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I present the basements of a computational framework to analyse chromatin organization around TF binding sites from ATAC-seq data. As a matter of fact, the results presented here are quite preliminary. However, in the best case, this may shape a basis for other projects. % Because reporting these results, even if incomplete, is at least useless and at most useful to the scientific community, there is no reason not to present them. \section{Monitoring TF binding} -As discussed above (see section \ref{intro_dgf}), DGF assays are able to highlight active regulatory elements from an entire genome, at once. However, this comes with the price of an information loss. First, even if we can identify active loci likely to be bound by TFs, we have no direct idea about the identities of the TFs bound. Second, we have no idea about the function of those regions. These regions may act as transcriptional activator or repressor. This activity is ultimately bared by the TF and other complexes bound. Thus delineating a region function necessitate to identify the TFs bound here. +As discussed above (see section \ref{intro_dgf}), DGF assays are able to highlight active regulatory elements from an entire genome, at once. However, this comes at the price of an information loss. First, even if we can identify active loci likely to be bound by TFs, we have no direct information about the identity of the bound TF(s). Second, we have no idea about the function of those regions. These regions may act as transcriptional activator or repressor. This activity is ultimately bared by the TF and other complexes bound. Thus delineating a region function necessitate to identify the TFs bound here. -This task, even if difficult, can be undertaken by implementing dedicated strategies. First, it is possible to collect evidences about the identity of TF likely to bind at a given location through a motif analysis. TFs can bind DNA directly through their own DNA binding domain or indirectly, through an interaction with at least one other partner TF which binds DNA directly \citep{neph_expansive_2012}. For a given TF, direct binding events can be detected by monitoring the presence of a binding motif if a specificity model is available. Thus a footprint baring a motif is likely to reflect a direct binding event. However, this method has two important limitations : i) related TF often share a common DNA specificity and ii) this does not detect indirect binding events. However, evidences about the presence of biggest complexes can be collected by studying the size of the footprint. Large complexes should leave large footprints. This approach, even if limited is able to pinpoint a handful of candidate TFs. +This task, even if difficult, can be undertaken by implementing dedicated strategies. First, it is possible to collect evidence about the identity of TF likely to bind at a given location through a motif analysis. TFs can bind DNA directly through their own DNA binding domain or indirectly, through an interaction with at least one other partner TF which binds DNA directly \citep{neph_expansive_2012}. For a given TF, direct binding events can be detected by monitoring the presence of a binding motif if a specificity model is available. Thus a footprint bearing a motif is likely to reflect a direct binding event. However, this method has two important limitations : i) related TF often share a common DNA specificity and ii) this does not detect indirect binding events. However, evidence about the presence of large complexes can be collected by studying the size of the footprint. Large complexes should leave large footprints. This approach, even if limited is able to pinpoint a handful of candidate TFs. -Second, deciphering the functions of the regulatory elements can be undertaken by looking at the footprint produced by a given factor. Indeed, previous studies have showed that activator and repressor TFs tend to produce different types of footprints \citep{berest_quantification_2018}. Also, the spatial positioning of TF motif within the footprint seemed to be linked with the factor functions \citep{grossman_positional_2018}. For instance, factors associated with the regulation of transcription tend to have a motif in the middle of the footprint whereas factors known to interact with chromatin remodeling factors tend to have a footprint at the edge of the footprint, in contact with the surrounding nucleosomes. +Second, it had been suggested that the regulatory function of a TF can be deciphered by looking at its footprint. Indeed, previous studies have showed that activator and repressor TFs tend to produce different types of footprints \citep{berest_quantification_2018}. Also, the spatial positioning of TF motif within the footprint seemed to be linked with the factor functions \citep{grossman_positional_2018}. For instance, factors associated with the regulation of transcription tend to have a motif in the middle of the footprint whereas factors known to interact with chromatin remodeling factors tend to have a footprint at the edge of the footprint, in contact with the surrounding nucleosomes. \section{The advent of single cell DGF} Recently, the advent of single-cell (sc) sequencing technologies have been a real game changer in the field of life science. These technological advances allowed to measure gene expression and chromatin accessibility (scATAC-seq) at a yet unprecedented resolution. As bulk sequencing was providing an average overview of what was going on, single-cell sequencing allows to monitor what is happening in each cell of a population. This advance had a profound impact on genomics for two reasons. First, for the really first time, the heterogeneity of a cell population became accessible and could be studied at the chromatin, transcriptional and protein levels. Second, the possibility of collecting high dimensionality data from tenth of thousands of individual cells allows genomics to fully enter in the modern big data era, making commonly used machine learning methods usable as the number of parameters to estimate in the models became smaller than the number of individuals, in this case cells, in the data \citep{angerer_single_2017}. % \section{A quick overview of scATAC-seq data analysis} % So far, most of the single cell technologies are targeted at measuring gene expression through scRNA-seq. Naturally, dedicated algorithms and computational methods have been developed to analyze these data. Currently, the most common types of analyses made are i) data projections and dimensionality reduction such as principal component analysis (PCA), t-stochastic distributed neighbours embedding (t-SNE) or uniform manifold approximation and projection (UMAP) and ii) cell population detection by clustering the cells based on the expression of genes \citep{fan_characterizing_2016, kiselev_sc3:_2017}, by reconstructing gene regulation network \citep{aibar_scenic:_2017} or by identifying cellular states based on the accessible region motif content \citep{gonzalez-blas_cistopic:_2019}. In all cases, the use of scATAC-seq data is to determined whether a region is accessible or not. The downstream analyses characterizes the accessible region using i) the number of reads mapping in these regions as a measure of the accessibility or ii) the sequence content within these accessible regions to determine regulatory topics. % \section{Open questions} % \begin{figure}[!htbp] % \begin{center} % \includegraphics[scale=0.5]{images/ch_atac-seq/pipeline.png} % \captionof{figure}{\textbf{framework to identify chromatin organization and use them to annotate cellular state :} the scATAC-seq data available in each individual cell are aggregated and used a if it was a bulk sequencing experiment. Regions of interest are listed using peak calling on the the bulk data. The read densities in these regions (center of the peaks +/- a given offset) are measured. The regions are then clustered based on their signal shape to identify different chromatin architectures and create a catalog. These chromatin signatures can then be used to annotate each region of interest in each cell, based on the signal resemblance. The information can be stored as a matrix (M) that can be used for downstream analyses, such as sub-population identification.