- sdfdfadqdqaaaaaaaaaaaaaaaaaaa
+
+These last years the cost of sequencing genomes has been greatly
+reduced, and thus more and more genomes are sequenced. Therefore
+automatic annotation tools are required to deal with this continuously
+increasing amount of genomical data. Moreover, a reliable and accurate
+genome annotation process is needed in order to provide strong
+indicators for the study of life\cite{Eisen2007}.
+
+Various annotation tools (\emph{i.e.}, cost-effective sequencing
+methods\cite{Bakke2009}) producing genomic annotations at many levels
+of detail have been designed by different annotation centers. Among
+the major annotation centers we can notice NCBI\cite{Sayers01012011},
+Dogma \cite{RDogma}, cpBase \cite{de2002comparative},
+CpGAVAS \cite{liu2012cpgavas}, and
+CEGMA\cite{parra2007cegma}. Usually, previous studies used one out of
+three methods for finding genes in annoted genomes using data from
+these centers: \textit{alignment-based}, \textit{composition based},
+or a combination of both~\cite{parra2007cegma}. The alignment-based
+method is used when trying to predict a coding gene (\emph{i.e.}.
+genes that produce proteins) by aligning a genomic DNA sequence with a
+cDNA sequence coding an homologous protein \cite{parra2007cegma}.
+This approach is also used in GeneWise\cite{birney2004genewise}. The
+alternative method, the composition-based one (also known
+as \textit{ab initio}) is based on a probabilistic model of gene
+structure to find genes according to the gene value probability
+(GeneID \cite{parra2000geneid}). Such annotated genomic data will be
+used to overcome the limitation of the first method described in the
+previous section. In fact, the second method we propose finds core
+genes from large amount of chloroplast genomes through genomic
+features extraction.
+
+Figure~\ref{Fig1} presents an overview of the entire method pipeline.
+More precisely, the second method consists of three
+stages: \textit{Genome annotation}, \textit{Core extraction},
+and \textit{Features Visualization} which highlights the
+relationships. To understand the whole core extraction process, we
+describe briefly each stage below. More details will be given in the
+coming subsections. The method uses as starting point some sequence
+database chosen among the many international databases storing
+nucleotide sequences, like the GenBank at NBCI \cite{Sayers01012011},
+the \textit{EMBL-Bank} \cite{apweiler1985swiss} in Europe
+or \textit{DDBJ} \cite{sugawara2008ddbj} in Japan. Different
+biological tools can analyze and annotate genomes by interacting with
+these databases to align and extract sequences to predict genes. The
+database in our method must be taken from any confident data source
+that stores annotated and/or unannotated chloroplast genomes. We have
+considered the GenBank-NCBI \cite{Sayers01012011} database as sequence
+database: 99~genomes of chloroplasts were retrieved. These genomes
+lie in the eleven type of chloroplast families and Table \ref{Tab2}
+summarizes their distribution in our dataset.
+
+\begin{figure}[h]
+ \centering
+ \includegraphics[width=0.75\textwidth]{generalView}
+\caption{A general overview of the annotation-based approach}\label{Fig1}
+\end{figure}
+
+Annotation, which is the first stage, is an important task for
+extracting gene features. Indeed, to extract good gene feature, a good
+annotation tool is obviously required. To obtain relevant annotated
+genomes, two annotation techniques from NCBI and Dogma are used. The
+extraction of gene feature, the next stage, can be anything like gene
+names, gene sequences, protein sequences, and so on. Our method
+considers gene names, gene counts, and gene sequence for extracting
+core genes and producing chloroplast evolutionary tree. The final
+stage allows to visualize genomes and/or gene evolution in
+chloroplast. Therefore we use representations like tables,
+phylogenetic trees, graphs, etc. to organize and show genomes
+relationships, and thus achieve the goal of representing gene
+evolution. In addition, comparing these representations with ones
+issued from another annotation tool dedicated to large population of
+chloroplast genomes give us biological perspectives to the nature of
+chloroplasts evolution. Notice that a local database linked with each
+pipe stage is used to store all the informations produced during the
+process.
+
+\input{population_Table}
+
+\subsection{Genome annotation techniques}
+
+For the first stage, genome annotation, many techniques have been
+developed to annotate chloroplast genomes. These techniques differ
+from each others in the number and type of predicted genes (for
+example: \textit{Transfer RNA (tRNA)} and \textit{Ribosomal RNA
+(rRNA)} genes). Two annotation techniques from NCBI and Dogma are
+considered to analyze chloroplast genomes.
