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+
+These last years the cost of sequencing genomes has been greatly
+reduced, and thus more and more genomes are sequenced. Therefore
+automatic annotation tools are required to deal with this continuously
+increasing amount of genomical data. Moreover, a reliable and accurate
+genome annotation process is needed in order to provide strong
+indicators for the study of life\cite{Eisen2007}.
+
+Various annotation tools (\emph{i.e.}, cost-effective sequencing
+methods\cite{Bakke2009}) producing genomic annotations at many levels
+of detail have been designed by different annotation centers. Among
+the major annotation centers we can notice NCBI\cite{Sayers01012011},
+Dogma \cite{RDogma}, cpBase \cite{de2002comparative},
+CpGAVAS \cite{liu2012cpgavas}, and
+CEGMA\cite{parra2007cegma}. Usually, previous studies used one out of
+three methods for finding genes in annoted genomes using data from
+these centers: \textit{alignment-based}, \textit{composition based},
+or a combination of both~\cite{parra2007cegma}. The alignment-based
+method is used when trying to predict a coding gene (\emph{i.e.}.
+genes that produce proteins) by aligning a genomic DNA sequence with a
+cDNA sequence coding an homologous protein \cite{parra2007cegma}.
+This approach is also used in GeneWise\cite{birney2004genewise}. The
+alternative method, the composition-based one (also known
+as \textit{ab initio}) is based on a probabilistic model of gene
+structure to find genes according to the gene value probability
+(GeneID \cite{parra2000geneid}). Such annotated genomic data will be
+used to overcome the limitation of the first method described in the
+previous section. In fact, the second method we propose finds core
+genes from large amount of chloroplast genomes through genomic
+features extraction.
+
+Figure~\ref{Fig1} presents an overview of the entire method pipeline.
+More precisely, the second method consists of three
+stages: \textit{Genome annotation}, \textit{Core extraction},
+and \textit{Features Visualization} which highlights the
+relationships. To understand the whole core extraction process, we
+describe briefly each stage below. More details will be given in the
+coming subsections. The method uses as starting point some sequence
+database chosen among the many international databases storing
+nucleotide sequences, like the GenBank at NBCI \cite{Sayers01012011},
+the \textit{EMBL-Bank} \cite{apweiler1985swiss} in Europe
+or \textit{DDBJ} \cite{sugawara2008ddbj} in Japan. Different
+biological tools can analyze and annotate genomes by interacting with
+these databases to align and extract sequences to predict genes. The
+database in our method must be taken from any confident data source
+that stores annotated and/or unannotated chloroplast genomes. We have
+considered the GenBank-NCBI \cite{Sayers01012011} database as sequence
+database: 99~genomes of chloroplasts were retrieved. These genomes
+lie in the eleven type of chloroplast families and Table \ref{Tab2}
+summarizes their distribution in our dataset.\\
+
+\begin{figure}[h]
+ \centering
+ \includegraphics[width=0.8\textwidth]{generalView}
+\caption{A general overview of the annotation-based approach}\label{Fig1}
+\end{figure}
+
+Annotation, which is the first stage, is an important task for
+extracting gene features. Indeed, to extract good gene feature, a good
+annotation tool is obviously required. To obtain relevant annotated
+genomes, two annotation techniques from NCBI and Dogma are used. The
+extraction of gene feature, the next stage, can be anything like gene
+names, gene sequences, protein sequences, and so on. Our method
+considers gene names, gene counts, and gene sequence for extracting
+core genes and producing chloroplast evolutionary tree. The final
+stage allows to visualize genomes and/or gene evolution in
+chloroplast. Therefore we use representations like tables,
+phylogenetic trees, graphs, etc. to organize and show genomes
+relationships, and thus achieve the goal of representing gene
+evolution. In addition, comparing these representations with ones
+issued from another annotation tool dedicated to large population of
+chloroplast genomes give us biological perspectives to the nature of
+chloroplasts evolution. Notice that a local database linked with each
+pipe stage is used to store all the informations produced during the
+process.
+
+\input{population_Table}
+
+\subsection{Genome annotation techniques}
+
+For the first stage, genome annotation, many techniques have been
+developed to annotate chloroplast genomes. These techniques differ
+from each others in the number and type of predicted genes (for
+example: \textit{Transfer RNA (tRNA)} and \textit{Ribosomal RNA
+(rRNA)} genes). Two annotation techniques from NCBI and Dogma are
+considered to analyze chloroplast genomes.
