-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 annotations tools (i.e cost-effective sequencing methods\cite{Bakke2009}) on different annotation levels. Two method 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 also 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 probability 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 accuracy\cite{Bakke2009}) of the genome. A general overview of the system is illustrated in Figure \ref{Fig1}.\\
+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 annotations tools (i.e cost-effective sequencing methods\cite{Bakke2009}) on different annotation levels. Two method 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 also 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 probability 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 accuracy\cite{Bakke2009} of the genome. A general overview of the system is illustrated in Figure \ref{Fig1}.\\
\begin{figure}[H]
\centering
\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, Gene extraction, } and \textit{relationships}. We will give a short discussion for each stage in the model in order to understand all core extraction process. Good database (as a first stage) will produce good results, however, many international Banks for nucleotide sequence databases like (GenBank in USA, EMBL-Bank in Europe, and DDBJ in Japon) where exists to store various genomes and DNA species. A lot of Biological tool interact with these databases for (Genome Annotation, Gene extraction, alignments, ... , etc). The database in model must be any confident data source that store annotated and/or unannotated chloroplast genomes. We will consider GenBank- NCBI database to be our nucleotide sequences database. Annotation (as the second stage) is consider to be the first important task for Extract Gene Features. Thanks to good annotation tool that lead us to extract good gene features. In this paper, two annotation techniques from \textit{NCBI, and Dogma} will be 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 will consider gene names, gene counts, and gene sequences for extracting core genes and chloroplast evolutionary tree. \\
+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 in the model in order to understand all core extraction process. Good database (as a first stage) will produce good results, however, many international Banks for nucleotide sequence databases like (GenBank in USA, EMBL-Bank in Europe, and DDBJ in Japon) where exists to store various genomes and DNA species. A lot of Biological tool interact with these databases for (Genome Annotation, Gene extraction, alignments, ... , etc). The database in the model must be any confident data source that store annotated and/or unannotated chloroplast genomes. We will consider GenBank- NCBI database to be our nucleotide sequences database. Annotation (as the second stage) is consider to be the first important task for Extract Gene Features. Thanks to good annotation tool that lead us to extract good gene features. In this paper, two annotation techniques from \textit{NCBI, and Dogma} will be 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 will consider gene names, gene counts, and gene sequences for extracting core genes and chloroplast evolutionary tree. \\
In last stage, verifying the work from Biological expert needs to organize and represent genomes relationships and gene evolution in the form of (tables, phylogenetic trees, graphs,...,etc). In addition, comparing these forms with the results from another annotation tool like Dogma\cite{RDogma} for large population of chloroplast genomes give to us biological perspective to the nature of chloroplast evolution. \\
-A Local database attache with each pipe stage to store all information of extraction process. The output from each stage in our system will be an input to the second stage and so on.
+A Local database attached with each pipe stage is to store all information 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}
-In this research, we retrieved 107 genomes of Chloroplasts from NCBI. 99 genomes of then is considered to working with. These genomes lies in the 11 types of chloroplast families, as shown in Table \ref{Tab1}. The list of distribution of genomes is illstrated in detail in Table \ref{Tab2}.
+In this research, we retrieved 107 genomes of Chloroplasts from NCBI. Ninety nine genomes of them were considered to work with. These genomes lies in the 11 type of chloroplast families, as shown in Table \ref{Tab1}. The distribution of genomes is illstrated in detail in Table \ref{Tab2}.
\begin{table}[H]
\caption{distribution on Chloroplast Families}\label{Tab1}
With each annotation model, we provide a quality check class for the flow of chloroplast genomes. This class has a direct access to NCBI taxonomy database based on genome accession number to retreive information for the genome. These information contains \textit{[Scientific name, lineage, Division, taxonomy ID, parentID, and Accession No]}. Examining each genome with this class (i.e based on some parameters), can ignore some genomes from this competition that not match a specific control condition.
\subsubsection{genome annotation from NCBI}
-The objective from this step is to organize, solve genes duplications, and generate sets of genes from each genome. The input to the system is our list of chloroplast genomes, annotated from NCBI\cite{Sayers01012011}. All genomes stored as \textit{.fasta} files include collection of Protein coding genes\cite{parra2007cegma,RDogma}(gene that produce proteins) with its coding sequences.
-As a preparation step to achieve the set of core genes, we need to translate these genomes using \textit{BioPython} package\cite{chapman2000biopython}, and extracting all information needed to find the core genes. A process starts by converting each genome in fasta format to GenVision\cite{geneVision} formats from DNASTAR, and 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 gene 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. We defines \textit{Identical state} to be 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. This state can be reached by filtering the database from redundant gene name. To do this, we have two solutions: first, we made an orthography checking. Orthographe checking is used to merge fragments of a gene to form one gene.
+The objective from this step is to organize genes, solve genes duplications, and generate sets of genes from each genome. The input to the system is our list of chloroplast genomes, annotated from NCBI\cite{Sayers01012011}. All genomes stored as \textit{.fasta} files include collection of Protein coding genes\cite{parra2007cegma,RDogma}(gene that produce proteins) with its coding sequences.
+As a preparation step to achieve the set of core genes, we need to translate these genomes using \textit{BioPython} package\cite{chapman2000biopython}, and extracting all information needed to find the core genes. A process starts by converting each genome in fasta format to GenVision\cite{geneVision} formats from DNASTAR, and this is not an easy job. The output from this operation is a lists of genes stored in a local database for each genome, their genes names and gene 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. We defines \textit{Identical state} to be 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. This state can be reached by filtering the database from redundant gene name. To do this, we have two solutions: first, we made an orthography checking. Orthographe checking is used to merge fragments of a gene to form one gene.
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 $set(G)=\{g_1,g_2,g_3,g_4\}$, and $|G|=4$ where $|G|$ is the cardinality number of the set $G$ which represent the number of genes in the set.\\
The whole process of extracting core genome based on genes names and counts among genomes is illustrate in Figure \ref{NCBI:Annotation}.\\