+
+\subsubsection{Genome annotation from NCBI}
+
+The objective is to generate sets of genes from each genome so that
+genes are organized without any duplication. The input is a list of
+chloroplast genomes annotated from NCBI. More precisely, all genomes
+are stored as \textit{.fasta} files which consists in a collection of
+protein coding genes\cite{parra2007cegma,RDogma} (gene that produce
+proteins) organized in coding sequences. To be able build the set of
+core genes, we need to preprocess these genomes
+using \textit{BioPython} package \cite{chapman2000biopython}. This
+step starts by converting each genome from FASTA file format to
+GenVision \cite{geneVision} format from DNASTAR. Each genome is thus
+converted in a list of genes, with gene names and gene counts. Gene
+name duplications can be accumulated during the treatment of a genome.
+These duplications come from gene fragments (\emph{e.g.} gene
+fragments treated with NCBI) and from chloroplast DNA sequences. To
+ensure that all the duplications are removed, each list of gene is
+translated into a set of genes. Note that NCBI genome annotation
+produces genes except \textit{Ribosomal (rRNA)} genes.
+
+\subsubsection{Genome annotation from Dogma}
+
+Dogma stands for \textit{Dual Organellar GenoMe Annotator}. It is an
+annotation tool developed at University of Texas in 2004 for plant
+chloroplast and animal mitochondrial genomes. This tool has its own
+database for translating a genome in all six reading frames and
+queries the amino acid sequence database using
+BLAST \cite{altschul1990basic} (\emph{i.e.} Blastx) with various
+parameters. Protein coding genes are identified in an input genome
+using sequence similarity of genes in Dogma database. In addition in
+comparison with NCBI annotation tool, Dogma can produce
+both \textit{Transfer RNAs (tRNA)} and \textit{Ribosomal RNAs (rRNA)},
+verify their start and end positions. Another difference is also that
+there is no gene duplication with Dogma after solving gene
+fragmentation. In fact, genome annotation with Dogma can be the key
+difference when extracting core genes.
+
+The Dogma annotation process is divided into two tasks. First, we
+manually annotate chloroplast genomes using Dogma web tool. The output
+of this step is supposed to be a collection of coding genes files for
+each genome, organized in GeneVision file. The second task is to solve
+the gene duplication problem and therefore we have use two
+methods. The first method, based on gene name, translates each genome
+into a set of genes without duplicates. The second method avoid gene
+duplication through a defragment process. In each iteration, this
+process starts by taking a gene from gene list, searches for gene
+duplication, if a duplication is found, it looks on the orientation of
+the fragment sequence. If it is positive it appends directly the
+sequence to gene files. Otherwise reverse complement operations are
+applied on the sequence, which is then also append to gene files.
+Finally, a check for missing start and stop codons is performed. At
+the end of the annotation process, all the genomes are fully
+annotated, their genes are defragmented, and gene counts are
+available.
+
+\subsection{Core genes extraction}
+
+The goal of this stage is to extract maximum core genes from sets of
+genes. To find core genes, the following methodology is applied.
+
+\subsubsection{Preprocessing}
+
+In order to extract core genomes in a suitable manner, the genomic
+data are preprocessed with two methods: on the one hand a method based
+on gene name and count, and on the other hand a method based on a
+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
+nam and genes counts can be extracted by a specific length command.
+The \textit{Intersection Core Matrix}, described in next subsection,
+is then computed to extract the core genes. The problem with this
+method can be stated as follows: how can we ensure that the gene which
+is predicted in core genes is the same gene in leaf genomes? The
+answer to this problem is that if the sequences of any gene in a
+genome annotated from Dogma and NCBI are similar with respect to a
+given threshold, then we do not have any problem with this
+method. When the sequences are not similar we have a problem, because
+we cannot decide which sequence belongs to a gene in core genes.
+
+The second method is based on the underlying idea: we can predict the
+the best annotated genome by merging the annotated genomes from NCBI
+and Dogma according to a quality test on genes names and sequences. To
+obtain all quality genes of each genome, we consider the following
+hypothesis: any gene will appear in the predicted genome if and only
+if the annotated genes in NCBI and Dogma pass a specific threshold
+of \textit{quality control test}. In fact, the Needle-man Wunch
+algorithm is applied to compare both sequences with respect to a
+threshold. If the alignment score is above the threshold, then the
+gene will be retained in the predicted genome, otherwise the gene is
+ignored. Once the prediction of all genomes is done,
+the \textit{Intersection Core Matrix} is computed on these new genomes
+to extract core genes, as explained in Algorithm \ref{Alg3:thirdM}.
