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-The field of Genome annotation pay a lot of attentions where the ability to collect and analysis genomical data can provide strong indicator for the study of life\cite{Eisen2007}. A lot of genome annotation centres present various types of annotation tools (i.e cost-effective sequencing methods\cite{Bakke2009}) on different annotation levels. Methods of gene finding in annotated genome can be categorized as: Alignment-based, composition based or combination of both\cite{parra2007cegma}. The Alignment-based method is used when we try to predict a coding gene (i.e. Genes that produce proteins) by aligning DNA sequence of gene to the protein of cDNA sequence of homolog\cite{parra2007cegma}, this approache used in GeneWise\cite{birney2004genewise} with known splicing signals. Composition-based mothod (known as \textit{ab initio} is based on a probabilistic model of gene structure to find genes and/or new genes accoding to the probility gene value, this method like GeneID\cite{parra2000geneid}. In this section, we will consider a new method of finding core genes from large amount of chloroplast genomes, as a solution of the previous method where stated in section two. This method is based on extracting gene features. The question now is how can we have good annotation genome? To answer this question, we need to focusing on studying the annotation's accuracy (systematically\cite{Bakke2009}) of the genome. The general overview of the system is illustrated in Figure 1.\\

 
 

-\begin{figure}[H]
-\caption{A general overview of the system}
-  \centering
-    \includegraphics[width=0.5\textwidth]{generalView}
-\end{figure}
+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}.

 
 

-In Figure 1, we illustrate the general overview of the system. In this system, there are three main stages: \textit{Database, Gene extraction ,} and \textit{relationships}. There are many international nucleotide sequence databases like (GenBank/NCBI in USA at (http://www.ncbi.nlm.nih.gov/genbank/),\\ EMBL-Bank/ENA/EBI in Europe at (http://www.ebi.ac.uk/ena/), and DDBJ in Japon at (http://www.ddbj.nig.ac.jp/)). In our work, the database must be any confident data source that store annotated or unannotated chloroplast genomes. We will consider GenBank/NCBI database as our nucleotide sequences database. Extract Gene Features, we refer to our main process of extracting needed information to find core genome from well large annotation genomes. Thanks to good annotation tool that lead us to extract good gene features. Here, Gene features can be anything like (genes names, gene sequences, protein sequence,...etc). To verify the results from our system, we need to organize and represent our results in the form of (tables, phylogenetic trees, graphs,...,etc), and compare these results with another annotation tool like Dogma\cite{RDogma}. All this work is to see the relationship among our large population of chloroplast genomes and find the core genome for root ancestral node. Furthermore, in this part we can visualize the evolution relationships of different chloroplast organisms.\\
-The output from each stage in our system will be considered to be an input to the second stage and so on. The rest of this section, in section 3.1, we will introduce some annotation problem with NCBI chloroplast genomes and we will discuss our method for how can we extract useful data. Section 3.2 we will present here our system for calculating evolutionary core genome based on another annotation tool than NCBI.
+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.

 
 

-\subsection{Genomes Samples}
-In this research, we retrieved 107 genomes of Chloroplasts from NCBI where 9 genomes considered as not good. These 99 genomes lies in the 11 types of chloroplast families, divided as 11 for Algues Brunes, 3 Algue Rouges, 17 Algues Vertes, 45 Angiospermes, 3 Brypoytes, 2 Dinoflagelles, 2 Euglenes, 5 Filicophytes, 7 Gymnosperms, 2 Lycophytes, and 1 Haptophytes, as show in Table 1.
-\begin{figure}[H]
-\caption{Sample Genomes with its Families}
-  \centering
-    \includegraphics[width=0.7\textwidth]{image1}
-\end{figure}
-\begin{figure}[H]
+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
   \centering

-    \includegraphics[width=0.7\textwidth]{image2}
+    \includegraphics[width=0.75\textwidth]{generalView}
+\caption{A general overview of the annotation-based approach}\label{Fig1}

 \end{figure}
 
 \end{figure}
 

-\subsection{Gene Extraction Techniques from annotated NCBI genomes}
-With NCBI, the idea is to use the existing annotations of NCBI with chloroplast genomes. To extract the core and pan genes: Core extraction techniques with NCBI are based on two techniques: Gene count and Gene contents based on some similarity issues.
+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.

