diff --git a/main/ch_atac-seq.aux b/main/ch_atac-seq.aux index 6aaf713..9d8324f 100644 --- a/main/ch_atac-seq.aux +++ b/main/ch_atac-seq.aux @@ -1,161 +1,161 @@ \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} \@writefile{toc}{\contentsline {section}{\numberline {7.5}Identifying over-represented signals}{85}{section.7.5}} \@writefile{toc}{\contentsline {subsection}{\numberline {7.5.1}ChIPPartitioning : an algorithm to identify over-represented read patterns}{85}{subsection.7.5.1}} \@writefile{toc}{\contentsline {subsection}{\numberline {7.5.2}EMSequence : an algorithm to identify over-represented sequences}{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. The overall design is the same. Both algorithms model the data has having being sampled from a distribution and perform a maximum-likelihood estimation of the distribution parameters from the data through an iterative procedure. EMJoint algorithm is the combination of both ChIPPartitioning and EMSequence at the same time.\relax }}{86}{figure.caption.36}} \newlabel{atac_seq_em}{{7.1}{86}{\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. The overall design is the same. Both algorithms model the data has having being sampled from a distribution and perform a maximum-likelihood estimation of the distribution parameters from the data through an iterative procedure.\\ EMJoint algorithm is the combination of both ChIPPartitioning and EMSequence at the same time.\relax }{figure.caption.36}{}} \citation{nair_probabilistic_2014} \citation{nair_probabilistic_2014} \citation{nair_probabilistic_2014} \@writefile{toc}{\contentsline {subsubsection}{without shift and flip}{87}{figure.caption.36}} \newlabel{atac_seq_emseq_likelihood}{{7.1}{87}{without shift and flip}{equation.7.5.1}{}} \newlabel{atac_seq_emseq_update_model}{{7.2}{87}{without shift and flip}{equation.7.5.2}{}} \@writefile{toc}{\contentsline {subsubsection}{with shift and flip}{87}{equation.7.5.2}} \newlabel{atac_seq_emseq_likelihood_shift_flip}{{7.3}{87}{with shift and flip}{equation.7.5.3}{}} \citation{nair_probabilistic_2014} \citation{nair_probabilistic_2014} \newlabel{atac_seq_emseq_reverse_motif}{{7.4}{88}{with shift and flip}{equation.7.5.4}{}} \newlabel{atac_seq_emseq_update_model_shift_flip}{{7.5}{88}{with shift and flip}{equation.7.5.5}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {7.5.3}EMJoint : an algorithm to identify over-represented sequences and chromatin architectures}{88}{subsection.7.5.3}} \citation{nair_probabilistic_2014} \citation{nair_probabilistic_2014} \newlabel{atac_seq_emjoint_likelihood}{{7.6}{89}{EMJoint : an algorithm to identify over-represented sequences and chromatin architectures}{equation.7.5.6}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {7.5.4}Data realignment}{89}{subsection.7.5.4}} \citation{voss_dynamic_2014} \citation{cirillo_opening_2002,zaret_pioneer_2011,soufi_pioneer_2015} \@writefile{lof}{\contentsline {figure}{\numberline {7.2}{\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 }}{90}{figure.caption.37}} \newlabel{atac_seq_fragment_size}{{7.2}{90}{\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.37}{}} \@writefile{toc}{\contentsline {section}{\numberline {7.6}Results}{90}{section.7.6}} \@writefile{lof}{\contentsline {figure}{\numberline {7.3}{\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 }}{91}{figure.caption.38}} \newlabel{atac_seq_ctcf_all_data}{{7.3}{91}{\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.38}{}} \citation{buenrostro_transposition_2013} \citation{buenrostro_transposition_2013} \citation{adey_rapid_2010} \citation{buenrostro_transposition_2013,li_identification_2019} \@writefile{toc}{\contentsline {subsection}{\numberline {7.6.1}Fragment size analysis}{92}{subsection.7.6.1}} \@writefile{toc}{\contentsline {subsection}{\numberline {7.6.2}Measuring open chromatin and nucleosome occupancy}{92}{subsection.7.6.2}} \citation{neph_expansive_2012} \citation{fu_insulator_2008} \citation{neph_expansive_2012} \@writefile{lof}{\contentsline {figure}{\numberline {7.4}{\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. 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 }}{93}{figure.caption.39}} \newlabel{atac_seq_ctcf_sp1_myc_ebf1_footprint}{{7.4}{93}{\textbf {Signal around CTCF, SP1, myc and EBF1 motifs :} the human genome was scanned with one PWM per TF to predict their binding sites. 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.39}{}} \citation{kundaje_ubiquitous_2012} \@writefile{toc}{\contentsline {subsection}{\numberline {7.6.3}Evaluation of EMSequence and ChIPPartitioning}{94}{subsection.7.6.3}} \@writefile{lof}{\contentsline {figure}{\numberline {7.5}{\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 }}{95}{figure.caption.40}} \newlabel{atac_seq_emseq_auc_roc}{{7.5}{95}{\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.40}{}} \@writefile{toc}{\contentsline {subsubsection}{EMSequence}{95}{subsection.7.6.3}} \@writefile{lof}{\contentsline {figure}{\numberline {7.6}{\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 }}{96}{figure.caption.41}} \newlabel{atac_seq_emseq_sp1_10class}{{7.6}{96}{\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.41}{}} \citation{kent_blatblast-like_2002} \citation{chatr-aryamontri_biogrid_2017} \citation{castro-mondragon_rsat_2017} \citation{nair_probabilistic_2014} \@writefile{toc}{\contentsline {subsubsection}{ChIPPartitioning}{97}{figure.caption.41}} \@writefile{lof}{\contentsline {figure}{\numberline {7.7}{\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 }}{98}{figure.caption.42}} \newlabel{atac_seq_emread_ctcf_noshift_flip}{{7.7}{98}{\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.42}{}} \@writefile{lof}{\contentsline {figure}{\numberline {7.8}{\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 }}{98}{figure.caption.43}} \newlabel{atac_seq_emread_ctcf_shift_flip}{{7.8}{98}{\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.43}{}} \@writefile{toc}{\contentsline {section}{\numberline {7.7}Aligning the binding sites}{99}{section.7.7}} \@writefile{lof}{\contentsline {figure}{\numberline {7.9}{\ignorespaces \textbf {Central parts of the extended sequence and chromatin models} found in 10'000 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 }}{100}{figure.caption.44}} \newlabel{atac_seq_23class}{{7.9}{100}{\textbf {Central parts of the extended sequence and chromatin models} found in 10'000 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.44}{}} \citation{kurotaki_transcriptional_2017,rico_comparative_2017} \citation{castro-mondragon_rsat_2017} \@writefile{lof}{\contentsline {figure}{\numberline {7.10}{\ignorespaces \textbf {CTCF sub-classes} obtained by extracting CTCF class data and subjecting them to a ChIPPartitioning classification into 8 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 }}{102}{figure.caption.45}} \newlabel{atac_seq_ctcf_subclass}{{7.10}{102}{\textbf {CTCF sub-classes} obtained by extracting CTCF class data and subjecting them to a ChIPPartitioning classification into 8 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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Each model can be retrieved within JASPAR matrix clustering (\url {http://jaspar2018.genereg.net/matrix-clusters/vertebrates/?detail=true}) using the cluster and node ID. "TFs covered" refers to all TF which models are children of the given node. "Name" refers to the label this model is referred to in the text and figures.\relax }}{107}{table.caption.46}} \newlabel{atac_seq_motif_table}{{7.1}{107}{\textbf {TF binding models} from JASPAR matrix clustering. Each model can be retrieved within JASPAR matrix clustering (\url {http://jaspar2018.genereg.net/matrix-clusters/vertebrates/?detail=true}) using the cluster and node ID. "TFs covered" refers to all TF which models are children of the given node. 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\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.33}} \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.33}{}} \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.34}} \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.34}{}} \@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.35}} \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.30}} \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.30}{}} \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. 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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. 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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 }}{73}{figure.caption.32}} -\newlabel{smileseq_auc}{{5.3}{73}{\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.32}{}} -\@writefile{toc}{\contentsline {subsection}{\numberline {5.0.5}Conclusions}{74}{subsection.5.0.5}} +\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.32}} +\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.32}{}} +\@writefile{toc}{\contentsline {section}{\numberline {5.5}Conclusions}{73}{section.5.5}} \@setckpt{main/ch_smile-seq}{ -\setcounter{page}{75} +\setcounter{page}{74} \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}{5} -\setcounter{section}{0} -\setcounter{subsection}{5} +\setcounter{section}{5} +\setcounter{subsection}{0} \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} \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}{2} -\setcounter{algocfproc}{2} -\setcounter{algocf}{2} +\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_smile-seq.tex b/main/ch_smile-seq.tex index 3c3b62e..0930b76 100644 --- a/main/ch_smile-seq.tex +++ b/main/ch_smile-seq.tex @@ -1,137 +1,138 @@ \cleardoublepage \chapter{SMiLE-seq data analysis} \label{smile_seq} \markboth{SMiLE-seq data analysis}{SMiLE-seq data analysis} \addcontentsline{toc}{chapter}{SMiLE-seq data analysis} %As discussed earlier, deciphering gene regulation necessitates to understand and model TFs sequence specificity. Several assay and technologies already exist to measure TF-DNA interactions in vivo (ChIP-seq \citep{robertson_genome-wide_2007}) and in vitro (HT-SELEX \citep{roulet_high-throughput_2002,zhao_inferring_2009,jolma_multiplexed_2010}, PBM \citep{bulyk_nucleotides_2002,berger_compact_2006}). Each assay has it strengths and weaknesses \citep{stormo_determining_2010}. %ChIP-seq data allow to potentially list all binding sites of a TF in a given cell type. However, it has two majors short comings. First, it necessitate a proper antibody to perform the ChIP step. Second, because of the presence of other factors in the chromatin, the data produced not only reflect the intrinsic sequence preferences of the TF but also i) positive interactions with other TFs, ii) competition with other TFs and iii) competition with nucleosomes. %PBM chips are simple to design and manufacture and to get a result from. However their size is constrained, limiting the number of probes and forcing the designer to optimize the sequence of each prob to maximize the number of k-mers contained in a given chip. %Finally, HT-SELEX is also a powerful tool. It allows to obtain, at once, hundreds to thousands of sequences to which a TF is binding. However, repeated SELEX cycles introduce a double bias : i) strong affinity binding sites are exponentially enriched for and ii) a PCR step in between two cycles inevitably replicates some sequences more