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diff --git a/main/ch_pwmscan.tex b/main/ch_pwmscan.tex
index d2760e2..f2c0c0b 100644
--- a/main/ch_pwmscan.tex
+++ b/main/ch_pwmscan.tex
@@ -1,159 +1,159 @@
 \cleardoublepage
 \chapter{PWMScan}
 \label{pwmscan}
 \markboth{PWMScan}{PWMScan}
 \addcontentsline{chapter}{toc}{PWMScan}
 
 
-The problem of pattern-matching using a matrix is considered an important problem and many algorithms and program have been developed. The challenge comes from the fact that this problem should be solved in a time and memory efficient manner in order to allow large eukaryotic genomes to be scanned with complex patterns.
+The problem of pattern-matching using a matrix is considered an important problem and many algorithms and programs have been developed. The challenge comes from the fact that this problem should be solved in a time and memory efficient manner in order to allow large eukaryotic genomes to be scanned with complex patterns.
 
-In this chapter, I describe PWMScan, a software that has been developed to predict TF binding sites given a binding specificity model. PWMScan is a web-server for rapid scanning of large genomes for high-scoring matches to a user-supplied or server-resident PWM. Compared to other web-based PWM scanning tools, PWMScan is unique in that it scans server-resident whole genomes rather than user-uploaded DNA sequences. Other key features are: i) menu-driven access to genomes of >30 model organisms; ii) menu-driven access to >300 public PWM libraries; iii) support of various PWM representations and formats; iv) cut-off values can be specified as match scores or P-values; v) output in BEDdetail format with match scores and P-values; vi) links to UCSC genome browser for visualization of results; and vii) action buttons to transfer match lists to analysis tools.
+In this chapter, I describe PWMScan, a software that has been developed to predict TF binding sites given a genome and a binding specificity model. PWMScan is a web-server for rapid scanning of large genomes for high-scoring matches to a user-supplied or server-resident PWM. Compared to other web-based PWM scanning tools, PWMScan is unique in that it scans server-resident whole genomes rather than user-uploaded DNA sequences. Other key features are: i) menu-driven access to genomes of >30 model organisms, ii) menu-driven access to >300 public PWM libraries, iii) support of various PWM representations and formats, iv) cut-off values can be specified as match scores or P-values, v) output in BEDdetail format with match scores and P-values, vi) links to UCSC genome browser for visualization of results, and vii) action buttons to transfer match lists to analysis tools.
 
 This work has been conducted in close collaboration with Giovana Ambrosini, a senior scientist of the laboratory. Some parts of this chapter have been taken and adapted - with the author permission -  from the original article that presents this work \citep{ambrosini_pwmscan:_2018}. For this project, I contributed to the development of the matrix\_scan program, the development of the necessary Python code for an efficient parallelization of matrix\_scan on the server backend and I set up and executed the entire benchmarking procedure. Furthermore, I produced all the figures presented in this chapter.
 
 
 % article Figure 1
 % \begin{figure}[!htbp]
 % \begin{center}
 % 	\includegraphics[scale=0.4]{images/ch_lab_resources/pwmscan_fig1.png}  
 % 	\captionof{figure}{Screen shot of the PWMScan graphical user interface.}
 % \label{lab_resources_gui}
 % \end{center}
 % \end{figure}
 
 \section{Algorithms}
 
-From an algorithmic perspective, PWMScan is dual. It implements a regular genome scanner and also implements a novel approach that uses short read alignments to find PWM matches. A description of both algorithms follows. 
+From an algorithmic perspective, PWMScan is dual. It implements a regular genome scanner and also implements a novel approach that uses short read alignment to find PWM matches.
 
