Modifications made in the two first sections

[chloroplast13.git] / annotated.tex

diff --git a/annotated.tex b/annotated.tex
index 2729939cf32eeb8174cb5ad82402619f3f3e31f3..3a7fbd18453a2ccf45285af1b49d51ee931de624 100644 (file)
--- a/annotated.tex
+++ b/annotated.tex
@@ -1,4 +1,26 @@
-The field of Genome annotation pays a lot of attentions where the ability to collect and analysis genomical data can provide strong indicator for the study of life\cite{Eisen2007}. Four of genome annotation centers, (such as, \textit{NCBI\cite{Sayers01012011}, Dogma \cite{RDogma}, cpBase \cite{de2002comparative}, CpGAVAS \cite{liu2012cpgavas}, and CEGMA\cite{parra2007cegma}}), present various types of annotations tools (i.e cost-effective sequencing methods\cite{Bakke2009}) on different annotation levels. Generally, one of three methods of gene finding in annotated genome can be categorized using these centers: \textit{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 homology\cite{parra2007cegma}. This approache also is used in GeneWise\cite{birney2004genewise}. Composition-based mothod (known as \textit{ab initio}) is based on a probabilistic model of gene structure to find genes and/or new genes according to the probability gene value (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 problem resulting from the method stated in section two. This method is based on extracting gene features. A general overview of the system is illustrated in Figure \ref{Fig1}.\\
+The  field of genome  annotation pays  a lot  of attentions  where the
+ability  to collect  and analysis  genomical data  can  provide strong
+indicators  for  the study  of  life\cite{Eisen2007}.  Four of  genome
+annotation   centers   (such  as,   \textit{NCBI\cite{Sayers01012011},
+Dogma       \cite{RDogma},       cpBase      \cite{de2002comparative},
+CpGAVAS    \cite{liu2012cpgavas},   and   CEGMA\cite{parra2007cegma}})
+present various types  of annotation tools (\emph{i.e.} cost-effective
+sequencing    methods\cite{Bakke2009})    on   different    annotation
+levels. Generally, previous studies used one of three methods for gene
+finding       in        annotated       genome       using       these
+centers: \textit{alignment-based, composition based, or combination of
+both\cite{parra2007cegma}}. The alignment-based method is used when we
+try  to  predict  a  coding  gene  (\emph{i.e.}.  genes  that  produce
+proteins)  by aligning DNA  sequence of  gene to  the protein  of cDNA
+sequence of homology\cite{parra2007cegma}.  This approach also is used
+in GeneWise\cite{birney2004genewise}.  Composition-based method (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}).  In this  section, we  consider  a new
+method of finding core genes from large amount of chloroplast genomes,
+as  a solution  of the  problem resulting  from the  method  stated in
+section  two. This  method is  based  on extracting  gene features.  A
+general overview of the system is illustrated in Figure \ref{Fig1}.\\

 
 \begin{figure}[H]  
   \centering
 
 \begin{figure}[H]  
   \centering


@@ -6,35 +28,35 @@ The field of Genome annotation pays a lot of attentions where the ability to col
 \caption{A general overview of the system}\label{Fig1}
 \end{figure}
 