} % \label{atac_seq_pipeline} % \end{center} % \end{figure} % All these methods have shown good performances to identify know and new cell populations [REFERENCES]. However, some issues remains open. First, none of these methods uses DGF data to identify different types of footprints or chromatin architecture, in terms of signal shape, at the single cell level. Second, ATAC-seq measures chromatin accessibility but also provides information about the nucleosome occupancy at accessible genomic regions \citep{buenrostro_transposition_2013}. Thus counting the number of reads mapping at a given loci is, indeed, an indication of accessibility but it does use only a small fraction of the available information. Finally, to date, no study has tried to determine whether what is observed at the bulk level can also be seen at the individual cell level and whether this can be used to infer the molecular state of the cells. % In this project, I designed and developed the basements of a computational framework to construct a catalog of prototypical chromatin architectures from single-cell data that can later on be used to annotate individual regions, in single cell. Such a method can be useful to determine cellular molecular state and to group cells accordingly. The entire pipeline is illustrate in Figure \ref{atac_seq_pipeline}. \section{Open issues} I identified two interesting question with regard to ATAC-seq data. First, in the previous chapters, I studied how chromatin is organized in the vicinity of TF binding sites using a pretty standard combination of ChIP-seq, DNase-seq and MNase-seq data. However, I wanted to asses to what extend the same could be done from ATAC-seq data which are cheaper and easier to produce. Second, I wonder to what extent single-cell data could be pooled together and used as a bulk sequencing experiment. \section{Data} To this end, I choose to work with a publicly available sc-ATAC-seq dataset from 5'000 human blood monocytes from a healthy donor. These data have been produced by 10xGenomics (\url{https://www.10xgenomics.com}). % 10xGenomics is one of the most promising and fast growing company specialized in sequencing technologies in the San Francisco Bay area \citep{hepler_10x_2018}. The core activity is to sell sequencing technologies and data analysis softwares to public and private entities. To advertise their products, 10xGenomics offer a free access to several high quality single cell datasets. -10xGenomics is a company active in the field of sequencing technologies and data analysis softwares. To demonstrate the capabilities of their sequencing and bioinformatics analysis technologies, 10xGenomics offer a free access to several high quality single cell datasets together with their analysis results. Thus pre-processing steps such as mapping, cell demultiplexing, sequencing adapters trimming, quality control checks have already been performed. Thus working with these data require minimum handling. Additionally, some downstream analyses such as peak calling or clustering have already been performed. For these reasons, their datasets offer all the conditions to be used as a standard to develop and benchmark new analyses methods. +10xGenomics is a company active in the field of sequencing technologies and data analysis softwares. To demonstrate the capabilities of their sequencing and bioinformatics analysis technologies, 10xGenomics offers a free access to several high quality single cell datasets together with their analysis results. Thus pre-processing steps such as mapping, cell demultiplexing, sequencing adapters trimming, quality control checks have already been performed. Thus working with these data require minimum handling. Additionally, some downstream analyses such as peak calling or clustering have already been performed. For these reasons, their datasets offer all the conditions to be used as a standard to develop and benchmark new analyses methods. -Hg19 mapped reads were downloaded in bam format as well as the corresponding peaks called on the aggregated data. +Hg19 mapped reads were downloaded in bam format as well as the corresponding peaks, in bed format, called on the aggregated data. \section{Identifying over-represented signals} -The study of signal shape (distribution) has been a quite active field for bulk sequencing experiments during the last decade. Dedicated algorithms \citep{hon_chromasig:_2008} \citep{nielsen_catchprofiles:_2012} \citep{kundaje_ubiquitous_2012} \citep{nair_probabilistic_2014} \citep{groux_spar-k:_2019} have been developed to cluster genomic regions based on their distribution of reads. The major issue faced where i) to assess whether two regions are similar in terms of sequencing signal, they have to be properly aligned and ii) oriented and iii) there may be region-specific sequencing depth differences. Nonetheless, these algorithms allow to capture important trends in the data and to collect evidence about the underlying biological mechanisms at play. +The study of signal shape (distribution) has been a quite active field for bulk sequencing experiments during the last decade. Dedicated algorithms \citep{hon_chromasig:_2008} \citep{nielsen_catchprofiles:_2012} \citep{kundaje_ubiquitous_2012} \citep{nair_probabilistic_2014} \citep{groux_spar-k:_2019} have been developed to cluster genomic regions based on their distribution of reads. As discussed in section \ref{intro_pattern_discovery}, the major issue faced are that i) to assess whether two regions are similar in terms of sequencing signal, they have to be properly aligned and ii) oriented and iii) there may be region-specific sequencing depth differences. Nonetheless, these algorithms allow to capture important trends in the data and to collect evidence about the underlying biological mechanisms at play. \subsection{ChIPPartitioning algorithm} -ChIPPartitioning is an algorithm that has been developed by \cite{nair_probabilistic_2014} to classify regions based on their sequencing profiles and to identify archetypical sequencing densities (or models). Because the algorithm is already presented details in section \ref{encode_peaks_chippartitioning}, it will not be discussed further here. Nonetheless, the reader is invited to read the above mentioned section in order to properly understand the points discussed below. +ChIPPartitioning is an algorithm that has been developed by \cite{nair_probabilistic_2014} to classify regions based on their sequencing read densities and to identify archetypical sequencing densities (or models). Because the algorithm is already presented in details in section \ref{encode_peaks_chippartitioning}, it will not be discussed further here. Nonetheless, the reader is invited to read the above mentioned section in order to properly understand the points