than others. Additionally, the presence of PCR/sequencing primers in the DNA fragments limits the effective size of the inserts. %To help tackle the issue of better characterizing of monomeric, homodimeric and heterodimeric TFsm binding specificity, Prof Bart Deplancke's laboratory at EPFL developed a microfluidic based technology. This assay - selective microfluidics-based ligand enrichment followed by sequence (SMiLE-seq) - was developed in order to minimize hand-on time, to allow massive parallelization of the measurements and to allow the measurement of TF binding over a wide-range of affinities \citep{isakova_smile-seq_2017}. -The following section contains work made in collaboration with Alina Isakova Prof. Bart Deplancke research group at EPFL and published in \cite{isakova_smile-seq_2017}. I personally realized the presented figures and analyses, with the exception of Figure \ref{smile_seq_pipeline}. +The following section contains work made in collaboration with Alina Isakova Prof. Bart Deplancke research group at EPFL and published in \cite{isakova_smile-seq_2017}. I personally made the presented figures and analyses, with the exception of Figure \ref{smile_seq_pipeline}. -\subsection{Introduction} +\section{Introduction} \begin{figure} \begin{center} \includegraphics[scale=0.25]{images/ch_smile-seq/figure1.jpg} \captionof{figure}{\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}.} \label{smile_seq_pipeline} \end{center} \end{figure} -Deciphering TF binding specificity is key to understand gene regulation. Several technologies exists to study TF specificity in vitro such as mechanically induced trapping of molecular interactions (MITOMI \cite{maerkl_systems_2007}), protein binding-microarray (PBM \cite{berger_universal_2009}) or hight throughput systematic enrichment of ligangs by exponential enrichment (HT-SELEX \cite{zhao_inferring_2009,jolma_multiplexed_2010}). +Deciphering TF binding specificity is key to understand the regulation of gene expression. Several technologies exist to study TF specificity in vitro such as mechanically induced trapping of molecular interactions (MITOMI \cite{maerkl_systems_2007}), protein binding-microarray (PBM \cite{berger_universal_2009}) or hight throughput systematic enrichment of ligangs by exponential enrichment (HT-SELEX \cite{zhao_inferring_2009,jolma_multiplexed_2010}). Because TFs can bind as monomer, homodimer, heterodimer and as higher order complexes, it is critical to have suited technologies to interrogate their binding specificity. Nonetheless, because of combinatorials, this undertaking represent a staggering amount of work. In order to allow robust and easy measurement of TF monomers and dimers sequence specificity, over a wide range of binding affinities, Prof. Bart Deplancke research group at EPFL developed selective microfluidics-based ligand enrichment followed by sequence (SMiLE-seq, \cite{isakova_smile-seq_2017}). An overview of the SMiLE-seq data production and processing procedures is shown in Figure \ref{smile_seq_pipeline}. Overall, three major conceptual featzres should be highlighted. First, to tackle the scalability issue, the SMILe-seq plateform core is microfluidic device that contains hundreds of individual wells allowing to run as many different TF-DNA interaction assays at the same time. Second, the SMiLE-seq assay does not perform an iterative enrichment, as HT-SELEX does. SMiLE-seq is a one round selection system. Third, because TF-DNA interactions happens over a wide range of affinities, the weakest interaction can be lost during washing. In order to prevent this, complexes are protected under a membrane during the washing step. In order to assess the quality of this new technology and its ability to produce relevant data to model TF binding, I ran de novo motif discovery analyses on these data and benchmark their predictive performances. Our laboratory developed a Hidden Markov Model (HMM) based motif discovery method and a binding motif evaluation tool. Both are available on the laboratory web portal \url{http://ccg.vital-it.ch/pwmtools/}. My involvment in this project was to develop a scalable and efficient pipeline, at the backend of our server, to run the motif discovery and benchmark steps and to analyze the results. -\subsection{Hidden Markov Model Motif discovery} +\section{Hidden Markov Model Motif discovery} \label{section_smileseq_hmm} \begin{figure} \begin{center} \includegraphics[scale=0.2]{images/ch_smile-seq/figure_hmm.png} \captionof{figure}{\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}} \label{smile_seq_hmm} \end{center} \end{figure} -This program has been developed an implemented by Philipp Bucher and Giovanna Ambrosini - a senior scientist of the laboratory - for the DREAM5 TF-DNA Motif Recognition Challenge \citep{weirauch_evaluation_2013}. This motif discovery method was awarded the 2$^{nd}$ place in this contest, attesting of its performances. +This motif discovery method is based on an initial work performed by by Philipp Bucher and Giovanna Ambrosini for the DREAM5 TF-DNA Motif Recognition Challenge \citep{weirauch_evaluation_2013} - which was awarded the 2$^{nd}$ place in this contest, attesting of its performances - and extend it. -This motif discovery method models the DNA sequences using an HMM. The sequences are composed of $N$ sequences of length $L$ and the motif is represented as a probability matrix \textbf{M} of dimensions $4 \times L'$ where $L' \leq L$ and with the constrain $\sum_{i=1}^4 m_{i,j} = 1$. +This motif discovery method models the DNA sequences using an HMM. The sequences are composed of $N$ sequences of length $L$ and the motif is