 \caption{A general overview of the system}\label{Fig1}
 \end{figure}
 

-In Figure 1, we illustrate the general overview of system pipeline: \textit{Database, Genomes annotation, Core extraction,} and \textit{relationships}. We will give a short discussion for each stage of the model in order to understand all core extraction process. This work starts with a gene Bank database; however, many international Banks for nucleotide sequence databases (such as, \textit{GenBank} \cite{Sayers01012011} in USA, \textit{EMBL-Bank} \cite{apweiler1985swiss} in Europe, and \textit{DDBJ} \cite{sugawara2008ddbj} in Japon) where exist to store various genomes and DNA species. Different Biological tools are provided to analyse and annotate genomes by interacting with these databases to  align and extract sequences to predict genes. The database in this model must be taken from any confident data source that store annotated and/or unannotated chloroplast genomes. We will consider GenBank-NCBI \cite{Sayers01012011} database to be our nucleotide sequences database. Annotation (as the second stage) is considered to be the first important task for Extract Gene Features. Good annotation tool lead us to extracts good gene feature. In this paper, two annotation techniques from \textit{NCBI, and Dogma} are used to extract \textit{genes features}. Extracting Gene feature (as a third stage) can be anything like (genes names, gene sequences, protein sequence,...etc). Our methodologies in this paper consider gene names, genes counts, and gene sequences for extracting core genes and producing chloroplast evolutionary tree. \\
-In last stage, features visualization represents methods to visualize genomes and/or gene evolution in chloroplast. By using the forms of (tables, phylogenetic trees, graphs,...,etc) to organize and represent genomes relationships can achieve the goal of gene evolution with what the biological expert needs. In addition, compare these forms with another annotation tool forms for large population of chloroplast genomes give us biological perspective to the nature of chloroplasts evolution. \\
-A Local database attached with each pipe stage is used to store all the informations of extraction process. The output from each stage in our system will be an input to the second stage and so on.
+In Figure 1, we illustrate the general overview of system pipeline: \textit{Database, Genomes annotation, Core extraction,} and \textit{relationships}. We will give a short discussion for each stage of the model in order to understand the whole core extraction process. This work starts with a gene Bank database; however, many international Banks for nucleotide sequence databases (such as, \textit{GenBank} \cite{Sayers01012011} in USA, \textit{EMBL-Bank} \cite{apweiler1985swiss} in Europe, and \textit{DDBJ} \cite{sugawara2008ddbj} in Japon) exist to store various genomes and DNA species. Different biological tools can analyse and annotate genomes by interacting with these databases to  align and extract sequences to predict genes. The database in this model must be taken from any confident data source that stores annotated and/or unannotated chloroplast genomes. We consider GenBank-NCBI \cite{Sayers01012011} database to be our nucleotide sequences database. Annotation (as the second stage) is considered to be the first important task for extract gene features. Good annotation tool leads us to extract good gene feature. In this paper, two annotation techniques from \textit{NCBI, and Dogma} are used to extract \textit{genes features}. Extracting gene feature (as a third stage) can be anything like (genes names, gene sequences, protein sequence,...etc). Our methodology in this paper consider gene names, genes counts, and gene sequence for extracting core genes and producing chloroplast evolutionary tree. \\
+In last stage, features visualization represents methods to visualize genomes and/or gene evolution in chloroplast. We use the forms of tables, phylogenetic trees, graphs,...,etc to organize and represent genomes relationships to achieve the goal of representing gene evolution. In addition, comparing these forms with another annotation tool forms dedicated to large population of chloroplast genomes give us biological perspectives to the nature of chloroplasts evolution. \\
+A local database attached with each pipe stage is used to store all the informations of extraction process. The output from each stage in our system will be an input to the second stage and so on.

 
 \subsection{Genomes Samples}
 
 \subsection{Genomes Samples}

-In this research, we retrieved genomes of Chloroplasts from NCBI. Ninety nine genome of them were considered to work with. These genomes lies in the eleven type of chloroplast families. The distribution of genomes is illustrated in detail in Table \ref{Tab2}.
-
-\input{population_Table}       
-
+In this research, we retrieve genomes of Chloroplasts from NCBI. Ninety nine genome of them are considered to work with. These genomes lies in the eleven type of chloroplast families. The distribution of genomes is illustrated in detail in Table \ref{Tab2}.
+       
+\input{population_Table}

 \subsection{Genome Annotation Techniques}
 \subsection{Genome Annotation Techniques}

-Genome annotation is the second stage in the model pipeline. Many techniques were developed to annotate chloroplast genomes but the problem is that they vary in the number and type of predicting genes (i.e the ability to predict genes and \textit{for example: Transfer RNA (tRNA)} and \textit{Ribosomal RNA (rRNA)} genes). Two annotation techniques from NCBI and Dogma are considered to analyse chloroplast genomes to examine the accuracy of predicted coding genes.   
+Genome annotation is the second stage in the model pipeline. Many techniques were developed to annotate chloroplast genomes but the problem is that they vary in the number and type of predicted genes (\emph{i.e.} the ability to predict genes and \textit{for example: Transfer RNA (tRNA)} and \textit{Ribosomal RNA (rRNA)} genes). Two annotation techniques from NCBI and Dogma are considered to analyse chloroplast genomes to examine the accuracy of predicted coding genes.   