+
+\begin{algorithm}[H]
+\caption{Extract new genome based on gene quality test}
+\label{Alg3:thirdM}
+\begin{algorithmic}
+\REQUIRE $Gname \leftarrow \text{Genome Name}, Threshold \leftarrow 65$
+\ENSURE $geneList \leftarrow \text{Quality genes}$
+\STATE $dir(NCBI\_Genes) \leftarrow \text{NCBI genes of Gname}$
+\STATE $dir(Dogma\_Genes) \leftarrow \text{Dogma genes of Gname}$
+\STATE $geneList=\text{empty list}$
+\STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
+\FOR{$\text{gene in common}$}
+ \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
+\ENDFOR
+\RETURN $geneList$
+\end{algorithmic}
+\end{algorithm}
+
+\textbf{geneChk} is a subroutine used to find the best similarity score between
+two gene sequences after applying operations like \textit{reverse}, {\it complement},
+and {\it reverse complement}. Algorithm~\ref{Alg3:genechk} gives the outline of
+geneChk subroutine.
+
+\begin{algorithm}[H]
+\caption{Find the Maximum Similarity Score between two sequences}
+\label{Alg3:genechk}
+\begin{algorithmic}
+\REQUIRE $g1,g2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
+\ENSURE $\text{Maximum similarity score}$
+\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}
+
+\subsubsection{Intersection Core Matrix (\textit{ICM})}
+
+To extract core genes, we iteratively collect the maximum number of
+common genes between genomes and therefore during this stage
+an \textit{Intersection Core Matrix} (ICM) is built. ICM is a two
+dimensional symmetric matrix where each row and each column correspond
+to one genome. Hence, an element of the matrix stores
+the \textit{Intersection Score} (IS): the cardinality of the core
+genes set obtained by intersecting one genome with another
+one. Maximum cardinality results in selecting the two genomes having
+the maximum score. Mathematically speaking, if we have $n$ genomes in
+local database, the ICM is an $n \times n$ matrix whose elements
+satisfy:
+\begin{equation}
+score_{ij}=\vert g_i \cap g_j\vert
+\label{Eq1}
+\end{equation}
+\noindent where $1 \leq i \leq n$, $1 \leq j \leq n$, and $g_i, g_j$ are
+genomes. The generation of a new core gene depends obviously on the
+value of the intersection scores $score_{ij}$. More precisely, the
+idea is to consider a pair of genomes such that their score is the
+largest element in ICM. These two genomes are then removed from matrix
+and the resulting new core genome is added for the next iteration.
+The ICM is then updated to take into account the new core gene: new IS
+values are computed for it. This process is repeated until no new core
+gene can be obtained.
+
+We can observe that the ICM is very large due to the amount of
+data. As a consequence, the computation of the intersection scores is
+both time and memory consuming. However, since ICM is a symetric
+matrix we can reduce the computation overhead by considering only its
+triangular upper part. The time complexity for this process after
+enhancement is thus $O(\frac{n.(n-1)}{2})$. Algorithm ~\ref{Alg1:ICM}
+illustrates the construction of the ICM matrix and the extraction of
+the core genes, where \textit{GenomeList} represents the database
+storing all genomes data. At each iteration, it computes the maximum
+core genes with its two genomes parents.
+
+% ALGORITHM HAS BEEN REWRITTEN
+
+\begin{algorithm}[H]
+\caption{Extract Maximum Intersection Score}
+\label{Alg1:ICM}
+\begin{algorithmic}
+\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 $core1 \leftarrow set(GenomeList[L[i]])$
+ \STATE $g1 \leftarrow L[i]$
+ \FOR{$j \leftarrow i+1:len(L)$}
+ \STATE $core2 \leftarrow set(GenomeList[L[j]])$
+ \STATE $Core \leftarrow core1 \cap core2$
+ \IF{$len(Core) > score$}
+ \STATE $score \leftarrow len(Core)$
+ \STATE $g2 \leftarrow L[j]$
+ \ENDIF
+ \ENDFOR
+ \STATE $B1[score] \leftarrow (g1,g2)$
+\ENDFOR
+\RETURN $max(B1)$
+\end{algorithmic}
+\end{algorithm}
+
+\subsection{Features visualization}
+
+The goal is to visualize results by building a tree of evolution. All
+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 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 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
+between species from different families. To depict the links between
+species clearly, we built a phylogenetic tree showing the
+relationships based on the distances among genes sequences. Many tools
+are available to obtain a such tree, for example:
+PHYML\cite{guindon2005phyml},
+RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml}, BioNJ, and
+TNT\cite{goloboff2008tnt}}. In this work, we chose to use
+RAxML\cite{stamatakis2008raxml,stamatakis2005raxml} because it is
+fast, accurate, and can build large trees when dealing with a large
+number of genomic sequences.