+
+\subsubsection{Genome annotation from NCBI}
+
+The objective is to generate sets of genes from each genome so that
+genes are organized without any duplication. The input is a list of
+chloroplast genomes annotated from NCBI. More precisely, all genomes
+are stored as \textit{.fasta} files which consists in a collection of
+protein coding genes\cite{parra2007cegma,RDogma} (gene that produce
+proteins) organized in coding sequences. To be able build the set of
+core genes, we need to preprocess these genomes
+using \textit{BioPython} package \cite{chapman2000biopython}. This
+step starts by converting each genome from FASTA file format to
+GenVision \cite{geneVision} format from DNASTAR. Each genome is thus
+converted in a list of genes, with gene names and gene counts. Gene
+name duplications can be accumulated during the treatment of a genome.
+These duplications come from gene fragments (\emph{e.g.} gene
+fragments treated with NCBI) and from chloroplast DNA sequences. To
+ensure that all the duplications are removed, each list of gene is
+translated into a set of genes. Note that NCBI genome annotation
+produces genes except \textit{Ribosomal (rRNA)} genes.
+
+\subsubsection{Genome annotation from Dogma}
+
+Dogma stands for \textit{Dual Organellar GenoMe Annotator}. It is an
+annotation tool developed at University of Texas in 2004 for plant
+chloroplast and animal mitochondrial genomes. This tool has its own
+database for translating a genome in all six reading frames and
+queries the amino acid sequence database using
+BLAST \cite{altschul1990basic} (\emph{i.e.} Blastx) with various
+parameters. Protein coding genes are identified in an input genome
+using sequence similarity of genes in Dogma database. In addition in
+comparison with NCBI annotation tool, Dogma can produce
+both \textit{Transfer RNAs (tRNA)} and \textit{Ribosomal RNAs (rRNA)},
+verify their start and end positions. Another difference is also that
+there is no gene duplication with Dogma after solving gene
+fragmentation. In fact, genome annotation with Dogma can be the key
+difference when extracting core genes.
+
+The Dogma annotation process is divided into two tasks. First, we
+manually annotate chloroplast genomes using Dogma web tool. The output
+of this step is supposed to be a collection of coding genes files for
+each genome, organized in GeneVision file. The second task is to solve
+the gene duplication problem and therefore we have use two
+methods. The first method, based on gene name, translates each genome
+into a set of genes without duplicates. The second method avoid gene
+duplication through a defragment process. In each iteration, this
+process starts by taking a gene from gene list, searches for gene
+duplication, if a duplication is found, it looks on the orientation of
+the fragment sequence. If it is positive it appends directly the
+sequence to gene files. Otherwise reverse complement operations are
+applied on the sequence, which is then also append to gene files.
+Finally, a check for missing start and stop codons is performed. At
+the end of the annotation process, all the genomes are fully
+annotated, their genes are defragmented, and gene counts are
+available.
+
+\subsection{Core genes extraction}
+
+The goal of this stage is to extract maximum core genes from sets of
+genes. To find core genes, the following methodology is applied.
+
+\subsubsection{Preprocessing}
+
+In order to extract core genomes in a suitable manner, the genomic
+data are preprocessed with two methods: on the one hand a method based
+on gene name and count, and on the other hand a method based on a
+sequence quality control test.
+
+In the first method, we extract a list of genes from each chloroplast
+genome. Then we store this list of genes in the database under genome
+nam and genes counts can be extracted by a specific length command.
+The \textit{Intersection Core Matrix}, described in next subsection,
+is then computed to extract the core genes. The problem with this
+method can be stated as follows: how can we ensure that the gene which
+is predicted in core genes is the same gene in leaf genomes? The
+answer to this problem is that if the sequences of any gene in a
+genome annotated from Dogma and NCBI are similar with respect to a
+given threshold, then we do not have any problem with this
+method. When the sequences are not similar we have a problem, because
+we cannot decide which sequence belongs to a gene in core genes.