 
 

-\subsubsection{genome annotation from NCBI} 
-The objective from this step is to organize genes, solve gene duplications, and generate sets of genes from each genome. The input to the system is our list of chloroplast genomes, annotated from NCBI. All genomes stored as \textit{.fasta} files which have a collection of protein coding genes\cite{parra2007cegma,RDogma}(gene that produce proteins) with its coding sequences.
-As a preprocessing step to achieve the set of core genes, we need to analyse these genomes (using \textit{BioPython} package\cite{chapman2000biopython}), to extracting all information needed to find the core genes. The process starts by converting each genome from fasta format to GenVision\cite{geneVision} formats from DNASTAR. The outputs from this operation are lists of genes for each genome, their genes names and gene counts. In this stage, we accumulate some Gene duplications for each treated genome. In other words, duplication in gene name can comes from genes fragments, (e.g. gene fragments treated with NCBI), as long as chloroplast DNA sequences. To ensure that all the duplications are removed, each list of genes is translated into a set of genes. NCBI genome annotation produce genes except \textit{Ribosomal rRNA}.
+\subsubsection{Genome annotation from NCBI} 
+The objective from this step is to organize genes, solve gene duplications, and generate sets of genes from each genome. The input to the system is a list of chloroplast genomes, annotated from NCBI. All genomes stored as \textit{.fasta} files which have a collection of protein coding genes\cite{parra2007cegma,RDogma} (gene that produce proteins) with its coding sequences.
+As a preprocessing step to build the set of core genes, we need to analyse these genomes (using \textit{BioPython} package\cite{chapman2000biopython}). The process starts by converting each genome from fasta format to GenVision\cite{geneVision} format from DNASTAR. The outputs from this operation are lists of genes for each genome, their gene names and gene counts. In this stage, we accumulate some gene duplications for each treated genome. These gene name duplication can come from gene fragments, (e.g. gene fragments treated with NCBI), and from chloroplast DNA sequences. To ensure that all the duplications are removed, each list of genes is translated into a set of genes. NCBI genome annotation produce genes except \textit{Ribosomal rRNA}.

 
 \subsubsection{Genome annotation from Dogma}
 Dogma is an annotation tool developed in the university of Texas in 2004. Dogma is an abbreviation of (\textit{Dual Organellar GenoMe Annotator}) for plant chloroplast and animal mitochondrial genomes.
 
 \subsubsection{Genome annotation from Dogma}
 Dogma is an annotation tool developed in the university of Texas in 2004. Dogma is an abbreviation of (\textit{Dual Organellar GenoMe Annotator}) for plant chloroplast and animal mitochondrial genomes.