+
+The procedure used to built a phylogenetic tree is as follows:
+\begin{enumerate}
+\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}
+
+\begin{figure}[H]
+ \centering \includegraphics[width=0.75\textwidth]{Whole_system}
+ \caption{Overview of the pipeline}\label{wholesystem}
+\end{figure}
+
+\section{Implementation}
+We implemented the three algorithms using dell laptop model latitude E6430 with 6 GB of memory, and Intel core i5 processor of 2.5 Ghz$\times 4$ with 3 MB of CPU cash. We built the code using python version 2.7 under ubuntu 12.04 LTS. We also used python packages such as os, Biopython, memory\_profile, re, numpy, time, shutil, and xlsxwriter to extract core genes from large amount of chloroplast genomes. Table \ref{Etime}, show the annotation type, execution time, and the number of core genes for each method:
+
+\begin{center}
+\begin{tiny}
+\begin{table}[H]
+\caption{Type of Annotation, Execution Time, and core genes for each method}\label{Etime}
+\begin{tabular}{p{2.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.2cm}}
+\hline\hline
+ & \multicolumn{2}{c}{Annotation} & \multicolumn{2}{c}{Features} & \multicolumn{2}{c}{E. Time} & \multicolumn{2}{c}{C. genes} & \multicolumn{2}{c}{Bad Gen.} \\
+~ & N & D & Name & Seq & N & D & N & D & N & D \\
+\hline
+Gene prediction & $\surd$ & - & - & $\surd$ & ? & - & ? & - & 0 & -\\[0.5ex]
+Gene Features & $\surd$ & $\surd$ & $\surd$ & - & 4.98 & 1.52 & 28 & 10 & 1 & 0\\[0.5ex]
+Gene Quality & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{$\simeq$3 days + 1.29} & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{1}\\[1ex]
+\hline
+\end{tabular}
+\end{table}
+\end{tiny}
+\end{center}
+
+In table \ref{Etime}, we show that all methods need low execution time to finish extracting core genes from large chloroplast genomes except in gene quality method where we need about 3-4 days for sequence comparisons to construct quality genomes then it takes just 1.29 minute to extract core genes. This low execution time give us a privilage to use these methods to extract core genes on a personal comuters rather than main frames or parallel computers. In the table, \textbf{N} means NCBI, \textbf{D} means DOGMA, and \textbf{Seq} means Sequence. Annotation is represent the type of algorithm used to annotate chloroplast genome. We can see that the two last methods used the same annotation sources. Features means the type of gene feature used to extract core genes, and this is done by extracting gene name, gene sequence, or both of them. The execution time is represented the whole time needed to extract core genes in minutes. We can see in the table that the second method specially with DOGMA annotation has the lowest execution time of 1.52 minute. In last method We needs approxemetly three days (this period is depend on the amount of genomes) to finish the operation of extracting quality genomes only, while the execution time will be 1.29 minute if we have quality genomes. The number of core genes is represents the amount of genes in the last core genome. The main goal is to find the maximum core genes that simulate biological background of chloroplasts. With NCBI we have 28 genes for 96 genomes instead of 10 genes with DOGMA for 97 genomes. But the biological distribution of genomes with NCBI in core tree did not reflect good biological perspective. While in the core tree with DOGMA, the distribution of genomes are biologically good. Bad genomes are the number of genomes that destroy core genes because of the low number of gene intersection. \textit{NC\_012568.1 Micromonas pusilla}, is the only genome that observed to destroy the core genome with NCBI based on the method of gene features and in the third method of gene quality. \\
+
+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{Memory usages in (MB) for each methodology}\label{mem}
+\begin{tabular}{p{2.5cm}p{1.5cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}}
+\hline\hline
+Method& & Load Gen. & Conv. gV & Read gV & ICM & Core 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 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\
+\hline
+\end{tabular}
+\end{table}
+\end{tiny}
+\end{center}
+
+We used a package from PyPI~(\textit{the Python Package Index}) where located at~ (https://pypi.python.org/pypi) named \textit{Memory\_profile} to extract all the values in table \ref{mem}. In this table, all the values are presented in mega bytes and \textit{gV} means genevision file format. We see that all memory levels in all methods are reletively low and can be available in any personal computer. All memory values shows that the method of gene features based on DOGMA annotation have the more resonable memory values to extract core genome from loading genomes until extracting core sequences. The third method, gives us the lowest values if we already have the quality genomes, but it will consume high memory locations if we do not have them. Also, the amount of memory locations in the third method vary according to the size of each genome.\\
+
+
+
+