discussed below. % Most of the above mentioned algorithms and softwares deal with some of these issues. However, ChIPPartitioning \citep{nair_probabilistic_2014} (see section \ref{encode_peaks_chippartitioning}) is really interesting. It is a probabilistic partitioning method that softly clusters a sets of genomic regions represented as a vector of counts corresponding to the number of reads (ChIP-seq, DNase-seq) along them. The regions clustered based on their signal shape resemblance. To ensure proper comparisons between the regions, the algorithm allows to offset one region compare to the other to retrieve a similar signal at different offsets and to flip the signal orientation. Finally, it has been demonstrated to be really robust to sparse data. % This algorithm models the signal over a region of length $L$ has having being sampled from a mixture of $K$ signal models, using $L$ independent Poisson distributions. The number of reads sequenced over this region is then the result of this sampling process. The entire set of regions is assumed to have been generated from a mixture of $K$ different signal models (classes). Each class is represented by a vector of $L' \le L$ values that represent the expected number of reads at each position for that class. These values are thus the Poisson distribution parameters. % In order to discover the $K$ different chromatin signatures in the data, the algorithm proceed to a maximum likelihood estimation of the Poisson distribution parameters using an expectation-maximization (EM) framework. Given a set of $K$ models, the likelihoods of each region given each class is computed. A posterior probability of each class given each region can, in turn, be computed. These probabilities can be interpreted as a soft clustering. The parameters of the classes are updated using a weighted aggregation of the signal. Since each region is computed a probability to belong to each class, it participates to the update of all the classes, with different weights. % If the length of the chromatin signature searched $L' | cut -d ',' -f 1" where is the downloaded file. +The reads mapped to hg19 genome were downloaded, in bam format, from 10xGenomics website (\url{http://s3-us-west-2.amazonaws.com/10x.files/samples/cell-atac/1.1.0/atac_v1_pbmc_5k/atac_v1_pbmc_5k_possorted_bam.bam}). The corresponding peaks called on the aggregated data were downloaded, in bed format, from \url{http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_v1_pbmc_5k/atac_v1_pbmc_5k_peaks.bed}. A file containing cell barcode related information was downloaded from \url{http://cf.10xgenomics.com/samples/cell-atac/1.0.1/atac_v1_pbmc_5k/atac_v1_pbmc_5k_singlecell.csv} and the barcode sequences extracted using "grep -E \_cell\_[0-9]+ | cut -d ',' -f 1" where is the downloaded file. %genome -The hg19 genome sequence was downloaded from the Ensembl ftp at \url{ftp://ftp.ensembl.org/pub/grch37/current/fasta/homo_sapiens/dna/}. Chromosome 1, 2, ..., 22, X and Y sequences were downloaded in fasta format and concatenated together. The sequence headers were then formatted to fit a "chr" format where is 1, 2, ..., 22, X or Y as in the bed file containing the peaks. +The hg19 genome sequence was downloaded from the Ensembl ftp at \url{ftp://ftp.ensembl.org/pub/grch37/current/fasta/homo_sapiens/dna/}. Chromosome 1, 2, ..., 22, X and Y sequences were downloaded in fasta format and concatenated together. The sequence headers were then formatted to fit a "chr" format where is 1, 2, ..., 22, X or Y to correspond to the sequence field values in the bed file containing the peaks. % repeat mask The list of repeated elements for hg19 was downloaded from USCS Genome Browser Table Browser (\url{http://genome.ucsc.edu/cgi-bin/hgTables}). The parameters were set as follows : i) clade to mammal, ii) genome to human, iii) assembly to hg19, iv) group to repeat, v) track to repeatMasker. Finally all the regions that were not mapped to chromosome 1 to 22, M, X or Y were filtered out. \subsection{Data post-processing} % reads The reads that did not have a proper barcode were filtered out using "python3.6 filter\_bam.py -i --tag CB --values -o " where is the bam file containing the reads, the file containing the barcodes created with the above grep command and is the output bam file. filter\_bam.py is a in-house developed python program for this project. % peaks -The peak name field was modified such that the values corresponding to a chromosome name followed a "chrN" format using "sed -E s/\^{}\textbackslash([0-9XY])/chr\textbackslash\textbackslash1/". Then only the peaks mapping to chromosome contigs were selected using "grep -E \^{}chr". The peaks that had at least 30\% overlap with any repeated element were filtered out using "bedtools substract -A -f 0.3 -a -b " where and are the peak and repeat files in bed format, from the bedtools suite \citep{quinlan_bedtools:_2010}. Finally, the peaks were sortede by position using "sort -K 1,1V -k2,2n -k3,3n". +The peak name field was modified such that the values corresponding to a chromosome name followed a "chrN" format using "sed -E s/\^{}\textbackslash([0-9XY])/chr\textbackslash\textbackslash1/". Then only the peaks mapping to chromosome contigs were selected using "grep -E \^{}chr". + +The peaks that had at least 30\% overlap with any repeated element were filtered out using "bedtools substract -A -f 0.3 -a -b " where and are the peak and repeat files in bed format, from the bedtools suite \citep{quinlan_bedtools:_2010}. Finally, the peaks were sortede by position using "sort -K 1,1V -k2,2n -k3,3n". \subsection{Model extension} \label{atac_seq_method_model_ext} -Let's assume that we have partitioned a read density matrix $R$ of dimensions $NxL$ using $K$ classes, a shifting freedom $S$ and with flipping. The posterior matrix probability $P$ has dimensions $NxKxSx2$ and the $K$ models have a length of $L'=L-S+1$. +Let's assume that we have partitioned a read density matrix $R$ of dimensions $NxL$ using $K$ classes, with a shifting freedom $S$ and with flipping. The posterior matrix probability $P$ has dimensions $NxKxSx2$ (region, class, shift, flip) and the $K$ models have a length of $L'=L-S+1$. Extending the models is obtained by computing a larger matrix $R^{ext}$ of dimensions $NxL''$ where $L''=L+E$. In this case $E$ is the extra number of columns to add. Care should be taken to construct $R^{ext}$ by adding exactly $E/2$ columns one each side of $R$ such that $R$ is contained in the central part of $R^{ext}$. Once $R^{ext}$ has been constructed, the model update step (equation 11 in \cite{nair_probabilistic_2014}) can be applied on it using the posterior probability matrix $P$. This will results in the creation of $K$ models of length $L''-S+1$. The $K$ original models will be contained in the central part of the $K$ extended models. Extending a sequence model is done using exactly the same procedure with the exception that equation \ref{atac_seq_emseq_update_model_shift_flip} should be used to compute the models. The read and sequence model extension procedures are implemented in C++ in the ReadModelExtender and SequenceModelExtender programs. \subsection{Extracting data assigned to a class} % The difference between hard and soft clustering (such as ChIPPartitioning) methods is that in soft clustering "the output is a membership function, so each pattern can belong to more than one group with varying degrees of membership" \citep{dalton_clustering_2009}, while in hard clustering each pattern is assigned to only one group. In the former