-It has its own database for translating the genome in all six reading frames and query the amino acid sequence database using Blast\cite{altschul1990basic}(i.e Blastx) with various parameters. Further more, identify protein coding genes in the input genome based on sequence similarity of genes in Dogma database. In addition, it can produce the \textit{Transfer RNAs (tRNA)}, and the \textit{Ribosomal RNAs (rRNA)} and verifies their start and end positions rather than NCBI annotation tool. There are no gene duplication with dogma after solving gene fragmentation. \\
-Genome Annotation with dogma can be the key difference of extracting core genes. The step of annotation divided into two tasks: First, It starts to annotate complete chloroplast genome (i.e \textit{Unannotate genome from NCBI} by using Dogma web tool. This process was done manually. The output from dogma is considered to be collection of coding genes file for each genome in the form of GeneVision file format. 
-Where the second task is to solve gene fragments. Two methods used to solve genes duplication for extract core genes. First, for the method based on gene name, all the duplications are removed, where each list of genes is translated into a set of genes. Second, for the method of gene quality test, defragment process starts immediately to solve fragments of coding genes for each genome to avoid gene duplication. In each iteration, this process starts by taking one gene from gene list, search for gene duplication, if exists, look on the orientation of the fragment sequence: if it is positive, then appending fragment sequence to gene file. Otherwise, the process applies reverse complement operations on gene sequence and append it to gene file. Additional process applied to check start and stop codon and try to find appropriate start and end codon in case of missing. All genomes after this stage are fully annotated, their genes were de-fragmented, genes lists and counts were identified.\\
+It has its own database for translating the genome in all six reading frames and it queries the amino acid sequence database using Blast\cite{altschul1990basic}(\emph{i.e.} Blastx) with various parameters. Furthermore, identify protein coding genes in the input genome based on sequence similarity of genes in Dogma database. In addition, it can produce the \textit{Transfer RNAs (tRNA)}, and the \textit{Ribosomal RNAs (rRNA)} and verifies their start and end positions rather than NCBI annotation tool. There is no gene duplication with dogma after solving gene fragmentation. \\
+Genome annotation with dogma can be the key difference of extracting core genes. The step of annotation is divided into two tasks: first, It starts to annotate complete chloroplast genomes (\emph{i.e.} \textit{Unannotate genome from NCBI} by using Dogma web tool. This process is done manually. The output from dogma is considered to be a collection of coding genes files for each genome in the form of GeneVision file format. 
+The second task is to solve gene fragments. Two methods are used to solve gene duplication. First, for the method based on gene name, all the duplications are removed, where each list of genes is translated into a set of genes. Second, for the method of gene quality test, a defragment process used to avoid gene duplication. \\
+In each iteration, this process starts by taking one gene from gene list, searches for gene duplication, if exists, it looks on the orientation of the fragment sequence: if it is positive, then it appends fragment sequence to a gene files. Otherwise, the process applies reverse complement operations on gene sequences and appends it to gene files. An additional process is then applied to check start and stop codons in case of missing. All genomes after this stage are fully annotated, their genes are de-fragmented, and counts are identified.\\

 
 \subsection{Core Genes Extraction}
 The goal of this step is to extract maximum core genes from sets of genes. The methodology of finding core genes is as follow: \\
 
 \subsubsection{Pre-Processing}
 
 \subsection{Core Genes Extraction}
 The goal of this step is to extract maximum core genes from sets of genes. The methodology of finding core genes is as follow: \\
 
 \subsubsection{Pre-Processing}

-We apply two pre-processing methods for organize and prepare genomes data, one method based on gene name and count, and the second method is based on sequence quality control test.\\
-In the first method, preparing chloroplasts genomes to extract core genes based on gene name and count starts after annotation process because genomes vary in genes counts and types according to the annotation used method. Then we store each genome in the database under genome name with the set of genes names. Genes counts can extracted simply by a specific length command. \textit{Intersection core matrix} will apply then to extract the core genes. The problem with this method is how we can quarantine that the gene predicted in core genes is the same gene in leaf genomes?. To answer this question, if the sequence of any gene in a genome annotated from dogma and NCBI are similar with respect to a threshold, we do not have any problem with this method. Otherwise, we have a problem, because we can not decide which sequence goes to a gene in core genes.
-The second pre-processing method state: we can predict the best annotated genome by merge the annotated genomes from NCBI and dogma based on the quality of genes names and sequences test. To generate all quality genes of each genome. the hypothesis state: Any gene will be in predicted genome if and only if the annotated genes between NCBI and Dogma pass a specific threshold of\textit{quality control test}. To accept the quality test, we applied Needle-man Wunch algorithm to compare two gene sequences with respect to pass a threshold. If the alignment score pass this threshold, then the gene will be in the predicted genome. Otherwise, the gene will be ignored. After predicting all genomes, \textit{Intersection core matrix} will apply on these new genomes to extract core genes. As shown in Algorithm \ref{Alg3:thirdM}.         
+We apply two pre-processing methods to organize and prepare genomes data: the first method based on gene name and count, and the second one is based on 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 name. Genes counts can be extracted by a specific length command. \textit{Intersection Core Matrix}  then applied to extract the core genes. The problem with this method is how can we ensure that the gene which is predicted in core genes is the same gene in leaf genomes? The answer of this question is as follows: if the sequence of any gene in a genome annotated from dogma and NCBI are similar with respect to a threshold, we do not have any problem with this method. Otherwise, we have a problem, because we can not decide which sequence goes to a gene in core genes.
+The second pre-processing method states: we can predict the best annotated genome by merging the annotated genomes from NCBI and dogma if we follow the quality of genes names and sequences test. To generate all quality genes of each genome, the hypothesis state: any gene will be in predicted genome if and only if the annotated genes between NCBI and Dogma pass a specific threshold of \textit{quality control test}. To accept the quality test, we applied Needle-man Wunch algorithm to compare two gene sequences with respect to a threshold. If the alignment score passes the threshold, then the gene will be in the predicted genome. Otherwise, the gene is ignored. After predicting all genomes, \textit{Intersection Core Matrix} is applied on these new genomes to extract core genes, as shown in Algorithm \ref{Alg3:thirdM}.         

