\end{figure}
\section{Implementation}
-We implemented three algorithms to extract maximum core genes from large amount of chloroplast genomes. one algorithm used to extract core genes based on NCBI annotation, and the others based on NCBI and DOGMA annotation tool. Evolutionary tree generated as a result from each method implementation. In this section, we will present the three methods, and how they can extract maximum core genes?, and how the developed code will generate the evolutionary tree.
+We implemented the three algorithms to extract maximum core genes from large amount of chloroplast genomes. Table \ref{Etime}, show the annotation, execution time, and the number of core genes for each method:
+
+\begin{center}
+\begin{tiny}
+\begin{table}[H]
+\caption{Annotation, Execution Time, and core genes for each methodology. Annotation means the type of annotation algorithm used to annotate chloroplast genome, Features means the gene features which it is in two types: either gene name, gene sequence, or using the both. The execution time is represented in minute. The number of core genes in the super core is represented with NCBI and DOGMA. Bad genomes: are the number of genomes that can destroy core genes by low number of gene intersection}\label{Etime}
+\begin{tabular}{ccccccccccc}
+\hline\hline
+ & \multicolumn{2}{c}{Annotation} & \multicolumn{2}{c}{Features} & \multicolumn{2}{c}{Exec Time} & \multicolumn{2}{c}{Core genes} & \multicolumn{2}{c}{Bad genomes} \\
+~ & NCBI & DOGMA & Name & Seq & NCBI & DOGMA & NCBI & DOGMA & NCBI & \multicolumn{1}{c}{DOGMA} \\
+\hline
+Gene prediction & $\surd$ & - & - & $\surd$ & ? & - & ? & - & 0 & -\\
+Gene Features & $\surd$ & $\surd$ & $\surd$ & - & 4.98 & 1.52 & 28 & 10 & 1 & 0\\
+Gene Quality & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{1.29} & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{1}
+
+\end{tabular}
+\end{table}
+\end{tiny}
+\end{center}
+
+
+The second important factor is the amount of memory usage in each methodology. Table \ref{mem} show the amounts of memory consumption by each method.
+
+\begin{center}
+\begin{tiny}
+\begin{table}[H]
+\caption{Annotation, Execution Time, and core genes for each methodology. Annotation means the type of annotation algorithm used to annotate chloroplast genome, Features means the gene features which it is in two types: either gene name, gene sequence, or using the both. The execution time is represented in minute. The number of core genes in the super core is represented with NCBI and DOGMA. Bad genomes: are the number of genomes that can destroy core genes by low number of gene intersection}\label{Etime}
+\begin{tabular}{cccccccc}
+\hline\hline
+& & Load Genomes & T. genevision & Read genevision & ICM & Draw tree & Core Seq. \\
+\hline
+Gene prediction & ~ & ~ & ~ & ~ & ~ & ~ & ~\\
+\multirow{2}{*}{Gene Features} & NCBI & 15.4 & 18.9 & 17.5 & 18 & 18 & 28.1\\
+ & DOGMA& 15.3 & 15.3 & 16.8 & 17.8 & 17.9 & 31.2\\
+Gene Quality & ~ & 15.3 & >134 & 16.1 & 17 & 17.1 & 24.4
+\end{tabular}
+\end{table}
+\end{tiny}
+\end{center}
+
+one algorithm used to extract core genes based on NCBI annotation, and the others based on NCBI and DOGMA annotation tool. Evolutionary tree generated as a result from each method implementation.
\subsection{Extract Core Genes based on Gene Contents}
\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}
-\begin{figure}[H]
- \centering \includegraphics[width=0.75\textwidth]{NCBI_mem}
- \caption{Memory consuming from NCBI annotation}\label{Nmem}
-\end{figure}
-
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}
\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}
-\begin{figure}[H]
- \centering \includegraphics[width=0.75\textwidth]{dogma_mem}
- \caption{Memory consuming from DOGMA annotation}\label{dmem}
-\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}