+
+The second method is based on the underlying idea: we can predict the
+the best annotated genome by merging the annotated genomes from NCBI
+and Dogma according to a quality test on genes names and sequences. To
+obtain all quality genes of each genome, we consider the following
+hypothesis: any gene will appear in the predicted genome if and only
+if the annotated genes in NCBI and Dogma pass a specific threshold
+of \textit{quality control test}. In fact, the Needle-man Wunch
+algorithm is applied to compare both sequences with respect to a
+threshold. If the alignment score is above the threshold, then the
+gene will be retained in the predicted genome, otherwise the gene is
+ignored. Once the prediction of all genomes is done,
+the \textit{Intersection Core Matrix} is computed on these new genomes
+to extract core genes, as explained in Algorithm \ref{Alg3:thirdM}.
+
+\begin{algorithm}[H]
+\caption{Extract new genome based on gene quality test}
+\label{Alg3:thirdM}
+\begin{algorithmic}
+\REQUIRE $Gname \leftarrow \text{Genome Name}, Threshold \leftarrow 65$
+\ENSURE $geneList \leftarrow \text{Quality genes}$
+\STATE $dir(NCBI\_Genes) \leftarrow \text{NCBI genes of Gname}$
+\STATE $dir(Dogma\_Genes) \leftarrow \text{Dogma genes of Gname}$
+\STATE $geneList=\text{empty list}$
+\STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
+\FOR{$\text{gene in common}$}
+ \STATE $gen1 \leftarrow open(NCBI\_Genes(gene)).read()$
+ \STATE $gen2 \leftarrow open(Dogma\_Genes(gene)).read()$
+ \STATE $score \leftarrow geneChk(gen1,gen2)$
+ \IF {$score > Threshold$}
+ \STATE $geneList \leftarrow gene$
+ \ENDIF
+\ENDFOR
+\RETURN $geneList$
+\end{algorithmic}
+\end{algorithm}
+
+\textbf{geneChk} is a subroutine used to find the best similarity score between
+two gene sequences after applying operations like \textit{reverse}, {\it complement},
+and {\it reverse complement}. Algorithm~\ref{Alg3:genechk} gives the outline of
+geneChk subroutine.
+
+\begin{algorithm}[H]
+\caption{Find the Maximum Similarity Score between two sequences}
+\label{Alg3:genechk}
+\begin{algorithmic}
+\REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
+\ENSURE $\text{Maximum similarity score}$
+\STATE $Score1 \leftarrow needle(gen1,gen2)$
+\STATE $Score2 \leftarrow needle(gen1,Reverse(gen2))$
+\STATE $Score3 \leftarrow needle(gen1,Complement(gen2))$
+\STATE $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$
+\RETURN $max(Score1, Score2, Score3, Score4)$
+\end{algorithmic}
+\end{algorithm}
+
+% THIS SUBSECTION MUST BE IMPROVED
+
+\subsubsection{Intersection Core Matrix (\textit{ICM})}
+
+To extract core genes, we iteratively collect the maximum number of
+common genes between genomes and therefore during this stage
+an \textit{Intersection Core Matrix} (ICM) is built. ICM is a two
+dimensional symmetric matrix where each row and each column correspond
+to one genome. Hence, an element of the matrix stores
+the \textit{Intersection Score} (IS): the cardinality of the core
+genes set obtained by intersecting one genome with another
+one. Maximum cardinality results in selecting the two genomes having
+the maximum score. Mathematically speaking, if we have $n$ genomes in
+local database, the ICM is an $n \times n$ matrix whose elements
+satisfy:
+\begin{equation}
+score_{ij}=\vert g_i \cap g_j\vert
+\label{Eq1}
+\end{equation}
+\noindent where $1 \leq i \leq n$, $1 \leq j \leq n$, and $g_i, g_j$ are
+genomes. The generation of a new core gene depends obviously on the
+value of intersection scores $score_{ij}$:
+
+% TO BE CONTINUED
+
+$$
+\text{new Core} =
+\begin{cases}
+\text{Ignored} & \text{if $\textit{score}=0$;} \\
+\text{new Core id} & \text{if $\textit{Score}>0$.}
+\end{cases}
+$$
+
+if $\textit{Score}=0$ then we have \textit{disjoint
+relation} \emph{i.e.}, no common genes between two genomes. In this
+case the system ignores the genome that annul the core gene
+size. Otherwise, The system removes these two genomes from ICM and add
+new core genome with a \textit{coreID} of them to ICM for the
+calculation in next iteration. This process reduces the size of ICM
+and repeats until all genomes are treated \emph{i.e.} ICM has no more
+genomes. We observe that ICM is very large because of the amount of
+data that it stores. This results to be time and memory consuming for
+calculating the intersection scores. To increase the speed of
+calculations, it is sufficient to only calculate the upper triangle
+scores. The time complexity for this process after enhancement is thus
+$O(\frac{n.(n-1)}{2})$. Algorithm \ref{Alg1:ICM} illustrates the
+construction of the ICM matrix and the extraction of the core genes
+where \textit{GenomeList}, represents the database where all genomes
+data are stored. At each iteration, it computes the maximum core genes
+with its two genomes parents.