 
 \begin{algorithm}[H]
 \caption{Extract new genome based on Gene Quality test}
 
 \begin{algorithm}[H]
 \caption{Extract new genome based on Gene Quality test}


@@ -70,35 +92,28 @@ The second pre-processing method state: we can predict the best annotated genome
 \STATE $Score2 \leftarrow needle(gen1,Reverse(gen2))$
 \STATE $Score3 \leftarrow needle(gen1,Complement(gen2))$
 \STATE $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$
 \STATE $Score2 \leftarrow needle(gen1,Reverse(gen2))$
 \STATE $Score3 \leftarrow needle(gen1,Complement(gen2))$
 \STATE $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$

-\IF {$max(Score1, Score2, Score3, Score4)==Score1$}
-       \RETURN $Score1$
-\ELSIF {$max(Score1, Score2, Score3, Score4)==Score2$}
-       \RETURN $Score2$
-\ELSIF {$max(Score1, Score2, Score3, Score4)==Score3$}
-       \RETURN $Score3$
-\ELSIF {$max(Score1, Score2, Score3, Score4)==Score4$}
-       \RETURN $Score4$
-\ENDIF
+\RETURN $max(Score1, Score2, Score3, Score4)$

 \end{algorithmic}
 \end{algorithm}  
 
 \subsubsection{Intersection Core Matrix (\textit{ICM})}
 
 \end{algorithmic}
 \end{algorithm}  
 
 \subsubsection{Intersection Core Matrix (\textit{ICM})}
 

-The idea behind extracting core genes is to collect iteratively the maximum number of common genes between two genomes. To do so, the system builds an \textit{Intersection core matrix (ICM)}. ICM is a two dimensional symmetric matrix where each row and column represent one genome. Each position in ICM stores the \textit{intersection scores(IS)}. The Intersection Score is the cardinality number of a core genes which comes from intersecting one genome with other ones. Maximum cardinality results to select two genomes with their maximum core. Mathematically speaking, if we have an $n \times n$ matrix where $n=\text{number of genomes in local database}$, then lets consider:\\
+The idea behind extracting core genes is to iteratively collect the maximum number of common genes between two genomes. To do so, the system builds an \textit{Intersection Core Matrix (ICM)}. ICM is a two dimensional symmetric matrix where each row and each column represents one genome. Each position in ICM stores the \textit{Intersection Scores(IS)}. IS is the cardinality number of a core genes which comes from intersecting one genome with other ones. Maximum cardinality results to select two genomes with their maximum core. Mathematically speaking, if we have an $n \times n$ matrix where $n$  
+is the number of genomes in local database, then lets consider:\\

 
 \begin{equation}
 Score=\max_{i<j}\vert x_i \cap x_j\vert
 \label{Eq1}
 
 \begin{equation}
 Score=\max_{i<j}\vert x_i \cap x_j\vert
 \label{Eq1}

-\end{equation}\\
+\end{equation}

 
 