represented as a LPM \textbf{M} of dimensions $4 \times K$ where $K \leq L$ and with the constrain $\sum_{i=1}^4 m_{i,j} = 1$. -A sequence is modeled as a mixture of a set of consecutive positions belonging to the binding site (to which the TF binds) and of positions outside the binding site. Since the position of the binding site in each sequence is unknown, it is not possible to align the binding sites and to compute its base composition. Instead, we use an HMM where the hidden state is whether a position in a sequence belongs to the binding site and the emission parameters are the base probabilities at this position inside the binding site. +A sequence is modeled as a mixture of a set of consecutive positions belonging to the binding site (to which the TF binds) and of positions outside the binding site. Since the position of the binding site in each sequence is unknown, they have to be guessed in order to align the binding sites and compute a LPM. Instead, we use an HMM that can handle the hidden information about the position of the binding site within a longer sequence. The emission parameters are the base probabilities at each position inside the binding site. Additionally, to account for experimental biases, such as unspecific binding, a sequence can zero binding site. The entire model is composed of three Markov chains representing each path. Because a motif can occur on the + or the - strand of the DNA sequence, the modeling of the binding site is done by two paired Markov chains. The modification of a parameter in one of these two paired chains is propagated to the equivalent parameters in the other paired chain. An example of HMM is displayed in Figure \ref{smile_seq_hmm}. The parameter estimation given the data is performed using the Baum-Welch algorithm. Mamot \citep{schutz_mamot:_2008}, a dedicated computational framework for HMMs, is used to handle all the computations. Because, the Baum-Welch algorithm performs an iterative optimization of the model parameters, it needs a starting state. The motif length and the starting emission parameters are estimated using an over-represented kmer analysis. The motif length was set to the best kmer length and the starting emission probabilities are set to 0.7 for the base present in the kmer and 0.1 for the three others. -\subsection{Binding motif evaluation} +\section{Binding motif evaluation} \label{section_smileseq_pwmeval} To evaluate and compare different binding models coming from different libraries - computed with different algorithms and data - with the SMiLE-seq data derived binding models, I used PWMEval-ChIP-peak (formerly named PWMEval), a program using a methodology proposed by Orenstein and Shamir \citep{orenstein_comparative_2014} that has been developed in-house and is available at \url{https://ccg.epfl.ch/pwmtools/pwmeval_chippeak.php}. In order to run massive batch analyses, I developed a dedicated pipeline that was run at the backend of our servers. PWMEval takes as input a set $S^{pos}$ of $N$ experimentally validated binding sites sequences of length $L$ - extracted from ChIP-seq peaks - and a set $S^{neg}$ of $N$ presumably non-binding-sites of length $L$ - extracted 300bp downstream of the corresponding peak sequences - and a probability matrix \textbf{M} describing the binding motif. Each positive set sequence $S^{pos}_{i}$ is scored using: \begin{equation} \begin{aligned} - score^{pos}_{i} & = \sum_{l=1}^{L-L'+1} \prod_{k=1}^{K} \frac{m_{b,k}} + score^{pos}_{i} & = \sum_{l=1}^{L-K'+1} \prod_{k=1}^{K} \frac{m_{b,k}} {p_{b}} \\ \text{with } b & = \begin{cases} 1 & \text{if $s^{pos}_{i,l+k-1} = $ A}.\\ 2 & \text{if $s^{pos}_{i,l+k-1} = $ C}.\\ 3 & \text{if $s^{pos}_{i,l+k-1} = $ G}.\\ 4 & \text{if $s^{pos}_{i,l+k-1} = $ T}.\\ \end{cases} \\ \end{aligned} \label{smile_seq_pwmeval_score} \end{equation} The same is done for each negative set sequence $S^{neg}_{i}$, resulting in the creation of two vectors of scores of length $N$ - one for each sequence set. Both vectors are concatenated into a unique vector of scores. Two vector of size $N$, containing the class labels ($N$ times 0 or 1) are created and concatenated as well. Then the vector of labels is sorted according to the decreasing sorting order of the score vector. -If the binding model allows to perfectly segregate the positive from the negative sequences, then we expect the positive sequence scores to be larger than the negative sequence scores. Thus the positive labels should all be at the beginning of the label vector and the negative labels at the end. The propensity of the model to recognize true binding sites from other sequences can be measured by computing the area under the curve (AUC) of the receiver operating characteristic (ROC). Given the list of sorted labels, the true positive and true negative discovery rates can be computed and the AUC-ROC can be computed using the following procedure : - -\SetKwProg{Fn}{}{\{}{}\SetKwFunction{Function}{float AUC-ROC}% -\begin{algorithm}[H] - \label{smile_seq_algo_auc} - \Fn{\Function{LABELS}} - { \KwData{$LABELS$ a vector of $2N$ score-ordered labels encoded as 0 for true negative and 1 for true positives.