case, isolating all regions assigned to a class $X$, creating a matrix of read density and re-running the clustering method on this matrix is straightforward and would do the trick. In the latter case, this is also possible but requires to account for the feature described above. % Let's assume that a first matrix $R$ of dimensions $NxL$ containing $N$ regions of length $L$ has been partitioned in $K$ classes by ChIPPartitioning, with shifting freedom $S -o --length -" where is the bam file containing the reads, the output bam and split\_by\_size.py is a program developed for this project. For the open chromatin fragments, and were set to 30 and 84. For the mono-nucleosome fragments and were set to 133 and 266. Finally, for the di-nucleosome fragments and were set to 341 and 500. In order to create a nucleosome fragment set, the di-nucleosome fragments were cut in two at their center position using "python3.6 split\_in\_two.py -i -o " where is the bam file containing the di-nucleosome fragments and is the output bam file containing the the reads corresponding to the fragments cut in two. "split\_in\_two.py" is a in-house developed python program for this projet. This file was then sorted using "samtools sort " from the samtools suite \citep{li_sequence_2009}. The sorted file was then merged with the mono-nucleosome fragments using "samtools sort " where is the bam file containing the mono-nucleosome fragments and is the sorted bam file containing the di-nucleosome fragments split in two. Finally, the open chromatin fragment and nucleosome fragment bam files were indexed using "samtools index " where is the bam file to sort. \subsection{Simulated sequences} -2'000 synthetic DNA sequences of 100bp long were simulated. Two equiprobable classes were created and each sequence was assign to either of the two classes. Each class was defined by a 8bp sequence motif (Figure \ref{suppl_atac_seq_emseq_best_motifs}). Each sequence had exactly one motif occurrence, anywhere in the sequence (with a uniform probability), on either strand (equiprobable). The motif sequence was sampled using the corresponding class model. Finally, the bases outside the sequence were sampled using a mono-nucleotide model with 0.25 probability for each base. +2'000 synthetic DNA sequences of 100bp long were generated. Two equiprobable classes were created and each sequence was assign to either of the two classes. Each class was defined by a 8bp sequence motif (Figure \ref{suppl_atac_seq_emseq_best_motifs}). Each sequence had exactly one motif occurrence, anywhere in the sequence (with a uniform probability), on either strand (equiprobable). The motif sequence was sampled using the corresponding class model. Finally, the bases outside the sequence were sampled using a mono-nucleotide model with 0.25 probability for each base. \subsection{Binding site prediction} \label{atac_seq_method_pwmscan} -The hg19 genome was scanned on both strands using PWMScan \citep{ambrosini_pwmscan:_2018} from its web interface (\url{https://ccg.epfl.ch/pwmtools/pwmscan.php}) to predict the binding sites of CTCF, EBF1, myc and SP1 using PWMs from the JASPAR 2018 collection \citep{khan_jaspar_2018} (MA0139.1 CTCF, MA0154.3 EBF1, MA0147.3 MYC and MA0079.3 SP1 respectively). The reference position was set to 10, 7, 6 and 6 respectively and the threshold was set to a pvalue of $1^{-6}$ for all TFs excepted for SP1 for which a pvalue of $1^{-7}$ was used. Non-overlapping matches option was enabled. +The hg19 genome was scanned on both strands using PWMScan \citep{ambrosini_pwmscan:_2018} from its web interface (\url{https://ccg.epfl.ch/pwmtools/pwmscan.php}) to predict the binding sites of CTCF, EBF1, myc and SP1 using PWMs from the JASPAR 2018 collection \citep{khan_jaspar_2018} (MA0139.1 CTCF, MA0154.3 EBF1, MA0147.3 MYC and MA0079.3 SP1 respectively). The reference positions were set to 10, 7, 6 and 6 respectively and the threshold was set to a pvalue of $1^{-6}$ for all TFs excepted for SP1 for which a pvalue of $1^{-7}$ was used. Non-overlapping matches option was enabled. \subsection{Realignment using JASPAR motifs} % peaks and repeat masking The peaks were filtered for repeated elements. The peaks that had at least 30\% overlap with any repeated element were filtered out using "bedtools substract -A -f 0.3 -a -b " where and are the peak and repeat files in bed format, from the bedtools suite \citep{quinlan_bedtools:_2010}. % matrix creation 70'642 DNA sequences of 1'0001bp corresponding to the central position of each peak +/-500bp were extracted and used to create a sequence matrix using "SequenceMatrixCreator --bed --fasta --from -500 --to 500" where is the bed file containing the repeat filtered peaks and is a fasta file containing chromosome 1, 2, ..., 22, X and Y sequences of the hg19 genome assembly. The corresponding open chromatin and nucleosome sequencing read density matrices were created using "CorrelationMatrixCreator --bed --bam --bai --from -500 --to 500 --binSize 1 --method read\_atac" and "CorrelationMatrixCreator --bed --bam --bai --from -500 --to 500 --binSize 1 --method fragment\_center". and are the bam file containing the ATAC-seq reads, as provided by 10xGenomics and the corresponding bam index file. The bin size of 1b ensured that the read density matrix regions exactly correspond the sequence matrix regions. % PWMs -A total of 23 binding models were downloaded from the motif clustering of JASPAR \citep{castro-mondragon_rsat_2017}. Briefly, the motif clustering is made of a forest of trees (each tree is a cluster in which the leaves are the individual TF binding models. Internal nodes binding models are also available. As a matter of fact, they represent a consensus over multiple individual TF binding models. In order to i) have models representing the binding specificity of the TFs of interest and ii) widen the analysis to other TFs if they were sufficiently related to one of the TFs of interest in terms of specificity, I manually selected binding motifs, in the different motif trees, that would fit these requirements. +A total of 23 binding models were downloaded from the motif clustering of JASPAR \citep{castro-mondragon_rsat_2017}. Briefly, the motif clustering is made of a forest of trees (each tree is a cluster in which the leaves are the individual TF binding models). Internal nodes binding models are also available. As a matter of fact, they represent a consensus over multiple individual TF binding models. In order to i) have models representing the binding specificity of the TFs of interest and ii) widen the analysis to other TFs if they were sufficiently related to one of the TFs of interest in terms of specificity, I manually selected binding motifs, in the different motif trees, that would fit these requirements. The downloaded models were : \begin{table}[H] \begin{center} \begin{tabular}{ |l|l|p{90mm}|l| } \hline \multicolumn{4}{|c|}{Binding models downloaded} \\ \hline Cluster ID & Node ID & TFs covered & Name \\ \hline 1 & 74 & ARID3b, LHX3 & LHX3 \\ 2 & 12 & ESRRG, NR4A1, ESRRB, NR2F2 & NR4A1 \\ 3 & 23 & FOSL1::JUNB, FOSL1::JUN, FOS::JUND, \newline FOSL2::JUN, FOS::JUNB, JDP2, NFE2, FOSL1, FOS, JUND, FOSL2, JUNB, JUN::JUNB, FOSL1::JUND, FOS::JUN, FOSL2::JUND, FOSB::JUNB, FOSL2::JUNB, BATF::JUN, JUN & AP1 \\ 3 & 24 & NFE2L2, BACH1::MAFK, MAF::NFE2, BACH2 & NFE2 \\ 4 & 22 & max::myc, MXI1, myc, mycn & myc \\ 4 & 30 & ARNT, AHR::ARNT & AHR \\ 4 & 31 & HIF1A, HES5, HES7 & HIF1A \\ 5 & 20 & CEBPA, CEBPG, CEBPD, CEBPB, CEBPE & CEBP \\ 7 & 13 & SPIC, SPI1 & PU.1 \\ 7 & 17 & ELF5, ELF3, EHF, ELF1, ELF4 & ELF \\ 19 & 2 & NFAT5,NFATC1,NFATC3 & NFAT \\ 20 & 4 & MEF2C,MEF2B,MEF2A,MEF2D & MEF2 \\ 21 & 5 & GATA3, GATA5, GATA4, GATA6, GATA1, GATA2 & GATA \\ 28 & 13 & EGR2, EGR4, EGR1, EGR3 & EGR \\ 28 & 14 & KLF4,KLF1,KLF9 & KLF \\ 31 & 4 & IRF7, IRF9, IRF4, IRF8, IRF5 & IRF4 \\ 31 & 5 & STAT1::STAT2, IRF2 & IRF2 \\ 32 & STAT6 & STAT6 & STAT6 \\ 33 & 1 & SOX3, SOX6 & SOX \\ 38 & 3 & RUNX1, RUNX2, RUNX3 & RUNX \\ 39 & 1 & E2F3, E2F2 & E2F \\ 48 & CTCF & CTCF & CTCF \\ 66 & 1 & FOXH1 & FOXH1 \\ \hline \end{tabular} \captionof{table} { \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. "Name" refers to the label this model is referred to in the text and figures.