 
 

-\subsubsection{Core genes based on NCBI Gene names and Counts}
-The trivial and simple 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 gene counts stored in each chloroplast genome then find the intersection core genes based on gene names.\\
+\input{population_Table}
+       
+\subsection{Genome annotation techniques}

 
 

-\textbf{Step I: pre-processing}\\
-The objective from this step is to organize, solve genes duplications, and generate sets of genes for each genome. The input to the system is a list genomes from NCBI stored as a \textit{.fasta} file that include a collection of coding genes\cite{parra2007cegma}(genes that produce protein) with its coding sequences.
-As a preparation step to achieve the set of core genes, we need to translate these genomes and extracting all information needed to find the core genes. This is not an easy job. The output from this operation is a lists of genes stored in a local database for genomes, their genes names and genes counts. In this stage, we will accumulate some Gene duplications with each genome treated. In other words, duplication in gene name can comes from genes fragments as long as chloroplast DNA sequences. Identical state, which it is the state that each gene present only one time in a genome (i.e Gene has no copy) without considering the position or gene orientation can be reached by filtering the database from redundant gene name. To do this, we have two solutions: first, we made an orthography checking. Orthography checking is used to merge fragments of a gene to be one gene so that we can solve a duplication. 
-Second, we convert the list of genes names for each genome (i.e. after orthography check) in the database to be a set of genes names. Mathematically speaking, if $g=\left[g_1,g_2,g_3,g_1,g_3,g_4\right]$ is a list of genes names, by using the definition of a set in mathematics, we will have $set(g)=\{g_1,g_2,g_3,g_4\}$, where each gene represented only ones. With NCBI genomes, we do not have a problem of genes fragments because they already treated it, but there are a problem of genes orthography. This can generate the problem of gene lost in our method and effect in turn the core genes. 
-The whole process of extracting core genome based on genes names and counts among genomes is illustrate in Figure 3.
+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.

 
 

-\begin{figure}[H]
-\caption{Extracting Core genome based on Gene Counts}
-  \centering
-    \includegraphics[width=0.7\textwidth]{NCBI_GeneName}
-\end{figure}
+\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 $g1 \leftarrow open(NCBI\_Genes(gene)).read()$   
+       \STATE $g2 \leftarrow open(Dogma\_Genes(gene)).read()$
+       \STATE $score \leftarrow geneChk(g1,g2)$
+       \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 $g1,g2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
+\ENSURE $\text{Maximum similarity score}$
+\STATE $score1 \leftarrow needle(g1,g2)$
+\STATE $score2 \leftarrow needle(g1,Reverse(g2))$
+\STATE $score3 \leftarrow needle(g1,Complement(g2))$
+\STATE $score4 \leftarrow needle(g1,Reverse(Complement(g2)))$
+\RETURN $max(score1,score2,score3,score4)$
+\end{algorithmic}
+\end{algorithm}  
+
+\subsubsection{Intersection Core Matrix (\textit{ICM})}

 
 

-\textbf{Step II: Gene Intersection}\\
-The main objective of this step is try to find best core genes from sets of genes in the database. The idea for finding core genes is to collect in each iteration the maximum number of common genes. To do this, the system build an intersection core matrix(ICM). ICM here is a two dimensional symmetric matrix where each row and column represent the list of genomes in the local database. Each position in ICM stores the \textit{intersection scores}. Intersection Score(IS), is the score by intersect in each iteration two sets of genes for two different genomes in the database. Taking maximum score from each row and then  taking the maximum of them will result to draw the two genomes with their maximum core. Then, the system remove these two genomes from ICM and add the core of them under a specific name to ICM for the calculation in next iteration. The core genes generated with its set of genes will store in a database for reused in the future. this process repeat until all genomes treated. If maximum intersection core(MIC) equal to 0, the system will avoid this intersection operation and ignore the genome that smash the maximum core genes.\\ 
-We observe that ICM will result to be very large because of the huge amount of data that it stores. In addition, this will results to be time and memory consuming for calculating the intersection scores by using just genes names. To increase the speed of calculations, we can calculate the upper triangle scores only and exclude diagonal scores. This will reduce whole processing time and memory to half. The time complexity for this process after  enhancement changed from $O(n^2)$ to $O((n-1)\log{n})$.\\
-The Algorithm of construction the matrix and extracting maximum core genes where illustrated in Algorithm 1. The output from this step is list of core genes with their lengths to be drawn in a tree.  
+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 the  intersection scores  $score_{ij}$. More  precisely, the
+idea is  to consider a  pair of genomes  such that their score  is the
+largest element in ICM. These two genomes are then removed from matrix
+and the  resulting new  core genome is  added for the  next iteration.
+The ICM is then updated to take into account the new core gene: new IS
+values are computed for it. This process is repeated until no new core
+gene can be obtained.
+
+We  can observe  that  the ICM  is very  large  due to  the amount  of
+data. As a consequence, the  computation of the intersection scores is
+both  time and  memory consuming.  However,  since ICM  is a  symetric
+matrix we can reduce the  computation overhead by considering only its
+triangular  upper part.  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
+storing all genomes  data. 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}
 
 \begin{algorithm}[H]
 \caption{Extract Maximum Intersection Score}

+\label{Alg1:ICM}

 \begin{algorithmic} 
 \begin{algorithmic} 

-\REQUIRE $L \leftarrow sets of genomes genes$
-\ENSURE $B1 \leftarrow Max core$ 
+\REQUIRE $L \leftarrow \text{genomes sets}$
+\ENSURE $B1 \leftarrow \text{Max Core set}$ 