 \subsection{Scanner algorithm}
-The program matrix\_scan implements a scanner (or window sliding) searching algorithm. It takes as input a PWM of dimensions $L\times4$, a DNA sequence $S$ of length $L' \geq L$ and a threshold score $t$. For each possible offset $i = 1, 2, ..., L'-L+1$, the sub-sequence $S_{i} = S_{i,i+1,...,i+L-1}$ is given a score $score(S_{i})$ using equation \ref{intro_eq_score_pwm}. If $score(S_{i}) \geq t$ then the sub-sequence $S_{i}$ is predicted to be a binding site. This naive approach is $\theta((L'-L+1)*L)$ in time. Even though this is not bad in terms of asymptotics analysis,  many unnecessary computations are performed. Indeed, during the score computations for a sub-sequence $S_{i}$, it is possible to define a point after which the current score has become so bad that it will automatically turn to be $<t$. From that moment, the computations for the current sub-sequence $S_{i}$ can be dropped and the next sub-sequence $S_{i+1}$ can be tested.
+The program matrix\_scan implements a scanner (or sliding window) searching algorithm. It takes as input a DNA sequence of length $L$, a PWM of dimensions $L'\times4$ where $L' \leq L$ and a threshold score $t$. For each possible offset $i = 1, 2, ..., L-L'+1$, the sub-sequence $S_{i} = S_{i,i+1,...,i+L'-1}$ is given a score $score(S_{i})$ using equation \ref{intro_eq_score_pwm}. If $score(S_{i}) \geq t$ then the sub-sequence $S_{i}$ is predicted to be a binding site. This naive approach is $\theta((L-L'+1)*L')$ in time. Even though this is not bad in terms of asymptotics analysis,  many unnecessary computations are performed. Indeed, during the score computations for a sub-sequence $S_{i}$, it is possible to define a point after which the current score has become so bad that it will automatically turn to be $<t$. From that moment, the computations for the current sub-sequence $S_{i}$ can be dropped and the next sub-sequence $S_{i+1}$ can be tested.
 
-This only requires to modify the PWM such that the maximum possible score achieved at each position is 0. To do so, the values on each column should be substracted the maximum for that column. The threshold score $t$ should modified into $t'$ by subtracting the sum of the column maxima. This scheme is called 'lookahead' (LA, \cite{pizzi_fast_2008}). LA can further be improved by redefining the order of the position scoring order. The optimal position scoring order is to start first by the position in the PWM which can achieve the worst score, then by the PWM position achieving the second worst score and so one until the last PWM position that achieves the best possible score. By doing this, the score can drop under $t'$ faster. This scheme is called permutated LA (PLA, \cite{pizzi_fast_2008}). Even though LPA does not modify the algorithm complexity, in average this algorithm is faster than the naive one as it can avoid unnecessary computations.
+This only requires to modify the PWM such that the maximum possible score achieved at each position is 0. To do so, the values on each column should be substracted the maximum for that column. The threshold score $t$ should modified into $t'$ by subtracting the sum of the column maxima. This scheme is called 'lookahead' (LA, \cite{pizzi_fast_2008}). LA can further be improved by redefining the order of the position scoring order. The optimal position scoring order is to start first by the position in the PWM which can achieve the worst score, then by the PWM position achieving the second worst score and so one until the last PWM position that achieves the best possible score. By doing this, the score can drop under $t'$ faster. This scheme is called permutated LA (PLA, \cite{pizzi_fast_2008}). Even though PLA does not modify the algorithm complexity, in average this algorithm is faster than the naive one as it can avoid unnecessary computations.
 
 % bowtie
 \subsection{Matches enumeration and mapping}
-The second approach used by PWMScan is drastically different. It also takes as input a PWM of dimensions $L\times4$, a DNA sequence $S$ of length $L' \geq L$ and a threshold score $t$. Then, all the k-mer of length $L$ achieving a score higher than $t$ are enumerated and mapped against $S$ using a short read mapper - in this case Bowtie \citep{langmead_ultrafast_2009}.
+The second approach used by PWMScan is drastically different. It also takes as input a DNA sequence $S$ of length $L$, a PWM of dimensions $L'\times4$ where $L' \leq L$ and a threshold score $t$. Then, all the k-mer of length $L'$ achieving a score higher than $t$ are enumerated and mapped against $S$ using a short read mapper - in this case Bowtie \citep{langmead_ultrafast_2009}.
 