} - \KwResult{the ROC-AUC} - $TP =$ a vector of $2N$ 0's \; - $TN =$ a vector of $2N$ 0's \; - $n_{tp} =$ 0 \; - $n_{tn} =$ 0 \; - \For{$i=1$ to $2N$} - { \If{$LABELS_{i}$ == 0} - { $n_{tn} +=$ 1 \; } - \Else - { $n_{tp} +=$ 1 \; } - $TP_{i} = n_{tp} / N$ \; - $TN_{i} = n_{tn} / N$ \; - } - $AUC = \frac{U}{N^{2}}$ where $U$ is the Mann-Whitney $U$ statistic for the $TP$ and $TN$ vectors \; - \Return{$AUC$} - } - \caption{Computes the AUC-ROC} -\end{algorithm} - - - -\subsection{Results} +If the binding model allows to perfectly segregate the positive from the negative sequences, then we expect the positive sequence scores to be larger than the negative sequence scores. Thus the positive labels should all be at the beginning of the label vector and the negative labels at the end. The propensity of the model to recognize true binding sites from other sequences can be measured by computing the area under the curve (AUC) of the receiver operating characteristic (ROC). Given the list of sorted labels, the true positive and true negative discovery rates can be computed and the AUC-ROC can be computed as $U/N^{2}$ where $U$ is the Mann-Whitney $U$ statistic for the true positive and true negative discovery rates. + +% \SetKwProg{Fn}{}{\{}{}\SetKwFunction{Function}{float AUC-ROC}% +% \begin{algorithm}[H] +% \label{smile_seq_algo_auc} +% \Fn{\Function{LABELS}} +% { \KwData{$LABELS$ a vector of $2N$ score-ordered labels encoded as 0 for true negative and 1 for true positives.} +% \KwResult{the ROC-AUC} +% $TP =$ a vector of $2N$ 0's \; +% $TN =$ a vector of $2N$ 0's \; +% $n_{tp} =$ 0 \; +% $n_{tn} =$ 0 \; +% \For{$i=1$ to $2N$} +% { \If{$LABELS_{i}$ == 0} +% { $n_{tn} +=$ 1 \; } +% \Else +% { $n_{tp} +=$ 1 \; } +% $TP_{i} = n_{tp} / N$ \; +% $TN_{i} = n_{tn} / N$ \; +% } +% $AUC = \frac{U}{N^{2}}$ where $U$ is the Mann-Whitney $U$ statistic for the $TP$ and $TN$ vectors \; +% \Return{$AUC$} +% } +% \caption{Computes the AUC-ROC} +% \end{algorithm} + + +\section{Results} \begin{figure} \begin{center} \includegraphics[scale=0.2]{images/ch_smile-seq/figure2b_3a.png} \captionof{figure}{\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.} \label{smileseq_auc} \end{center} \end{figure} -In order to assess the robustness of SMiLE-seq, I derived binding models using the HMM motif discovery procedure for all TFs for which ChIP-seq peaks have been released by the ENCODE Consortium were available. For each ChIP-seq peak list, the 500 best peaks were selected based on their confidence score (attributed by ENCODE). The peak score reflects TF occupancy and likely the TF binding affinity to this site. Whenever several ChIP-seq peak lists were available for a given TF, the peak list achieving the highest mean AUC value over all models for that TF was considered. +In order to assess the robustness of SMiLE-seq, I derived binding models using the HMM motif discovery procedure for all TFs for which ChIP-seq peaks have been released by the ENCODE Consortium were available. For each ChIP-seq peak list, the 500 best peaks were selected based on their signal enrichment score (attributed by ENCODE). The peak score reflects TF occupancy and likely the TF binding affinity to this site. Whenever several ChIP-seq peak lists were available for a given TF, the peak list achieving the highest mean AUC value over all models available for that TF was considered. -I compared the SMiLE-seq derived binding models with the equivalent TF binding models from the HT-SELEX Jolma \citep{jolma_dna-binding_2013}, JASPAR 2014 \citep{mathelier_jaspar_2014} and HOCOMOCOv10 \citep{kulakovskiy_hocomoco:_2016} motif collections using the AUC-ROC procedure (Figure \ref{smileseq_auc} A). This analysis reveled that, in the majority of cases, the SMILe-seq derived binding models were doing at least as good (MAX, CEBPb, CTCF, NFkB, ZSCAN1 TCF7, JunD, RXRa) or better (YY1, ZEB1) than available models. +For each TF, LPMs equivalent to the SMiLE-seq derived LPM were selected from the HT-SELEX Jolma \citep{jolma_dna-binding_2013}, JASPAR 2014 \citep{mathelier_jaspar_2014} and HOCOMOCOv10 \citep{kulakovskiy_hocomoco:_2016} motif collections and were compared to the SMiLE-seq derived LPM using the AUC-ROC procedure procedure (Figure \ref{smileseq_auc} A). This analysis reveled that, in the majority of cases, the SMILe-seq derived binding models were doing at least as good (MAX, CEBPb, CTCF, NFkB, ZSCAN1 TCF7, JunD, RXRa) or better (YY1, ZEB1) than available models. + +% I compared the SMiLE-seq derived binding models with the equivalent TF binding models from the HT-SELEX Jolma \citep{jolma_dna-binding_2013}, JASPAR 2014 \citep{mathelier_jaspar_2014} and HOCOMOCOv10 \citep{kulakovskiy_hocomoco:_2016} motif collections using the AUC-ROC procedure (Figure \ref{smileseq_auc} A). This analysis reveled that, in the majority of cases, the SMILe-seq derived binding models were doing at least as good (MAX, CEBPb, CTCF, NFkB, ZSCAN1 TCF7, JunD, RXRa) or better (YY1, ZEB1) than available models. To verify that SMiLE-seq was indeed able to measure binding over a wide functional range of affinities, I computed AUC-ROC using decreasing affinity subsets of ChIP-seq peaks (Figure \ref{smileseq_auc} B and Figure \ref{suppl_smileseq_auc_2}B). For high affinity peak subsets, SMiLE-seq derived models scored as well as others but tend to become better than other models for lower affinity peaks subsets. To further emphasize the quality of the SMiLE-seq technology and support conceptual differences with HT-SELEX, binding models were trained from HT-SELEX data using the HMM motif discovery procedure. The rational was that if the model performances could be attributed to different motif discovery methods, running the HMM motif discovery one on all data should get us ride from this confounding factor. Because SMiLE-seq does not perform iterative enrichment of high affinity binders, 1st cycle HT-SELEX data were used. Here again, SMiLE-seq derived models were at least as good as HT-SELEX models (Figure \ref{suppl_smileseq_auc_2} A). -\subsection{Conclusions} +\section{Conclusions} These results demonstrated that SMiLE-seq is a valuable plateform to run in-vitro TF-DNA