+
+% ALGORITHM HAS BEEN REWRITTEN
+
+\begin{algorithm}[H]
+\caption{Extract Maximum Intersection Score}
+\label{Alg1:ICM}
+\begin{algorithmic}
+\REQUIRE $L \leftarrow \text{genomes vectors}$
+\ENSURE $B1 \leftarrow Max Core Vector$
+\FOR{$i \leftarrow 0:len(L)-1$}
+ \STATE $score \leftarrow 0$
+ \STATE $core1 \leftarrow set(GenomeList[L[i]])$
+ \STATE $g1 \leftarrow L[i]$
+ \FOR{$j \leftarrow i+1:len(L)$}
+ \STATE $core2 \leftarrow set(GenomeList[L[j]])$
+ \STATE $Core \leftarrow core1 \cap core2$
+ \IF{$len(Core) > score$}
+ \STATE $score \leftarrow len(Core)$
+ \STATE $g2 \leftarrow L[j]$
+ \ENDIF
+ \ENDFOR
+ \STATE $B1[score] \leftarrow (g1,g2)$
+\ENDFOR
+\RETURN $max(B1)$
+\end{algorithmic}
+\end{algorithm}
+
+\subsection{Features visualization}
+
+The goal is to visualize results by building a tree of evolution. All
+core genes generated represent important information in the tree,
+because they provide information about the ancestors of two or more
+genomes. Each node in the tree represents one chloroplast genome or
+one predicted core called \textit{(Genes count:Family name\_Scientific
+names\_Accession number)}, while an edge is labeled with the number of
+lost genes from a leaf genome or an intermediate core gene. Such
+numbers are very interesting because they give an information about
+the evolution: how many genes were lost between two species whether
+they belong to the same familie or not. By the principle of
+classification, a small number of genes lost among species indicates
+that those species are close to each other and belong to same family,
+while a large lost means that we have an evolutionary relationship
+between species from different families. To depict the links between
+species clearly, we built a phylogenetic tree showing the
+relationships based on the distances among genes sequences. Many tools
+are available to obtain a such tree, for example:
+PHYML\cite{guindon2005phyml},
+RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml}, BioNJ, and
+TNT\cite{goloboff2008tnt}}. In this work, we chose to use
+RAxML\cite{stamatakis2008raxml,stamatakis2005raxml} because it is
+fast, accurate, and can build large trees when dealing with a large
+number of genomic sequences.
+
+The procedure used to built a phylogenetic tree is as follows:
+\begin{enumerate}
+\item For each gene in a core gene, extract its sequence and store it in the database.
+\item Use multiple alignment tools such as (****to be write after see christophe****)
+to align these sequences with each others.
+\item Submit the resulting aligned sequences to RAxML program to compute the distances and finally draw the phylogenetic tree.
+\end{enumerate}
+
+\begin{figure}[H]
+ \centering \includegraphics[width=0.8\textwidth]{Whole_system}
+ \caption{Overview of the pipeline}\label{wholesystem}
+\end{figure}
+
+\section{Implementation}
+We implemented four algorithms to extract maximum core genes from large amount of chloroplast genomes. Two algorithms used to extract core genes based on NCBI annotation, and the others based on dogma annotation tool. Evolutionary tree generated as a result from each method implementation. In this section, we will present the four methods, and how they can extract maximum core genes?, and how the developed code will generate the evolutionary tree.