-Enumerating all possible L-mer using a naive algorithm is $\theta(4^{L})$ in time, which really quickly becomes cumbersome to handle. However, here again, many unnecessary computations can be avoided.
+Enumerating all possible L-mer using a naive algorithm is $\theta(4^{L'})$ in time, which really quickly becomes cumbersome to handle. However, here again, many unnecessary computations can be avoided.
 
-Let us assume a LA scheme as before. When scoring a sub-sequence $S_{i}$ from left to right, the score monotonically decreases. If at some position $j$ the score falls under the threshold score $t'$ then this sub-sequence $S_{i} = S_{i,i+i,...,i+L-1}$ is guaranteed to be rejected. By extension, this means that any sub-sequence that starts with a prefix $S_{i,i+1,...,i+j-1}$ are also guaranteed to be rejected. If the k-mers are stored in a trie, then it would be sufficient to ignore the sub-tree of $S_{i,i+1,...,i+j-1}$.
+Let us assume a LA scheme as before. When scoring a sub-sequence $S_{i}$ from left to right, the score monotonically decreases. If at some position $j$ the score falls under the threshold score $t'$ then the sub-sequence $S_{i} = S_{i,i+1,...,i+L'-1}$ is guaranteed to be rejected. By extension, this means that any sub-sequence that starts with a prefix $S_{i,i+1,...,i+j-1}$ are also guaranteed to be rejected. If the k-mers are stored in a trie, then it would be sufficient to ignore the sub-tree of $S_{i,i+1,...,i+j-1}$.
 
 The program mba (for branch and bound) implements thisk-mer enumeration strategy. The k-mers are enumerated by alphabetical order which is the same as exploring the k-mer trie in breadth-first order. If a sub-sequence $S_{i}$ is rejected by dropping the computations at step $j$, the program jumps to the next sub-sequence $S_{i'}$ that does not start with the same prefix and continues the enumeration here. This is equivalent to ignore the entire prefix sub-tree in the trie.
 
 Eventually, the enumerated k-mers are all the possible different matches that can be achieved with the given PWM and threshold score. Their locations in the genome are identified by a read mapping procedure using Bowtie, with $S$ as the reference genome. 
 
 
 \section{PMWScan architecture}
 
 % flowchart present in supplemental material
 \begin{figure}
 \begin{center}
 	\includegraphics[scale=0.1]{images/ch_lab_resources/pwmscan_flowchart.png}  
-	\captionof{figure}{\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.
+	\captionof{figure}{\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. LPMs or LFMs 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}.}
 \label{lab_resources_pwmscan_pipeline}
 \end{center}
 \end{figure}
 
 PWMScan architecture and design is presented in Figure \ref{lab_resources_pwmscan_pipeline}.
 
 % genomes
 Several genomes are supported by PWMScan and are offered to the user on the web interface. The different specy genome sequences were downloaded from the NCBI in FASTA format and indexed for rapid scanning using Bowtie \citep{langmead_ultrafast_2009}.
 
 % matrix collections
-PWMScan web interface offers several PWMs collections to scan the available genomes. Each collection was downloaded from the MEME Suite website \citep{bailey_meme_2009} and all matrices, of any type, were converted to integer PWMs (see Supplementary Material in \citep{ambrosini_pwmscan:_2018}).
+PWMScan web interface offers several PWM collections to scan the available genomes. Each collection was downloaded from the MEME Suite website \citep{bailey_meme_2009} and all matrices, of any type, were converted to integer PWMs (see Supplementary Material in \citep{ambrosini_pwmscan:_2018}).
 
 % score
-For all matrix formats, the cut-off value can be specified as PWM score, as percentage of the score range (0\% = minimal score and 100\% = maximal score) or as p-value. The p-value of a PWM score x is defined as the probability that a random k-mer sequence of the length of the PWM has a binding score $\geq$ x given the base composition of the genome. In the two latter cases, the corresponding cut-off value is computed.
+For all matrix formats, the cut-off value can be specified as a PWM score, as a percentage of the score range (0\% = minimal score and 100\% = maximal score) or as a p-value. The p-value of a PWM score $X$ is defined as the probability that a random k-mer sequence of the same length as the PWM has a binding score $\geq X$ given the base composition of the genome. In the two latter cases, the corresponding cut-off value is computed.
 