} \label{atac_seq_motif_table} \end{center} \end{table} All the binding models were downloaded as LFMs, in JASPAR format and were then converted into LPMs. % partitioning The DNA sequence matrix was partitioned into 23 classes using EMSequence with the model initialized using the 23 LPMs from JASPAR using "EMSequence --seq --class 23 --motifs --shift 971 --flip --iter 1 --seed --out " where is a text file containing the sequence matrix created by SequenceMatrixCreator, ,...,LPM23> is a coma separated list of 23 files containing the 23 JASPAR LPMs, a randomly generated seed and the output file containing the probability matrix. This resulted in the creation of 23 31bp sequence models from which the corresponding read models (open chromatin and nucleosome) were computed using "ProbToModel --read --prob " where is the file containing the read density matrix created using CorrelationMatrixCreator and is the file containing the probability matrix returned by EMSequence. These models were then extended of 1000bp leading to the creation of 1031bp models that are displayed in Figures \ref{suppl_atac_seq_23class} and \ref{atac_seq_23class}. The sequence models were extended using "SequenceModelExtender --bed --fasta --prob --from -500 --to 500 --ext 1000" where the values of the --bed, --fasta, --from and --to option were the values used for the creation of the sequence matrix using SequenceMatrixCreator and is the probability matrix file returned by EMSequence. The read models were extended using "ReadModelExtender --bed --bam --bai --prob --from -500 --to 500 --ext 1000 --binSize 1 --method " where the value of the --bed, --bam, --bai, --from, --to, --binSize and --method options were the values used for the creation of the read density matrices (open chromatin and nucleosomes) using CorrelationMatrixCreator and is the probability matrix file returned by EMSequence. \subsection{Per TF sub-classes} -For each of the 23 classes, a sequence matrix, an open chromatin read density matrix and a nucleosome read density matrix were created, correponding to each class signal. The sequence matrix was created using "ClassSequenceDataCreator --bed --fasta --prob --from -500 --to 500 --k --out " where the values of the --bed, --fasta, --from and --to option were the values used for the creation of the sequence matrix using SequenceMatrixCreator, is the probability matrix file returned by EMSequence, the number of the class to extract and the output file. The read density matrices were created using ""ClassReadDataCreator --bed --bam --bai --prob --from -500 --to 500 --binSize 1 --method " where the value of the --bed, --bam, --bai, --from, --to, --binSize and --method options were the values used for the creation of the read density matrices (open chromatin and nucleosomes) using CorrelationMatrixCreator and is the probability matrix file returned by EMSequence. +For each of the 23 TF classes, a sequence matrix, an open chromatin read density matrix and a nucleosome read density matrix were created. + +The sequence matrix was created using "ClassSequenceDataCreator --bed --fasta --prob --from -500 --to 500 --k --out " where the values of the --bed, --fasta, --from and --to option were the values used for the creation of the sequence matrix using SequenceMatrixCreator, is the probability matrix file returned by EMSequence, the number of the class to extract and the output file. + +The read density matrices were created using ""ClassReadDataCreator --bed --bam --bai --prob --from -500 --to 500 --binSize 1 --method " where the value of the --bed, --bam, --bai, --from, --to, --binSize and --method options were the values used for the creation of the read density matrices (open chromatin and nucleosomes) using CorrelationMatrixCreator and is the probability matrix file returned by EMSequence. The rows with no signal (0) in the open chromatin read density matrix were listed using "WhichNullRows --mat " where is the matrix created using ClassReadDataCreator. The partitioning was performed on the open chromatin signal using ChIPPartitiong using "EMRead --read --iter 20 --class --shift 1 --filter --seed --filter --out " where is the file containing the open chromatin read density matrix created using ClassReadDataCreator, is the number of classes from 1 to 10, the file created by WhichNullRows, a randomly generated seed and is the output file containing the probability matrix. No shifting nor flipping was used because the previous step of TF motif instance alignment already solved it. 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321) defining Unicode char U+0142 (decimal 322) defining Unicode char U+0143 (decimal 323) defining Unicode char U+0144 (decimal 324) defining Unicode char U+0145 (decimal 325) defining Unicode char U+0146 (decimal 326) defining Unicode char U+0147 (decimal 327) defining Unicode char U+0148 (decimal 328) defining Unicode char U+014A (decimal 330) defining Unicode char U+014B (decimal 331) defining Unicode char U+014C (decimal 332) defining Unicode char U+014D (decimal 333) defining Unicode char U+014E (decimal 334) defining Unicode char U+014F (decimal 335) defining Unicode char U+0150 (decimal 336) defining Unicode char U+0151 (decimal 337) defining Unicode char U+0152 (decimal 338) defining Unicode char U+0153 (decimal 339) defining Unicode char U+0154 (decimal 340) defining Unicode char U+0155 (decimal 341) defining Unicode char U+0156 (decimal 342) defining Unicode char U+0157 (decimal 343) defining Unicode char U+0158 (decimal 344) defining Unicode char U+0159 (decimal 345) defining Unicode char U+015A (decimal 346) defining Unicode char U+015B (decimal 347) defining Unicode char U+015C (decimal 348) defining Unicode char U+015D (decimal 349) defining Unicode char U+015E (decimal 350) defining Unicode char U+015F (decimal 351) defining Unicode char U+0160 (decimal 352) defining Unicode char U+0161 (decimal 353) defining Unicode char U+0162 (decimal 354) defining Unicode char U+0163 (decimal 355) defining Unicode char U+0164 (decimal 356) defining Unicode char U+0165 (decimal 357) defining Unicode char U+0168 (decimal 360) defining Unicode char U+0169 (decimal 361) defining Unicode char U+016A (decimal 362) defining Unicode char U+016B (decimal 363) defining Unicode char U+016C (decimal 364) defining Unicode char U+016D (decimal 365) defining Unicode char U+016E (decimal 366) defining Unicode char U+016F (decimal 367) defining Unicode char U+0170 (decimal 368) defining Unicode char U+0171 (decimal 369) defining Unicode char U+0172 (decimal 370) defining 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\l__coffin_display_coffin=\box66 \l__coffin_display_coord_coffin=\box67 \l__coffin_display_pole_coffin=\box68 \l__coffin_display_offset_dim=\dimen295 \l__coffin_display_x_dim=\dimen296 \l__coffin_display_y_dim=\dimen297 \l__coffin_bounding_shift_dim=\dimen298 \l__coffin_left_corner_dim=\dimen299 \l__coffin_right_corner_dim=\dimen300 \l__coffin_bottom_corner_dim=\dimen301 \l__coffin_top_corner_dim=\dimen302 \l__coffin_scaled_total_height_dim=\dimen303 \l__coffin_scaled_width_dim=\dimen304 ) (/usr/share/texlive/texmf-dist/tex/latex/l3kernel/l3pdfmode.def File: l3pdfmode.def 2017/03/18 v L3 Experimental driver: PDF mode \l__driver_color_stack_int=\count328 \l__driver_tmp_box=\box69 )) (/usr/share/texlive/texmf-dist/tex/latex/l3packages/xparse/xparse.sty Package: xparse 2018/02/21 L3 Experimental document command parser \l__xparse_current_arg_int=\count329 \g__xparse_grabber_int=\count330 \l__xparse_m_args_int=\count331 \l__xparse_mandatory_args_int=\count332 \l__xparse_v_nesting_int=\count333 ) (/usr/share/texlive/texmf-dist/tex/latex/l3packages/l3keys2e/l3keys2e.sty Package: l3keys2e 2018/02/21 LaTeX2e option processing using LaTeX3 keys ) Package: chemmacros 2017/08/28 v5.8b comprehensive support for typesetting chem istry documents (CN) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \IfChemCompatibilityTF with sig. 