binding assays. More specifically, this was demonstrated by showing that SMiLE-seq derived models are as good or better than models derived from ChIP-seq data (such as HOCOMOCO or JASPAR models) or from in vitro data (HT-SELEX). Moreover, it was also possible to support the fact that SMiLE-seq was able to capture interaction over a wide range of affinities. This was shown through the use of differential binding affinity sites in the AUC-ROC analyses. All together, these results support that SMiLE-seq is a valuable technology and a strong competitor for other in vitro TF-DNA interaction assays such as HT-SELEX. \ No newline at end of file diff --git a/my_thesis.aux b/my_thesis.aux index 8b365bb..a18a1e3 100644 --- a/my_thesis.aux +++ b/my_thesis.aux @@ -1,192 +1,192 @@ \relax \providecommand\hyper@newdestlabel[2]{} \providecommand\BKM@entry[2]{} \catcode `:\active \catcode `;\active \catcode `!\active \catcode `?\active \catcode `"\active \providecommand\HyperFirstAtBeginDocument{\AtBeginDocument} \HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined \global\let\oldcontentsline\contentsline \gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}} \global\let\oldnewlabel\newlabel \gdef\newlabel#1#2{\newlabelxx{#1}#2} \gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}} \AtEndDocument{\ifx\hyper@anchor\@undefined \let\contentsline\oldcontentsline \let\newlabel\oldnewlabel \fi} \fi} \global\let\hyper@last\relax 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defining Unicode char U+00DF (decimal 223) defining Unicode char U+00E0 (decimal 224) defining Unicode char U+00E1 (decimal 225) defining Unicode char U+00E2 (decimal 226) defining Unicode char U+00E3 (decimal 227) defining Unicode char U+00E4 (decimal 228) defining Unicode char U+00E5 (decimal 229) defining Unicode char U+00E6 (decimal 230) defining Unicode char U+00E7 (decimal 231) defining Unicode char U+00E8 (decimal 232) defining Unicode char U+00E9 (decimal 233) defining Unicode char U+00EA (decimal 234) defining Unicode char U+00EB (decimal 235) defining Unicode char U+00EC (decimal 236) defining Unicode char U+00ED (decimal 237) defining Unicode char U+00EE (decimal 238) defining Unicode char U+00EF (decimal 239) defining Unicode char U+00F0 (decimal 240) defining Unicode char U+00F1 (decimal 241) defining Unicode char U+00F2 (decimal 242) defining Unicode char U+00F3 (decimal 243) defining Unicode char U+00F4 (decimal 244) defining Unicode char U+00F5 (decimal 245) defining Unicode char U+00F6 (decimal 246) defining Unicode char U+00F8 (decimal 248) defining Unicode char U+00F9 (decimal 249) defining Unicode char U+00FA (decimal 250) defining Unicode char U+00FB (decimal 251) defining Unicode char U+00FC (decimal 252) defining Unicode char U+00FD (decimal 253) defining Unicode char U+00FE (decimal 254) defining Unicode char U+00FF (decimal 255) defining Unicode char U+0100 (decimal 256) defining Unicode char U+0101 (decimal 257) defining Unicode char U+0102 (decimal 258) defining Unicode char U+0103 (decimal 259) defining Unicode char U+0104 (decimal 260) defining Unicode char U+0105 (decimal 261) defining Unicode char U+0106 (decimal 262) defining Unicode char U+0107 (decimal 263) defining Unicode char U+0108 (decimal 264) defining Unicode char U+0109 (decimal 265) defining Unicode char U+010A (decimal 266) defining Unicode char U+010B (decimal 267) defining Unicode char U+010C (decimal 268) defining Unicode char U+010D (decimal 269) defining Unicode char U+010E (decimal 270) defining Unicode char U+010F (decimal 271) defining Unicode char U+0110 (decimal 272) defining Unicode char U+0111 (decimal 273) defining Unicode char U+0112 (decimal 274) defining Unicode char U+0113 (decimal 275) defining Unicode char U+0114 (decimal 276) defining Unicode char U+0115 (decimal 277) defining Unicode char U+0116 (decimal 278) defining Unicode char U+0117 (decimal 279) defining Unicode char U+0118 (decimal 280) defining Unicode char U+0119 (decimal 281) defining Unicode char U+011A (decimal 282) defining Unicode char U+011B (decimal 283) defining Unicode char U+011C (decimal 284) defining Unicode char U+011D (decimal 285) defining Unicode char U+011E (decimal 286) defining Unicode char U+011F (decimal 287) defining Unicode char U+0120 (decimal 288) defining Unicode char U+0121 (decimal 289) defining Unicode char U+0122 (decimal 290) defining Unicode char U+0123 (decimal 291) defining Unicode char U+0124 (decimal 292) defining Unicode char U+0125 (decimal 293) defining Unicode char U+0128 (decimal 296) defining Unicode char U+0129 (decimal 297) defining Unicode char U+012A (decimal 298) defining Unicode char U+012B (decimal 299) defining Unicode char U+012C (decimal 300) defining Unicode char U+012D (decimal 301) defining Unicode char U+012E (decimal 302) defining Unicode char U+012F (decimal 303) defining Unicode char U+0130 (decimal 304) defining Unicode char U+0131 (decimal 305) defining Unicode char U+0132 (decimal 306) defining Unicode char U+0133 (decimal 307) defining Unicode char U+0134 (decimal 308) defining Unicode char U+0135 (decimal 309) defining Unicode char U+0136 (decimal 310) defining Unicode char U+0137 (decimal 311) defining Unicode char U+0139 (decimal 313) defining Unicode char U+013A (decimal 314) defining Unicode char U+013B (decimal 315) defining Unicode char U+013C (decimal 316) defining Unicode char U+013D (decimal 317) defining Unicode char U+013E (decimal 318) defining Unicode char U+0141 (decimal 321) defining Unicode char U+0142 (decimal 322) defining Unicode char U+0143 (decimal 323) defining Unicode char U+0144 (decimal 324) defining Unicode char U+0145 (decimal 325) defining Unicode