 It should be noted that IUPAC consensus sequences are also accepted instead of a matrix. In that case, they are converted into binary matrices consisting of 0 and 1. However, in this case, the threshold score is specified as the maximal number of mismatches allowed.
 
 Once the appropriate format conversions have been applied to the inputs, the integer PWM and the corresponding cut-off score are given to the search engine. The choice between the scanner approach and the read mapping approach depends on the length of the PWM and the cut-off. As a matter of fact, the read mapping strategy is more efficient for short PWMs and high cut-off values. Indeed, in this case, the k-mers enumeration step is really fast. Empirically, we found that the limit was around $10^{5}$ kmers. Above this limit, the scanner strategy is used. In this case, the individual chromosomes are processed in parallel with each chromosome sequence being distributed to a core by a Python script.
 
 % output
 Eventually, the output is a list of sequence regions that match the PWM with a score higher or equal to the cut-off value. Post-processing of this list involves computation of the corresponding p-values, addition of the matrix name and, optionally, elimination of overlapping matches. The final match list is provided in BEDdetail format. 
 
 On the web interface, PWMScan is interconnected with i) the ChIP-seq \citep{ambrosini_chip-seq_2016} and ii) the SSA \citep{ambrosini_signal_2003} servers which allows to proceed to downstream analyses. Additionally, different action buttons are present on the result page and allows to i) extract the DNA sequences around the matches, ii) send the match coordinates to UCSC genome browser for visualization and iii) liftover the match coordinates to other assemblies of the same or related species.
 
 PWMScan is also available as a standalone software from SourceForge. This includes a master script scheduling all computational steps running during a web job.
 
 
 \section{Benchmark}
 
 % supplemental figure 3 in article
 \begin{figure}
 \begin{center}
 	\includegraphics[scale=0.2]{images/ch_lab_resources/pwmscan_figure_s1.png}  
 	\captionof{figure}{\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}.}
 \label{lab_resources_pwmscan_benchmark}
 \end{center}
 \end{figure}
 
 \begin{table}
 \begin{center}
 \begin{tabular}{| c | c | c | c | c | c | c |}
 \hline 
       Program &        \multicolumn{3}{|c|}{CTCF}           &      \multicolumn{3}{|c|}{STAT1} \\
  \hline
               & \thead{Nb of\\matches} & \thead{Perc.\\overlap} & \thead{Score corr.} & \thead{Nb of\\matches} & \thead{Perc.\\overlap} & \thead{Score\\corr}. \\
  \hline
  matrix\_scan &    70607   &       100     &       1      &   138531  &    100        &       1 \\
  \hline
  Patser       &    70607   &       100     &       1      &   138531  &    100        &       1 \\
  \hline
  PossumSearch &    70607   &       100     &       1      &   138531  &    100        &       1 \\
  \hline
  FIMO         &    70131   &      99.16    &    0.9989    &   134901  &    100        &       1 \\
  \hline
  RSAT         &    70701   &      99.85    &    0.9999    &   134901  &    100        &       1 \\
  \hline
  HOMER        &    70701   &      99.95    &    0.9999    &   134901  &    100        &       1 \\
  \hline
  STORM        &    70704   &      99.83    &    0.9999    &   134901  &    100        &  0.9999 \\
  \hline
 \end{tabular}
-\caption{\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.
+\caption{\textbf{Motif scanning software comparison}. The performances of matrix\_scan were assessed by comparing how many of the regions returned 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 shortest of the two lists always served 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}.}
 \label{lab_resources_pwmscan_benchmark_table}
 \end{center}
 \end{table}
 
