\STATE $geneList=\text{empty list}$
\STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
\FOR{$\text{gene in common}$}
- \STATE $gen1 \leftarrow open(NCBI\_Genes(gene)).read()$
- \STATE $gen2 \leftarrow open(Dogma\_Genes(gene)).read()$
- \STATE $score \leftarrow geneChk(gen1,gen2)$
+ \STATE $g1 \leftarrow open(NCBI\_Genes(gene)).read()$
+ \STATE $g2 \leftarrow open(Dogma\_Genes(gene)).read()$
+ \STATE $score \leftarrow geneChk(g1,g2)$
\IF {$score > Threshold$}
\STATE $geneList \leftarrow gene$
\ENDIF
\caption{Find the Maximum Similarity Score between two sequences}
\label{Alg3:genechk}
\begin{algorithmic}
-\REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
+\REQUIRE $g1,g2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
\ENSURE $\text{Maximum similarity score}$
-\STATE $Score1 \leftarrow needle(gen1,gen2)$
-\STATE $Score2 \leftarrow needle(gen1,Reverse(gen2))$
-\STATE $Score3 \leftarrow needle(gen1,Complement(gen2))$
-\STATE $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$
-\RETURN $max(Score1, Score2, Score3, Score4)$
+\STATE $score1 \leftarrow needle(g1,g2)$
+\STATE $score2 \leftarrow needle(g1,Reverse(g2))$
+\STATE $score3 \leftarrow needle(g1,Complement(g2))$
+\STATE $score4 \leftarrow needle(g1,Reverse(Complement(g2)))$
+\RETURN $max(score1,score2,score3,score4)$
\end{algorithmic}
\end{algorithm}
-% THIS SUBSECTION MUST BE IMPROVED
-
\subsubsection{Intersection Core Matrix (\textit{ICM})}
To extract core genes, we iteratively collect the maximum number of
\caption{Extract Maximum Intersection Score}
\label{Alg1:ICM}
\begin{algorithmic}
-\REQUIRE $L \leftarrow \text{genomes vectors}$
-\ENSURE $B1 \leftarrow Max Core Vector$
+\REQUIRE $L \leftarrow \text{genomes sets}$
+\ENSURE $B1 \leftarrow \text{Max Core set}$
\FOR{$i \leftarrow 0:len(L)-1$}
\STATE $score \leftarrow 0$
\STATE $core1 \leftarrow set(GenomeList[L[i]])$
\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
-they belong to the same familie or not. By the principle of
+they belong to the same family or not. By the principle of
classification, a small number of genes lost among species indicates
that those species are close to each other and belong to same family,
while a large lost means that we have an evolutionary relationship
\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 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 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}
-
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}
