\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
+core genes generated represent an important information in the tree,
+because they provide ancestor information 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
+one predicted core and labelled as \textit{(Genes count:Family name\_Scientific
+names\_Accession number)}. While an edge is labelled 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
\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 we use an outer-group genome from cyanobacteria to calculate distances.
\item Submit the resulting aligned sequences to RAxML program to compute the distances and finally draw the phylogenetic tree.
\end{enumerate}
\end{figure}
\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.
+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.
\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}
\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.