 % article Table 1
 % \begin{center}
 % \captionof{table}{Speed is expressed in seconds. The benchmarking tests have been run on a Linux/CentOS7/x86\_64 workstation with 48 CPU-cores and 256 GB of DRAM.
 % The performance measurements for matrix\_scan were achieved using 1 (left value) and 10 (right value) CPU-cores in parallel.}
 % \begin{tabular}{ c c c }
 %  PWM/P-value                   & Bowtie speed & Matrix\_scan speed \\
 %  \hline
 %  STAT1(len = 11 bp)/$10^{-5}$  & 3            & 30/5  \\
 %  STAT1(len = 11 bp)/$10^{-4}$  & 8            & 40/8  \\
 %  STAT1(len = 11 bp)/$10^{-3}$  & 60           & 65/30 \\
 %  CTCF(len = 19 bp)/$10^{-5}$   & 12           & 40/6  \\
 %  CTCF(len = 19 bp)/$10^{-4}$   & 90           & 50/10 \\
 %  CTCF(len = 19 bp)/$10^{-3}$   & 720          & 90/35 \\
 % \label{lab_resources_pwmscan_table}
 % \end{tabular}
 % \end{center}
 
 % The problem of pattern-matching using a matrix is considered an important problem and many algorithms and program have been developed. The challenge comes from the fact that this problem should be solved in a time and memory efficient manner but also because it poses a more fundamental problem : most of the prediction turn to be false positives.
 
 Many algorithms have been developed to find matches in genome using matrices. The most straight forward approach to this problem is the scanner approach. This way of doing is implemented by matrix\_scan, Patser \citep{hertz_identification_1990}, PossuMSearch \citep{beckstette_fast_2006}, RSAT matrix-scan (later on simply referred to as RSAT, \citep{turatsinze_using_2008}), HOMER \citep{heinz_simple_2010} or FIMO \citep{grant_fimo:_2011}. PoSSuMSearch implements both the use of LA scheme and the use of enhanced suffix arrays (ESA) to store the indexed genome and search matches in sub-linear time.
 
 These programs also generally compute a p-value associated with each score. FIMO also performs an expensive false discovery rate (FDR) estimate in order to correct the p-values for multiple testing.  On its side, STORM \citep{schones_statistical_2007} uses a database of regulatory sequences to count the expected number of occurrences of kmers to compute a p-value. STORM creates a so called ($g$,$k$) table in order to store the number of occurrences of all possible kmers of size $k$ with a maximum gap of size $g$ present in a sequence database. Thus, it can handled gaped matches.
 
 Also, for completness, it is worth mentioning that other programs have been released, such as MotifScanner \citep{aerts_toucan:_2003}, MotifViz \citep{fu_motifviz:_2004} or TRED \citep{zhao_tred:_2005}. However they could not be included in the benchmark for diverse reasons. MotifScanner could only be used through a GUI client connected to a (offline?) server, making it unsable for batch analyses. MotifViz relied on deprecated 32bits libraries and TRED input sequence size was limited to 10kb.
 
 To assess how these programs perform compared to each other, I considered the run-time efficiency aspect but also, as important, the match consistency between programs. 
 In order to perform meaningful comparisons, each program options were set such that each program returned a number of matches as close as possible to PWMScan. A match was considered common between two programs if the starting positions reported were equal. FIMO, Patser, RSAT and HOMER positions were modified by '-1' to account for a constitutive shift with PWMScan reported positions. The proportion of overlap was then computed by dividing the number of common match by the length of the longest match list from either programs. Then, 
 a Spearman correlation was computed between the scores of the matches common to both programs to assess the scoring consistency.
 