char U+0146 (decimal 326) defining Unicode char U+0147 (decimal 327) defining Unicode char U+0148 (decimal 328) defining Unicode char U+014A (decimal 330) defining Unicode char U+014B (decimal 331) defining Unicode char U+014C (decimal 332) defining Unicode char U+014D (decimal 333) defining Unicode char U+014E (decimal 334) defining Unicode char U+014F (decimal 335) defining Unicode char U+0150 (decimal 336) defining Unicode char U+0151 (decimal 337) defining Unicode char U+0152 (decimal 338) defining Unicode char U+0153 (decimal 339) defining Unicode char U+0154 (decimal 340) defining Unicode char U+0155 (decimal 341) defining Unicode char U+0156 (decimal 342) defining Unicode char U+0157 (decimal 343) defining Unicode char U+0158 (decimal 344) defining Unicode char U+0159 (decimal 345) defining Unicode char U+015A (decimal 346) defining Unicode char U+015B (decimal 347) defining Unicode char U+015C (decimal 348) defining Unicode char U+015D (decimal 349) defining Unicode char U+015E (decimal 350) defining Unicode char U+015F (decimal 351) defining Unicode char U+0160 (decimal 352) defining Unicode char U+0161 (decimal 353) defining Unicode char U+0162 (decimal 354) defining Unicode char U+0163 (decimal 355) defining Unicode char U+0164 (decimal 356) defining Unicode char U+0165 (decimal 357) defining Unicode char U+0168 (decimal 360) defining Unicode char U+0169 (decimal 361) defining Unicode char U+016A (decimal 362) defining Unicode char U+016B (decimal 363) defining Unicode char U+016C (decimal 364) defining Unicode char U+016D (decimal 365) defining Unicode char U+016E (decimal 366) defining Unicode char U+016F (decimal 367) defining Unicode char U+0170 (decimal 368) defining Unicode char U+0171 (decimal 369) defining Unicode char U+0172 (decimal 370) defining 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(/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. 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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. 'mm' on line 2779. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chlewis with sig. 'O{}mm' on line 3334. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \setchemformula with sig. 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{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}{\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 component 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}Measuring TF binding in vivo}{13}{subsection.1.4.2} \contentsline {subsection}{\numberline {1.4.3}Measuring TF binding in vitro}{14}{subsection.1.4.3} \contentsline {subsection}{\numberline {1.4.4}Digital footprinting}{15}{subsection.1.4.4} \contentsline {section}{\numberline {1.5}Modeling sequence specificity}{17}{section.1.5} \contentsline {subsubsection}{The physics approach to PWMs}{17}{section.1.5} \contentsline {subsubsection}{The statistical mechanic approach to PWMs}{18}{equation.1.5.1} \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}{20}{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}{26}{subsection.2.1.2} \contentsline {section}{\numberline {2.2}Eukaryotic Promoter Database}{27}{section.2.2} \contentsline {subsection}{\numberline {2.2.1}EPDnew now annotates (some of) your mushrooms and vegetables}{28}{subsection.2.2.1} \contentsline {subsection}{\numberline {2.2.2}Increased mapping precision in human}{29}{subsection.2.2.2} \contentsline {subsection}{\numberline {2.2.3}Integration of EPDnew with other resources}{29}{subsection.2.2.3} \contentsline {subsection}{\numberline {2.2.4}Conclusions}{30}{subsection.2.2.4} \contentsline {subsection}{\numberline {2.2.5}Methods}{30}{subsection.2.2.5} \contentsline {subsubsection}{Motif occurrence profiles}{30}{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 {subsection}{\numberline {5.0.1}Introduction}{67}{subsection.5.0.1} -\contentsline {subsection}{\numberline {5.0.2}Hidden Markov Model Motif discovery}{69}{subsection.5.0.2} -\contentsline {subsection}{\numberline {5.0.3}Binding motif evaluation}{70}{subsection.5.0.3} -\contentsline {subsection}{\numberline {5.0.4}Results}{72}{subsection.5.0.4} -\contentsline {subsection}{\numberline {5.0.5}Conclusions}{74}{subsection.5.0.5} +\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 : an algorithm to identify over-represented read patterns}{85}{subsection.7.5.1} \contentsline {subsection}{\numberline {7.5.2}EMSequence : an algorithm to identify over-represented sequences}{85}{subsection.7.5.2} \contentsline {subsubsection}{without shift and flip}{87}{figure.caption.36} \contentsline {subsubsection}{with shift and flip}{87}{equation.7.5.2} \contentsline {subsection}{\numberline {7.5.3}EMJoint : an algorithm to identify over-represented sequences and chromatin architectures}{88}{subsection.7.5.3} \contentsline {subsection}{\numberline {7.5.4}Data realignment}{89}{subsection.7.5.4} \contentsline {section}{\numberline {7.6}Results}{90}{section.7.6} \contentsline {subsection}{\numberline {7.6.1}Fragment size analysis}{92}{subsection.7.6.1} \contentsline {subsection}{\numberline {7.6.2}Measuring open chromatin and nucleosome occupancy}{92}{subsection.7.6.2} \contentsline {subsection}{\numberline {7.6.3}Evaluation of EMSequence and ChIPPartitioning}{94}{subsection.7.6.3} \contentsline {subsubsection}{EMSequence}{95}{subsection.7.6.3} \contentsline {subsubsection}{ChIPPartitioning}{97}{figure.caption.41} \contentsline {section}{\numberline {7.7}Aligning the binding sites}{99}{section.7.7} \contentsline {section}{\numberline {7.8}Exploring individual TF classes}{103}{section.7.8} \contentsline {section}{\numberline {7.9}Discussions}{103}{section.7.9} \contentsline {section}{\numberline {7.10}Perspectives}{104}{section.7.10} \contentsline {section}{\numberline {7.11}Methods}{105}{section.7.11} \contentsline {subsection}{\numberline {7.11.1}Partitioning programs}{105}{subsection.7.11.1} \contentsline {subsection}{\numberline {7.11.2}Fragment classes}{105}{subsection.7.11.2} \contentsline {subsection}{\numberline {7.11.3}Simulated sequences}{106}{subsection.7.11.3} \contentsline {subsection}{\numberline {7.11.4}Realignment using JASPAR motifs}{106}{subsection.7.11.4} \contentsline {subsection}{\numberline {7.11.5}Model extension}{108}{subsection.7.11.5} \contentsline {subsection}{\numberline {7.11.6}Extracting data assigned to a class}{108}{subsection.7.11.6} \contentsline {subsection}{\numberline {7.11.7}Peak processing}{111}{subsection.7.11.7} \contentsline {subsection}{\numberline {7.11.8}Per TF classes}{111}{subsection.7.11.8} \contentsline {subsection}{\numberline {7.11.9}Per TF sub-classes}{111}{subsection.7.11.9} \contentsline {chapter}{\numberline {8}Discussion}{113}{chapter.8} \contentsline {chapter}{Discussions}{113}{chapter.8} \vspace {\normalbaselineskip } \contentsline {chapter}{\numberline {A}An appendix}{117}{appendix.A} \contentsline {section}{\numberline {A.1}Supplementary figures}{117}{section.A.1} \contentsline {chapter}{Bibliography}{149}{section*.66} \contentsline {chapter}{Bibliography}{161}{appendix*.67} \contentsline {chapter}{Curriculum Vitae}{163}{section*.68} diff --git a/tail/appendix.aux b/tail/appendix.aux index d83a219..53f3f32 100644 --- a/tail/appendix.aux +++ b/tail/appendix.aux @@ -1,112 +1,112 @@ \relax \providecommand\hyper@newdestlabel[2]{} \citation{jolma_dna-binding_2013} \citation{jolma_dna-binding_2013} \@writefile{toc}{\contentsline {chapter}{\numberline {A}An appendix}{117}{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}{117}{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 }}{117}{figure.caption.47}} \newlabel{suppl_smileseq_auc_2}{{A.1}{117}{\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.47}{}} \@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 }}{118}{figure.caption.48}} \newlabel{suppl_encode_peaks_em_ctcf}{{A.2}{118}{\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.48}{}} \@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 }}{119}{figure.caption.49}} \newlabel{suppl_encode_peaks_em_nrf1}{{A.3}{119}{\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.49}{}} \@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 }}{120}{figure.caption.50}} \newlabel{suppl_encode_peaks_em_cfos}{{A.4}{120}{\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.50}{}} \@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 }}{121}{figure.caption.51}} \newlabel{suppl_encode_peaks_em_max}{{A.5}{121}{\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.51}{}} \@writefile{lof}{\contentsline {figure}{\numberline {A.6}{\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 }}{122}{figure.caption.52}} \newlabel{suppl_encode_peaks_em_brca1}{{A.6}{122}{\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.52}{}} \@writefile{lof}{\contentsline {figure}{\numberline {A.7}{\ignorespaces \textbf {Nucleosome occupancy around CTCF peaks } measured by MNase-seq, in bins of 10bp. The nucleosome depleted region is displayed in blue.\relax }}{123}{figure.caption.53}} \newlabel{suppl_encode_peaks_ctcf_ndr}{{A.7}{123}{\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.53}{}} \citation{khan_jaspar_2018} \citation{khan_jaspar_2018} \@writefile{lof}{\contentsline {figure}{\numberline {A.8}{\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 }}{124}{figure.caption.54}} \newlabel{suppl_encode_peaks_jund_association}{{A.8}{124}{\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.54}{}} \@writefile{lof}{\contentsline {figure}{\numberline {A.9}{\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). 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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 }}{141}{figure.caption.58}} \newlabel{suppl_emread_sp1_noshift_flip}{{A.12}{141}{\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. 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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 }{figure.caption.59}{}} \@writefile{lof}{\contentsline {figure}{\numberline {A.14}{\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 }}{142}{figure.caption.60}} \newlabel{suppl_atac_seq_emseq_best_motifs}{{A.14}{142}{\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.60}{}} \@writefile{lof}{\contentsline {figure}{\numberline {A.15}{\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.61}} \newlabel{suppl_emseq_sp1_10class}{{A.15}{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.61}{}} \@writefile{lof}{\contentsline {figure}{\numberline {A.16}{\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 }}{144}{figure.caption.62}} \newlabel{suppl_emseq_sp1_10class}{{A.16}{144}{\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.62}{}} \@writefile{lof}{\contentsline {figure}{\numberline {A.17}{\ignorespaces \textbf {Extended sequence and chromatin models} found in 10'000 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.17}{145}{\textbf {Extended sequence and chromatin models} found in 10'000 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.18}{\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.18}{146}{\textbf {PU.1 sub-classes} obtained by extracting PU.1 class data and subjecting them to a ChIPPartitioning classification into 2 classes. 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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.19}{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. 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