'mm+m+m' on line 190. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \IfChemCompatibilityT with sig. 'mm+m' on line 193. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \IfChemCompatibilityF with sig. 'mm+m' on line 196. ................................................. (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros5.sty Package: chemmacros5 2017/08/28 v5.8b comprehensive support for typesetting che mistry documents (CN) \l__chemmacros_tmpa_dim=\dimen305 \l__chemmacros_tmpb_dim=\dimen306 \l__chemmacros_tmpc_dim=\dimen307 \l__chemmacros_tmpa_int=\count334 \l__chemmacros_tmpb_int=\count335 \l__chemmacros_tmpc_int=\count336 \l__chemmacros_tmpa_box=\box70 \l__chemmacros_tmpb_box=\box71 \l__chemmacros_tmpc_box=\box72 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemModule with sig. 'smmO{5.0}' on line 258. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \usechemmodule with sig. 'm' on line 262. ................................................. \g__file_internal_ior=\read2 (chemmacros) Loading module `base'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.base.code .tex File: chemmacros.module.base.code.tex 2017/08/28 v5.8b chemmacros module `base' 2017/08/28 basic chemmacros module (/usr/share/texlive/texmf-dist/tex/latex/etoolbox/etoolbox.sty Package: etoolbox 2018/02/11 v2.5e e-TeX tools for LaTeX (JAW) \etb@tempcnta=\count337 ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemDeprecated with sig. 'mm' on line 53. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemMacroset with sig. 'smmm' on line 151. ................................................. (/usr/share/texlive/texmf-dist/tex/latex/koma-script/scrlfile.sty Package: scrlfile 2017/09/07 v3.24 KOMA-Script package (loading files) ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemCleverefSupport with sig. 'mmomo' on line 356. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemFancyrefSupport with sig. 'mmo' on line 356. ................................................. (/usr/share/texlive/texmf-dist/tex/latex/tools/bm.sty Package: bm 2017/01/16 v1.2c Bold Symbol Support (DPC/FMi) \symboldoperators=\mathgroup8 \symboldletters=\mathgroup9 \symboldotherletters=\mathgroup10 LaTeX Font Info: Redeclaring math alphabet \mathbf on input line 141. LaTeX Info: Redefining \bm on input line 207. ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chemsetup with sig. 'om' on line 428. ................................................. (chemmacros) Loading module `errorcheck'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.errorchec k.code.tex File: chemmacros.module.errorcheck.code.tex 2017/08/28 v5.8b chemmacros module `errorcheck' 2016/10/05 error checking for unloaded modules )) (chemmacros) Loading module `lang'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.lang.code .tex File: chemmacros.module.lang.code.tex 2017/08/28 v5.8b chemmacros module `lang' 2016/05/31 language settings for chemmacros (/usr/share/texlive/texmf-dist/tex/latex/translations/translations.sty Package: translations 2017/08/31 v1.7a internationalization of LaTeX2e packages (CN) ) ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ChemTranslate with sig. 'm' on line 68. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemTranslations with sig. 'mm' on line 140. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemTranslation with sig. 'mmm' on line 144. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ForAllChemTranslationsDo with sig. '+m' on line 162. ................................................. ) (chemmacros) Loading module `greek'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.greek.cod e.tex File: chemmacros.module.greek.code.tex 2017/08/28 v5.8b chemmacros module `gree k' 2015/06/09 upright greek symbols (/usr/share/texlive/texmf-dist/tex/latex/chemgreek/chemgreek.sty Package: chemgreek 2016/12/20 v1.1 interfaceforuprightgreeklettersforuseinchemi stry (CN) \l__chemgreek_tmpa_int=\count338 \g__chemgreek_tmpa_int=\count339 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \newchemgreekmapping with sig. 'O{}mm' on line 336. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \renewchemgreekmapping with sig. 'O{}mm' on line 339. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \declarechemgreekmapping with sig. 'O{}mm' on line 342. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \newchemgreekmappingalias with sig. 'mm' on line 347. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \renewchemgreekmappingalias with sig. 'mm' on line 350. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \declarechemgreekmappingalias with sig. 'mm' on line 353. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \changechemgreeksymbol with sig. 'mmmm' on line 383. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \chemgreekmappingsymbol with sig. 'mm' on line 477. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \activatechemgreekmapping with sig. 'sm' on line 486. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \selectchemgreekmapping with sig. 'm' on line 491. ................................................. )) (chemmacros) Loading module `chemformula'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.chemformu la.code.tex File: chemmacros.module.chemformula.code.tex 2017/08/28 v5.8b chemmacros module `chemformula' 2016/05/03 integration of chemical formulas (chemmacros) Loading module `charges'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.charges.c ode.tex File: chemmacros.module.charges.code.tex 2017/08/28 v5.8b chemmacros module `ch arges' 2015/07/30 charges ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemCharge with sig. 'mm' on line 122. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemPartialCharge with sig. 'mm' on line 125. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \mch with sig. 'o' on line 146. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pch with sig. 'o' on line 147. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \fmch with sig. 'o' on line 148. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \fpch with sig. 'o' on line 149. ................................................. )) (chemmacros) Loading module `acid-base'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.acid-base .code.tex File: chemmacros.module.acid-base.code.tex 2017/08/28 v5.8b chemmacros module ` acid-base' 2016/05/31 acid/base ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemEqConstant with sig. 'mmm' on line 87. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \p with sig. 