+
+\subsection{Extract Core Genes based on Gene Contents}
+
+\subsubsection{Core Genes based on NCBI Annotation}
+The first idea to construct the core genome is based on the extraction of Genes names (as gene presence or absence). For instant, in this stage neither sequence comparison nor new annotation were made, we just want to extract all genes with counts stored in each chloroplast genome, then find the intersection core genes based on gene names. \\
+The pipeline of extracting core genes can summarize in the following steps according to pre-processing method used:\\
+
+\begin{enumerate}
+\item We downloads already annotated chloroplast genomes in the form of fasta coding genes (\emph{i.e.} \textit{exons}).
+\item Extract genes names and apply to solve gene duplication using first method.
+\item Convert fasta file format to geneVision file format to generate ICM.
+\item Calculate ICM matrix to find maximum core \textit{Score}. New core genes for two genomes will generate and a specific \textit{CoreId} will assign to it. This process continue until no elements remain in the matrix.
+\item Evolutionary tree will take place by using all data generated from step 1 and 4. The tree will also display the amount of genes lost from each intersection iteration. A specific excel file will be generated that store all the data in local database.
+\end{enumerate}
+
+There main drawback with this method is genes orthography (e.g two different genes sequences with same gene name). In this case, Gene lost is considered by solving gene duplication based on first method to solve gene duplication.
+
+\subsubsection{Core Genes based on Dogma Annotation}
+The main goal is to get as much as possible the core genes of maximum coding genes names. According to NCBI annotation problem based on \cite{Bakke2009}, annotation method like dogma can give us more reliable coding genes than NCBI. This is because NCBI annotation can carry some annotation and gene identification errors. The general overview of whole process of extraction illustrated in figure \ref{wholesystem}.
+
+extracting core genes based on genes names and counts summarized in the following steps:\\
+\begin{enumerate}
+\item We apply the genome annotation manually using Dogma annotation tool.
+\item Analysing genomes to store lists of code genes names (\textit{i.e. exons}). solve gene fragments is done by using first method in solve gene fragments. The output from annotation process with dogma is genomes files in GenVision file format. Sets of genes were stored in the database.
+\item Generate ICM matrix to calculate maximum core genes.
+\item Draw the evolutionary tree by extracted all genes sequences from each core. Then applying multiple alignment process on the sequences to calculate the distance among cores to draw a phylogenetic tree.
+
+\end{enumerate}
+
+
+The main drawback from the method of extracting core genes based on gene names and counts is that we can not depending only on genes names because of three causes: first, the genome may have not totally named (This can be found in early versions of NCBI genomes), so we will have some lost sequences. Second, we may have two genes sharing the same name, while their sequences are different. Third, we need to annotate all the genomes.
+
+\subsection{Extract Core Genes based on Gene Quality Control}
+The main idea from this method is to focus on genes quality to predict maximum core genes. By comparing only genes names from one annotation tool is not enough. The question here, does the predicted gene from NCBI is the same gene predicted by Dogma based on gene name and gene sequence?. If yes, then we can predict new quality genomes based on quality control test with a specific threshold. Predicted Genomes comes from merging two annotation techniques. While if no, we can not depending neither on NCBI nor Dogma because of annotation error. Core genes can by predicted by using one of the
+
+\subsubsection{Core genes based on NCBI and Dogma Annotation}
+This method summarized in the following steps:\\
+
+\begin{enumerate}
+\item Retrieve the annotation of all genomes from NCBI and Dogma: in this step, we apply the annotation of all chloroplast genomes in the database using NCBI annotation and Dogma annotation tool.
+\item Convert NCBI genomes to GeneVision file format, then apply the second method of gene defragmentation methods for NCBI and dogma genomes.
+\item Predict quality genomes: the process is to pick a genome annotation from two sources, extracting all common genes based on genes names, then applying Needle-man wunch algorithm to align the two sequences based on a threshold equal to 65\%. If the alignment score pass the threshold, then this gene will removed from the competition and store it in quality genome by saving its name with the largest gene sequence with respect to start and end codons. All quality genomes will store in the form of GenVision file format.
+\item Extract Core genes: from the above two steps, we will have new genomes with quality genes, ofcourse, we have some genes lost here, because dogma produced tRNA and rRNA genes while NCBI did not generate rRNA genes and vise-versa. Build ICM to extract core genes will be sufficient because we already check genes sequences.
+\item Display tree: An evolution tree then will be display based on the intersections of quality genomes.
+\end{enumerate}