 \FOR{$i \leftarrow 0:len(L)-1$}
 \FOR{$i \leftarrow 0:len(L)-1$}

+        \STATE $score \leftarrow 0$

        \STATE $core1 \leftarrow set(GenomeList[L[i]])$
        \STATE $core1 \leftarrow set(GenomeList[L[i]])$

-       \STATE $score1 \leftarrow 0$
-       \STATE $g1,g2 \leftarrow ''$
+       \STATE $g1 \leftarrow L[i]$

        \FOR{$j \leftarrow i+1:len(L)$}
        \FOR{$j \leftarrow i+1:len(L)$}

-               \STATE $core2 \leftarrow set(GenomeList[L[i]])$
-               \IF{$i < j$}
-                       \STATE $Core \leftarrow core1 \cap core2$
-                       \IF{$len(Core) > score1$}
-                               \STATE $g1 \leftarrow L[i]$
-                               \STATE $g2 \leftarrow L[j]$
-                               \STATE $Score \leftarrow len(Core)$
-                       \ELSIF{$len(Core) == 0$}
-                               \STATE $g1 \leftarrow L[i]$
-                               \STATE $g2 \leftarrow L[j]$
-                               \STATE $Score \leftarrow -1$
-                       \ENDIF
-               \ENDIF
+               \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
        \ENDFOR

-       \STATE $B1[score1] \leftarrow (g1,g2)$
+       \STATE $B1[score] \leftarrow (g1,g2)$

 \ENDFOR
 \ENDFOR

-\RETURN $B1$
+\RETURN $max(B1)$

 \end{algorithmic}
 \end{algorithm}
 
 \end{algorithmic}
 \end{algorithm}
 

-In this algorithm, \textit{GenomeList} represents the database.\\
+\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  family  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.

 
 

-\textbf{Step III: Drawing the Tree}\\
-The main objective here is to the results for visualizing a tree of evolution. We use here a directed graph from Dot graph package\cite{gansner2002drawing} from Graphviz library. The system produce this tree automatically by using all information available in a database. Core genes generated with their genes can be very important information, because they represent also the ancestor information for two genomes. In this tree, each node represent genome or core genes as \textit{(Genes count:Family name,Scientific name,Accession number)}, Edges here represent the number of lost genes from genomes-core or core-core intersection. The number of lost genes here can be an important factor for evolution, it represents how much lost of genes for the species in same or different families. By the principle of classification, small genes lost among species can say that these species are closely together and belongs to same family, while big genes lost means that species is far to be in the same family. To see the picture clearly, Phylogenetic tree is an evolutionary tree generated also by the system. Generating this tree is based on the distances among genes sequences. There are many resources to build such tree (for example: PHYML\cite{guindon2005phyml}, RAxML{\cite{stamatakis2008raxml}\cite{stamatakis2005raxml}, BioNJ , and TNT\cite{goloboff2008tnt}}. We consider to use RAxML\cite{stamatakis2008raxml}\cite{stamatakis2005raxml} to generate this tree.   
+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} 

 
 

-The main drawback from this method 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.
+\begin{figure}[H]
+  \centering \includegraphics[width=0.75\textwidth]{Whole_system}
+  \caption{Overview of the pipeline}\label{wholesystem}
+\end{figure}

 
 

-\subsubsection{Extracting Core genome from NCBI gene contents}
+\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}

 
 

-\subsection{Core genes from Dogma Annotation tool}
+\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}

 
 

-\subsubsection{Core genes based on Genes names and count}
+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.

 
 

-\begin{figure}[H]
-\caption{Extracting Core genome based on Gene Name}
-  \centering
-    \includegraphics[width=0.7\textwidth]{Dogma_GeneName}
-\end{figure}
+\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.

 
 

-\subsubsection{Core genome from Dogma gene contents}
+\end{enumerate}

 
 

-\begin{figure}[H]
-\caption{Extracting Core genome based on Gene Name}
-  \centering
-    \includegraphics[width=0.7\textwidth]{Dogma_GeneContent}
-\end{figure}
+
+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}