-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]
- \centering
- \includegraphics[width=0.7\textwidth]{image2}
-\end{figure}
+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}
+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 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.
+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}
+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}.