-Where $x_i, x_j$ are elements in the matrix. The generation of a new core genes is depending on the cardinality value of intersection scores, we call it \textit{Score}:
+\noindent where $x_i, x_j$ are elements in the matrix. The generation of a new core genes is depending on the cardinality value of intersection scores, we call it \textit{Score}:

 $$\text{New Core} = \begin{cases} 
 \text{Ignored} & \text{if $\textit{Score}=0$;} \\
 \text{new Core id} & \text{if $\textit{Score}>0$.}
 \end{cases}$$
 
 $$\text{New Core} = \begin{cases} 
 \text{Ignored} & \text{if $\textit{Score}=0$;} \\
 \text{new Core id} & \text{if $\textit{Score}>0$.}
 \end{cases}$$
 

-if $\textit{Score}=0$ then we have \textit{disjoint relation} (i.e no common genes between two genomes). In this case the system ignores the genome that annul the core genes size. Otherwise, The system will removes these two genomes from ICM and add new core genomes with a \textit{coreID} of them to ICM for the calculation in next iteration. This process will reduce the size of ICM and repeat until all genomes are treated (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 $O(\frac{(n-1).n}{2})$. 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.
+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.

 
 \begin{algorithm}[H]
 \caption{Extract Maximum Intersection Score}
 
 \begin{algorithm}[H]
 \caption{Extract Maximum Intersection Score}


@@ -132,7 +147,7 @@ We observe that ICM is very large because of the amount of data that it stores.
 \end{algorithm}
 
 \subsection{Features Visualization}
 \end{algorithm}
 
 \subsection{Features Visualization}

-The goal here is to visualize the results by building a tree of evolution. All Core genes generated with their genes are very important information in the tree, because they can be viewed as an ancestor information for two genomes or more. Further more, each node represents a genome or core as \textit{(Genes count:Family name, Scientific names, Accession number)}, Edges represent numbers of lost genes from genomes-core or core-core relationship. The number of lost genes here can represent 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 number of gene lost among species indicate that those species are close to each other and belong to same family, while big genes lost means that we have an evolutionary relationship between species from different families. 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,stamatakis2005raxml}, BioNJ , and TNT\cite{goloboff2008tnt}}. We consider to use RAxML\cite{stamatakis2008raxml,stamatakis2005raxml} because it is fast and accurate for build large trees for large count of genomes sequences. The procedure of constructing phylogenetic tree stated in the following steps:
+The goal here is to visualize results by building a tree of evolution. All core genes generated with their genes are very important information in the tree, because they can be viewed as an ancestor information for two genomes or more. Further more, each node in the tree represents one chloroplast genome or one predicted core which named under the title of \textit{(Genes count:Family name\_Scientific names\_Accession number)}, Edges represent the number of lost genes from each leaf genome or from an intermediate core genes. The number of lost genes here can represent an important factor for evolution: it represents how much is the lost of genes from the species belongs to same or different families. By the principle of classification, a small number of gene lost among species indicates that those species are close to each other and belong to same family, while big genes lost means that we have an evolutionary relationship between species from different families. 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,stamatakis2005raxml}, BioNJ , and TNT\cite{goloboff2008tnt}}. We consider to use RAxML\cite{stamatakis2008raxml,stamatakis2005raxml} because it is fast and accurate for build large trees for large count of genomes sequences. The procedure of constructing phylogenetic tree stated in the following steps:

 
 \begin{enumerate}
 \item Extract gene sequence for all gene in all core genes, store it in database.
 
 \begin{enumerate}
 \item Extract gene sequence for all gene in all core genes, store it in database.


@@ -156,7 +171,7 @@ The first idea to construct the core genome is based on the extraction of Genes
 The pipeline of extracting core genes can summarize in the following steps according to pre-processing method used:\\
 
 \begin{enumerate}
 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 (i.e \textit{exons}).
+\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 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.


@@ -171,7 +186,7 @@ The main goal is to get as much as possible the core genes of maximum coding gen
 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. 
 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 (i.e \textit{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 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.
 
 \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.