'm' on line 119. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pH with sig. '' on line 120. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pOH with sig. '' on line 121. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pKa with sig. 'o' on line 130. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \pKb with sig. 'o' on line 139. ................................................. ) (chemmacros) Loading module `symbols'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.symbols.c ode.tex File: chemmacros.module.symbols.code.tex 2017/08/28 v5.8b chemmacros module `sy mbols' 2015/06/09 symbols ................................................. . LaTeX info: "xparse/define-command" . . Defining command \standardstate with sig. '' on line 67. ................................................. ) (chemmacros) Loading module `particles'... (/usr/share/texlive/texmf-dist/tex/latex/chemmacros/chemmacros.module.particles .code.tex File: chemmacros.module.particles.code.tex 2017/08/28 v5.8b chemmacros module ` particles' 2016/04/02 particles ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemParticle with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \RenewChemParticle with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \DeclareChemParticle with sig. 'mm' on line 45. ................................................. ................................................. . LaTeX info: "xparse/define-command" . . Defining command \ProvideChemParticle with sig. 'mm' on line 45. ................................................. \l__chemmacros_nucleophile_dim=\dimen308 ................................................. . LaTeX info: "xparse/define-command" . . Defining command \NewChemNucleophile with sig. 'mm' on line 111. ................................................. ................................................. . 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'. ................................................. 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{english}{} \contentsline {chapter}{\numberline {1}Introduction}{1}{chapter.1} \contentsline {chapter}{Introduction}{1}{chapter.1} \contentsline {section}{\numberline {1.1}About chromatin}{1}{section.1.1} \contentsline {subsection}{\numberline {1.1.1}The chromatin structure}{2}{subsection.1.1.1} \contentsline {subsection}{\numberline {1.1.2}The chromatin is dynamic}{2}{subsection.1.1.2} \contentsline {subsection}{\numberline {1.1.3}About nucleosome positioning}{4}{subsection.1.1.3} \contentsline {section}{\numberline {1.2}About transcription factors}{7}{section.1.2} \contentsline {subsection}{\numberline {1.2.1}TF co-binding}{7}{subsection.1.2.1} \contentsline {section}{\numberline {1.3}Gene regulation in a nutshell}{9}{section.1.3} \contentsline {subsection}{\numberline {1.3.1}The chromatin barrier}{9}{subsection.1.3.1} \contentsline {subsection}{\numberline {1.3.2}TFs cooperative binding}{9}{subsection.1.3.2} \contentsline {subsection}{\numberline {1.3.3}Pioneer TFs}{10}{subsection.1.3.3} \contentsline {subsection}{\numberline {1.3.4}Regulatory elements}{10}{subsection.1.3.4} \contentsline {subsection}{\numberline {1.3.5}The genome goes 3D}{11}{subsection.1.3.5} \contentsline {section}{\numberline {1.4}Measuring chromatin features}{12}{section.1.4} \contentsline {subsection}{\numberline {1.4.1}Measuring nucleosome occupancy}{12}{subsection.1.4.1} \contentsline {subsection}{\numberline {1.4.2}Digital footprinting}{13}{subsection.1.4.2} \contentsline {subsection}{\numberline {1.4.3}Measuring TF binding in vivo}{15}{subsection.1.4.3} \contentsline {subsection}{\numberline {1.4.4}Measuring TF binding in vitro}{16}{subsection.1.4.4} \contentsline {section}{\numberline {1.5}Modeling sequence specificity}{17}{section.1.5} \contentsline {subsubsection}{The physics approach to PWMs}{17}{section.1.5} \contentsline {subsubsection}{The statistical mechanic approach to PWMs}{18}{equation.1.5.2} \contentsline {subsection}{\numberline {1.5.1}Aligning binding sites}{19}{subsection.1.5.1} \contentsline {subsection}{\numberline {1.5.2}Platitudes}{20}{subsection.1.5.2} \contentsline {subsection}{\numberline {1.5.3}Predicting binding sites}{20}{subsection.1.5.3} \contentsline {section}{\numberline {1.6}Over-represented patterns discovery}{21}{section.1.6} \contentsline {chapter}{\numberline {2}Laboratory resources}{25}{chapter.2} \contentsline {chapter}{Laboratory resources}{25}{chapter.2} \contentsline {section}{\numberline {2.1}Mass Genome Annotation repository}{25}{section.2.1} \contentsline {subsection}{\numberline {2.1.1}MGA content and organization}{26}{subsection.2.1.1} \contentsline {subsection}{\numberline {2.1.2}Conclusions}{27}{subsection.2.1.2} \contentsline {section}{\numberline {2.2}Eukaryotic Promoter Database}{28}{section.2.2} \contentsline {subsection}{\numberline {2.2.1}EPDnew now annotates (some of) your mushrooms and vegetables}{29}{subsection.2.2.1} \contentsline {subsection}{\numberline {2.2.2}Increased mapping precision in human}{30}{subsection.2.2.2} \contentsline {subsection}{\numberline {2.2.3}Integration of EPDnew with other resources}{30}{subsection.2.2.3} \contentsline {subsection}{\numberline {2.2.4}Conclusions}{31}{subsection.2.2.4} \contentsline {subsection}{\numberline {2.2.5}Methods}{31}{subsection.2.2.5} \contentsline {subsubsection}{Motif occurrence profiles}{31}{subsection.2.2.5} \contentsline {chapter}{\numberline {3}ENCODE peaks analysis}{33}{chapter.3} \contentsline {chapter}{ENCODE peaks analysis}{33}{chapter.3} \contentsline {section}{\numberline {3.1}Data}{33}{section.3.1} \contentsline {section}{\numberline {3.2}ChIPPartitioning : an algorithm to identify chromatin architectures}{35}{section.3.2} \contentsline {subsection}{\numberline {3.2.1}Data realignment}{36}{subsection.3.2.1} \contentsline {section}{\numberline {3.3}Nucleosome organization around transcription factor binding sites}{37}{section.3.3} \contentsline {section}{\numberline {3.4}The case of CTCF, RAD21, SMC3, YY1 and ZNF143}{39}{section.3.4} \contentsline {section}{\numberline {3.5}CTCF and JunD interactomes}{43}{section.3.5} \contentsline {section}{\numberline {3.6}EBF1 binds nucleosomes}{47}{section.3.6} \contentsline {section}{\numberline {3.7}Discussion}{50}{section.3.7} \contentsline {section}{\numberline {3.8}Methods}{50}{section.3.8} \contentsline {subsection}{\numberline {3.8.1}Data and data processing}{50}{subsection.3.8.1} \contentsline {subsection}{\numberline {3.8.2}Classification of MNase patterns}{51}{subsection.3.8.2} \contentsline {subsection}{\numberline {3.8.3}Quantifying nucleosome array intensity from classification results}{52}{subsection.3.8.3} \contentsline {subsection}{\numberline {3.8.4}Peak colocalization}{53}{subsection.3.8.4} \contentsline {subsection}{\numberline {3.8.5}NDR detection}{54}{subsection.3.8.5} \contentsline {subsection}{\numberline {3.8.6}CTCF and JunD interactors}{56}{subsection.3.8.6} \contentsline {subsection}{\numberline {3.8.7}EBF1 and nucleosome}{57}{subsection.3.8.7} \contentsline {chapter}{\numberline {4}SPar-K}{59}{chapter.4} \contentsline {section}{\numberline {4.1}Algorithm}{59}{section.4.1} \contentsline {section}{\numberline {4.2}Implementation}{60}{section.4.2} \contentsline {section}{\numberline {4.3}Benchmarking}{61}{section.4.3} \contentsline {subsection}{\numberline {4.3.1}K-means}{61}{subsection.4.3.1} \contentsline {subsection}{\numberline {4.3.2}ChIPPartitioning}{64}{subsection.4.3.2} \contentsline {subsection}{\numberline {4.3.3}Data}{64}{subsection.4.3.3} \contentsline {subsection}{\numberline {4.3.4}Performances}{65}{subsection.4.3.4} \contentsline {section}{\numberline {4.4}Partition of DNase and MNase data}{65}{section.4.4} \contentsline {section}{\numberline {4.5}Conclusions}{65}{section.4.5} \contentsline {chapter}{\numberline {5}SMiLE-seq data analysis}{69}{chapter.5} \contentsline {chapter}{SMiLE-seq data analysis}{69}{chapter.5} \contentsline {section}{\numberline {5.1}Introduction}{69}{section.5.1} \contentsline {section}{\numberline {5.2}Hidden Markov Model Motif discovery}{71}{section.5.2} \contentsline {section}{\numberline {5.3}Binding motif evaluation}{72}{section.5.3} \contentsline {section}{\numberline {5.4}Results}{73}{section.5.4} \contentsline {section}{\numberline {5.5}Conclusions}{75}{section.5.5} \contentsline {chapter}{\numberline {6}PWMScan}{77}{chapter.6} \contentsline {section}{\numberline {6.1}Algorithms}{77}{section.6.1} \contentsline {subsection}{\numberline {6.1.1}Scanner algorithm}{78}{subsection.6.1.1} \contentsline {subsection}{\numberline {6.1.2}Matches enumeration and mapping}{78}{subsection.6.1.2} \contentsline {section}{\numberline {6.2}PMWScan architecture}{79}{section.6.2} \contentsline {section}{\numberline {6.3}Benchmark}{81}{section.6.3} \contentsline {section}{\numberline {6.4}Conclusions}{83}{section.6.4} \contentsline {chapter}{\numberline {7}Chromatin accessibility of monocytes}{85}{chapter.7} \contentsline {section}{\numberline {7.1}Monitoring TF binding}{85}{section.7.1} \contentsline {section}{\numberline {7.2}The advent of single cell DGF}{86}{section.7.2} \contentsline {section}{\numberline {7.3}Open issues}{86}{section.7.3} \contentsline {section}{\numberline {7.4}Data}{86}{section.7.4} \contentsline {section}{\numberline {7.5}Identifying over-represented signals}{87}{section.7.5} \contentsline {subsection}{\numberline {7.5.1}ChIPPartitioning algorithm}{87}{subsection.7.5.1} \contentsline {subsection}{\numberline {7.5.2}EMSequence algorithm}{87}{subsection.7.5.2} \contentsline {subsubsection}{without shift and flip}{89}{figure.caption.35} \contentsline {subsubsection}{with shift and flip}{89}{equation.7.5.2} \contentsline {subsection}{\numberline {7.5.3}EMJoint algorithm}{91}{subsection.7.5.3} \contentsline {subsection}{\numberline {7.5.4}Data realignment}{92}{subsection.7.5.4} \contentsline {subsection}{\numberline {7.5.5}Soft aggregation plots}{92}{subsection.7.5.5} \contentsline {section}{\numberline {7.6}Data processing}{93}{section.7.6} \contentsline {section}{\numberline {7.7}Results}{93}{section.7.7} \contentsline {subsection}{\numberline {7.7.1}Aligning the binding sites}{93}{subsection.7.7.1} \contentsline {subsection}{\numberline {7.7.2}Exploring individual TF classes}{95}{subsection.7.7.2} \contentsline {section}{\numberline {7.8}Discussions}{97}{section.7.8} \contentsline {section}{\numberline {7.9}Perspectives}{97}{section.7.9} \contentsline {section}{\numberline {7.10}Methods}{98}{section.7.10} \contentsline {subsection}{\numberline {7.10.1}Code availability}{98}{subsection.7.10.1} \contentsline {subsection}{\numberline {7.10.2}Data sources}{99}{subsection.7.10.2} \contentsline {subsection}{\numberline {7.10.3}Data post-processing}{99}{subsection.7.10.3} -\contentsline {subsection}{\numberline {7.10.4}Model extension}{99}{subsection.7.10.4} +\contentsline {subsection}{\numberline {7.10.4}Model extension}{100}{subsection.7.10.4} \contentsline {subsection}{\numberline {7.10.5}Extracting data assigned to a class}{100}{subsection.7.10.5} \contentsline {subsection}{\numberline {7.10.6}Programs}{103}{subsection.7.10.6} \contentsline {subsection}{\numberline {7.10.7}Fragment classes}{104}{subsection.7.10.7} \contentsline {subsection}{\numberline {7.10.8}Simulated sequences}{105}{subsection.7.10.8} \contentsline {subsection}{\numberline {7.10.9}Binding site prediction}{105}{subsection.7.10.9} -\contentsline {subsection}{\numberline {7.10.10}Realignment using JASPAR motifs}{105}{subsection.7.10.10} +\contentsline {subsection}{\numberline {7.10.10}Realignment using JASPAR motifs}{106}{subsection.7.10.10} \contentsline {subsection}{\numberline {7.10.11}Per TF sub-classes}{108}{subsection.7.10.11} \contentsline {chapter}{\numberline {8}Discussion}{111}{chapter.8} \contentsline {chapter}{Discussions}{111}{chapter.8} \vspace {\normalbaselineskip } \contentsline {chapter}{\numberline {A}Supplementary material}{115}{appendix.A} \contentsline {section}{\numberline {A.1}ENCODE peaks analysis supplementary material}{116}{section.A.1} \contentsline {section}{\numberline {A.2}SPar-K supplementary material}{126}{section.A.2} \contentsline {section}{\numberline {A.3}SMiLE-seq supplementary material}{139}{section.A.3} \contentsline {section}{\numberline {A.4}Chromatin accessibility of monocytes supplementary material}{139}{section.A.4} \contentsline {subsection}{\numberline {A.4.1}Fragment size analysis}{139}{subsection.A.4.1} \contentsline {subsection}{\numberline {A.4.2}Measuring open chromatin and nucleosome occupancy}{140}{subsection.A.4.2} \contentsline {subsection}{\numberline {A.4.3}Evaluation of EMSequence and ChIPPartitioning}{143}{subsection.A.4.3} \contentsline {subsubsection}{EMSequence}{143}{subsection.A.4.3} \contentsline {subsubsection}{ChIPPartitioning}{146}{figure.caption.56} \contentsline {subsection}{\numberline {A.4.4}Other supplementary figures}{149}{subsection.A.4.4} \contentsline {chapter}{Bibliography}{153}{section*.64} \contentsline {chapter}{Bibliography}{165}{appendix*.65} \contentsline {chapter}{Curriculum Vitae}{167}{section*.66} diff --git a/scripts/ch_atac-seq/get_figures.sh b/scripts/ch_atac-seq/get_figures.sh index d3b4aec..ba6cee9 100755 --- a/scripts/ch_atac-seq/get_figures.sh +++ b/scripts/ch_atac-seq/get_figures.sh @@ -1,32 +1,31 @@ targ_dir='/local/groux/scATAC-seq/results' -targ_dir2='/local/groux/scATAC-seq/test2' dest_dir='/local/groux/phd_thesis/images/ch_atac-seq' script_dir='/local/groux/phd_thesis/scripts/ch_atac-seq' mkdir -p $dest_dir # fragment size distribution cp $targ_dir/10xgenomics_PBMC_5k/fragment_lengths.png $dest_dir/fragment_lengths.png # read/fragment/edge/center aggregations around CTCF, SP1, myc, EBF1 motifs Rscript $script_dir/figure_ctcf_sp1_myc_ebf1_footprint.R # chromatin around CTCF sites Rscript $script_dir/figure_ctcf_6classes.R # chromatin around SP1 sites Rscript $script_dir/figure_sp1_6classes.R # AUC and ROC results cp $targ_dir/toy_data.save/simulated_sequences_2class_flip_auc_roc.png $dest_dir/simulated_sequences_2class_flip_auc_roc.png cp $targ_dir/toy_data.save/simulated_sequences_2class_flip_best_motifs.png $dest_dir/simulated_sequences_2class_flip_best_motifs.png # SP1 binding sites Rscripts $script_dir/figure_sp1_motifs.R # realignment cp $targ_dir/10xgenomics_PBMC_5k_peaks_classification_7/peaks_rmsk_sampled_sequences_23class.png $dest_dir/peaks_rmsk_sampled_sequences_23class.png cp $targ_dir/10xgenomics_PBMC_5k_peaks_classification_7/peaks_rmsk_sampled_sequences_23class_2.png $dest_dir/peaks_rmsk_sampled_sequences_23class_2.png -cp $targ_dir2/PU.1/read/data_classPU.1_2class.png $dest_dir/data_classPU.1_2class.png -cp $targ_dir2/jun/read/data_classjun_3class.png $dest_dir/data_classjun_3class.png -cp $targ_dir2/CTCF/read/data_classCTCF_8class.png $dest_dir/data_classCTCF_8class.png +cp $targ_dir/10xgenomics_PBMC_5k_peaks_classification_8/PU.1/read/data_classPU.1_2class.png $dest_dir/data_classPU.1_2class.png +cp $targ_dir/10xgenomics_PBMC_5k_peaks_classification_8/jun/read/data_classjun_3class.png $dest_dir/data_classjun_3class.png +cp $targ_dir/10xgenomics_PBMC_5k_peaks_classification_8/CTCF/read/data_classCTCF_8class.png $dest_dir/data_classCTCF_8class.png