-The runtimes of PWMScan and of other competitor programs were measured by scanning the human genome (UCSC assembly hg19) with two different PWMs from JASPAR, STAT1 with length 11 bp and CTCF with length 19 bp, and different cut-off values expressed as P-values. Results are shown in Figure \ref{lab_resources_pwmscan_benchmark} and and Table \ref{lab_resources_pwmscan_benchmark_table}. PoSSuMSearch was used with its sliding window strategy. To our surprise, PWMScan was the fastest of the method. On the other hand, STORM was by far the slowest. This may be caused by STORM's ability to handle gaped matches. PWMScan also achieved a high similarity in terms of matches with the other programs both in terms of overlap between matches than in terms of score similarity. The differences observed cannot be explained with certainty. They could be explained, for instance, by differences between integer and double arithmetic or by differences in the handling of corner cases between different programs.
+The runtimes of PWMScan and of other competitor programs were measured by scanning the human genome (UCSC assembly hg19) with two different PWMs from JASPAR, STAT1 with length 11 bp and CTCF with length 19 bp, and different cut-off values expressed as p-values. Results are shown in Figure \ref{lab_resources_pwmscan_benchmark} and and Table \ref{lab_resources_pwmscan_benchmark_table}. PoSSuMSearch was used with its sliding window strategy. To our surprise, PWMScan was the fastest of the method. On the other hand, STORM was by far the slowest. This may be caused by STORM's ability to handle gaped matches. PWMScan also achieved a high similarity in terms of matches with the other programs both in terms of overlap between matches than in terms of score similarity. The differences observed cannot be explained with certainty. They could be explained, for instance, by differences between integer and double arithmetic or by differences in the handling of corner cases between different programs.
 
 \section{Conclusions}
 
 We developed PWMScan, a program that searches matches inside a genome using a PWM. We implemented two alternative search strategies : i) a traditional sliding window approach and ii) an innovative match mapping using Bowtie. Both strategies are competitive in terms of speed and score similarity compared to other existing programs. Furthermore, our window sliding approach turned out to be fastest of all.
 
 PWMScan is meant to support many types of genomic data analysis and designed to be interoperable with other tools from our group and elsewhere. An example of a typical workflow involving ChIP-seq data is presented in Supplementary Material in \citep{ambrosini_pwmscan:_2018}. Additionally, PWMScan is available as a standalone distribution.
 
-In short, PWMScan is an asset for the research community because of its simplicity of use and of its high performances.
\ No newline at end of file
+In short, PWMScan is an asset for the research community because of its simplicity of use and of its high performances.
diff --git a/main/ch_smile-seq.aux b/main/ch_smile-seq.aux
index f128a06..581fae3 100644
--- a/main/ch_smile-seq.aux
+++ b/main/ch_smile-seq.aux
@@ -1,83 +1,85 @@
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diff --git a/main/ch_smile-seq.tex b/main/ch_smile-seq.tex
index 0930b76..7a32746 100644
--- a/main/ch_smile-seq.tex
+++ b/main/ch_smile-seq.tex
@@ -1,138 +1,139 @@
 \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 made 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 from 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}.
 
 \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.
+	\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 double-stranded DNA, 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 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.
+Because TFs can bind as monomers, homodimers, heterodimers and as higher order complexes, it is critical to have suited technologies to interrogate their binding specificity. Nonetheless, because of combinatorials, this undertaking represents 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 the 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 features 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.
 
 \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 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 is based on an initial work performed 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 performance.
 
-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 $\sum_{i=1}^4 m_{i,j} = 1$. 
+This motif discovery method models the DNA sequences using an HMM. The input is composed of $N$ sequences of length $L$ and the motif is represented as a LPM \textbf{M} of dimensions $4 \times L'$ where $L' \leq L$ 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, 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.
+The sequences are 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 the LPM. To this end, we used 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}.
+Additionally, to account for experimental biases, such as unspecific binding, a sequence can bear 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.
 
 
 \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.
+To evaluate and compare different binding models originating 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.
+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-K'+1} \prod_{k=1}^{K} \frac{m_{b,k}}
+	score^{pos}_{i} & =  \sum_{l=1}^{L''-L'+1} \prod_{k=1}^{L'} \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.
+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 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.}
+	\captionof{figure}{\textbf{Predictive power of SMiLE-seq :} \textbf{A} SMiLE-seq detived 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.
+Figures and legends taken and adapted from \citep{isakova_smile-seq_2017}}
 \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 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. 
+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. 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 the 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. 
 
-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.
+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 the 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 the 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).
 
 \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.
+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 the 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
+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.
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diff --git a/my_thesis.log b/my_thesis.log
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 (/usr/share/texlive/texmf-dist/tex/latex/base/ts1enc.dfu
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 ))
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