section}{\numberline {4.5}Open questions}{46}{section.4.5} -\contentsline {section}{\numberline {4.6}Data}{48}{section.4.6} -\contentsline {section}{\numberline {4.7}Identification of catalog of chromatin architectures}{48}{section.4.7} -\contentsline {subsection}{\numberline {4.7.1}EMRead : an algorithm to identify over-represented chromatin architecture}{49}{subsection.4.7.1} -\contentsline {subsection}{\numberline {4.7.2}EMSequence : an algorithm to identify over-represented sequences}{50}{subsection.4.7.2} -\contentsline {subsubsection}{without shift and flip}{51}{subsection.4.7.2} -\contentsline {subsubsection}{with shift and flip}{51}{equation.4.7.2} -\contentsline {subsection}{\numberline {4.7.3}EMJoint : an algorithm to identify over-represented sequences and chromatin architectures}{52}{subsection.4.7.3} -\contentsline {subsection}{\numberline {4.7.4}Data realignment}{53}{subsection.4.7.4} -\contentsline {subsection}{\numberline {4.7.5}Implementations}{54}{subsection.4.7.5} -\contentsline {section}{\numberline {4.8}Results}{54}{section.4.8} -\contentsline {subsection}{\numberline {4.8.1}Fragment size analysis}{54}{subsection.4.8.1} -\contentsline {subsection}{\numberline {4.8.2}Measuring open chromatin and nucleosome occupancy}{58}{subsection.4.8.2} -\contentsline {subsection}{\numberline {4.8.3}Evaluation of EMRead and EMSequence}{59}{subsection.4.8.3} -\contentsline {subsubsection}{EMRead}{61}{subsection.4.8.3} -\contentsline {subsubsection}{EMSequence}{62}{figure.caption.34} -\contentsline {chapter}{\numberline {A}An appendix}{65}{appendix.A} -\contentsline {section}{\numberline {A.1}Supplementary figures}{65}{section.A.1} +\contentsline {subsection}{\numberline {2.7.2}Classification of MNase patterns}{34}{subsection.2.7.2} +\contentsline {subsection}{\numberline {2.7.3}Quantifying nucleosome array intensity from classification results}{35}{subsection.2.7.3} +\contentsline {subsection}{\numberline {2.7.4}Peak colocalization}{36}{subsection.2.7.4} +\contentsline {subsection}{\numberline {2.7.5}NDR detection}{36}{subsection.2.7.5} +\contentsline {chapter}{\numberline {3}SMiLE-seq data analysis}{41}{chapter.3} +\contentsline {chapter}{SMiLE-seq data analysis}{41}{chapter.3} +\contentsline {subsection}{\numberline {3.0.1}Introduction}{41}{subsection.3.0.1} +\contentsline {subsection}{\numberline {3.0.2}Hidden Markov Model Motif discovery}{43}{subsection.3.0.2} +\contentsline {subsection}{\numberline {3.0.3}Binding motif evaluation}{44}{subsection.3.0.3} +\contentsline {subsection}{\numberline {3.0.4}Results}{46}{subsection.3.0.4} +\contentsline {subsection}{\numberline {3.0.5}Conclusions}{48}{subsection.3.0.5} +\contentsline {chapter}{\numberline {4}Chromatin accessibility of monocytes}{49}{chapter.4} +\contentsline {section}{\numberline {4.1}ATAC-seq}{49}{section.4.1} +\contentsline {section}{\numberline {4.2}Monitoring TF binding}{51}{section.4.2} +\contentsline {section}{\numberline {4.3}The advent of single cell DGF}{52}{section.4.3} +\contentsline {section}{\numberline {4.4}A quick overview of scATAC-seq data analysis}{52}{section.4.4} +\contentsline {section}{\numberline {4.5}Open questions}{52}{section.4.5} +\contentsline {section}{\numberline {4.6}Data}{54}{section.4.6} +\contentsline {section}{\numberline {4.7}Identification of catalog of chromatin architectures}{54}{section.4.7} +\contentsline {subsection}{\numberline {4.7.1}EMRead : an algorithm to identify over-represented chromatin architecture}{55}{subsection.4.7.1} +\contentsline {subsection}{\numberline {4.7.2}EMSequence : an algorithm to identify over-represented sequences}{56}{subsection.4.7.2} +\contentsline {subsubsection}{without shift and flip}{57}{subsection.4.7.2} +\contentsline {subsubsection}{with shift and flip}{57}{equation.4.7.2} +\contentsline {subsection}{\numberline {4.7.3}EMJoint : an algorithm to identify over-represented sequences and chromatin architectures}{58}{subsection.4.7.3} +\contentsline {subsection}{\numberline {4.7.4}Data realignment}{59}{subsection.4.7.4} +\contentsline {subsection}{\numberline {4.7.5}Implementations}{60}{subsection.4.7.5} +\contentsline {section}{\numberline {4.8}Results}{60}{section.4.8} +\contentsline {subsection}{\numberline {4.8.1}Fragment size analysis}{60}{subsection.4.8.1} +\contentsline {subsection}{\numberline {4.8.2}Measuring open chromatin and nucleosome occupancy}{64}{subsection.4.8.2} +\contentsline {subsection}{\numberline {4.8.3}Evaluation of EMRead and EMSequence}{65}{subsection.4.8.3} +\contentsline {subsubsection}{EMRead}{67}{subsection.4.8.3} +\contentsline {subsubsection}{EMSequence}{68}{figure.caption.34} +\contentsline {chapter}{\numberline {A}An appendix}{71}{appendix.A} +\contentsline {section}{\numberline {A.1}Supplementary figures}{71}{section.A.1} \vspace {\normalbaselineskip } -\contentsline {chapter}{Bibliography}{77}{section*.49} -\contentsline {chapter}{Bibliography}{84}{appendix*.50} -\contentsline {chapter}{Curriculum Vitae}{85}{section*.51} +\contentsline {chapter}{Bibliography}{83}{section*.49} +\contentsline {chapter}{Bibliography}{90}{appendix*.50} +\contentsline {chapter}{Curriculum Vitae}{91}{section*.51} diff --git a/tail/appendix.aux b/tail/appendix.aux index 5ad401b..8fbc899 100644 --- a/tail/appendix.aux +++ b/tail/appendix.aux @@ -1,81 +1,81 @@ \relax \providecommand\hyper@newdestlabel[2]{} \citation{jolma_dna-binding_2013} \citation{jolma_dna-binding_2013} -\@writefile{toc}{\contentsline {chapter}{\numberline {A}An appendix}{65}{appendix.A}} +\@writefile{toc}{\contentsline {chapter}{\numberline {A}An appendix}{71}{appendix.A}} \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} \@writefile{loa}{\addvspace {10\p@ }} -\@writefile{toc}{\contentsline {section}{\numberline {A.1}Supplementary figures}{65}{section.A.1}} -\@writefile{lof}{\contentsline {figure}{\numberline {A.1}{\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. 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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.5}{\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 }}{69}{figure.caption.41}} -\newlabel{suppl_encode_peaks_em_max}{{A.5}{69}{\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. 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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 }}{70}{figure.caption.42}} -\newlabel{suppl_encode_peaks_em_brca1}{{A.6}{70}{\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. 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The nucleosome depleted region is displayed in blue.\relax }{figure.caption.43}{}} +\@writefile{toc}{\contentsline {section}{\numberline {A.1}Supplementary figures}{71}{section.A.1}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.1}{\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 }}{71}{figure.caption.37}} +\newlabel{suppl_smileseq_auc_2}{{A.1}{71}{\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.37}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.2}{\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 }}{72}{figure.caption.38}} +\newlabel{suppl_encode_peaks_em_ctcf}{{A.2}{72}{\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.38}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.3}{\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 }}{73}{figure.caption.39}} +\newlabel{suppl_encode_peaks_em_nrf1}{{A.3}{73}{\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.39}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.4}{\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 }}{74}{figure.caption.40}} +\newlabel{suppl_encode_peaks_em_cfos}{{A.4}{74}{\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.40}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.5}{\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 }}{75}{figure.caption.41}} +\newlabel{suppl_encode_peaks_em_max}{{A.5}{75}{\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. 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The y-axis indicates the min/max signal for all densities.\relax }}{76}{figure.caption.42}} +\newlabel{suppl_encode_peaks_em_brca1}{{A.6}{76}{\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. 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For the DNA logo, this corresponds to 0 and 2 bits respectively.\relax }}{78}{figure.caption.44}} +\newlabel{suppl_emread_sp1_noshift_flip}{{A.8}{78}{\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.44}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {A.9}{\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. 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