-The field of Genome annotation pay a lot of attentions where the ability to collect and analysis genomical data can provide strong indicator for the study of life\cite{Eisen2007}. A lot of genome annotation centres present various types of annotation tools (i.e cost-effective sequencing methods\cite{Bakke2009}) on different annotation levels. Methods of gene finding in annotated genome can be categorized as: Alignment-based, composition based or combination of both\cite{parra2007cegma}. The Alignment-based method is used when we try to predict a coding gene (i.e. Genes that produce proteins) by aligning DNA sequence of gene to the protein of cDNA sequence of homolog\cite{parra2007cegma}. This approache also used in GeneWise\cite{birney2004genewise} with known splicing signals. Composition-based mothod (known as \textit{ab initio} is based on a probabilistic model of gene structure to find genes and/or new genes accoding to the probility gene value, this method like GeneID\cite{parra2000geneid}. In this section, we will consider a new method of finding core genes from large amount of chloroplast genomes, as a solution of the previous method where stated in section two. This method is based on extracting gene features. The question now is how can we have good annotation genome? To answer this question, we need to focusing on studying the annotation's accuracy (systematically\cite{Bakke2009}) of the genome. The general overview of the system is illustrated in Figure \ref{Fig1}.\\
-\begin{figure}[H]
-\caption{A general overview of the system}
- \centering
- \includegraphics[width=0.5\textwidth]{generalView}
- \label{Fig1}
-\end{figure}
+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}.
-In Figure 1, we illustrate the general overview of the system. In this system, there are three main stages: \textit{Database, Gene extraction ,} and \textit{relationships}. There are many international nucleotide sequence databases like (GenBank/NCBI in USA at (http://www.ncbi.nlm.nih.gov/genbank/),\\ EMBL-Bank/ENA/EBI in Europe at (http://www.ebi.ac.uk/ena/), and DDBJ in Japon at (http://www.ddbj.nig.ac.jp/)). In our work, the database must be any confident data source that store annotated or unannotated chloroplast genomes. We will consider GenBank/NCBI database as our nucleotide sequences database. Extract Gene Features, we refer to our main process of extracting needed information to find core genome from well large annotation genomes. Thanks to good annotation tool that lead us to extract good gene features. Here, Gene features can be anything like (genes names, gene sequences, protein sequence,...etc). To verify the results from our system, we need to organize and represent our results in the form of (tables, phylogenetic trees, graphs,...,etc), and compare these results with another annotation tool like Dogma\cite{RDogma}. All this work is to see the relationship among our large population of chloroplast genomes and find the core genome for root ancestral node. Furthermore, in this part we can visualize the evolution relationships of different chloroplast organisms.\\
-The output from each stage in our system will be considered to be an input to the second stage and so on. The rest of this section, in section 3.1, we will introduce some annotation problem with NCBI chloroplast genomes and we will discuss our method for how can we extract useful data. Section 3.2 we will present here our system for calculating evolutionary core genome based on another annotation tool than NCBI.
-
-\subsection{Genomes Samples}
-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 \ref{Tab1}.\pagebreak
-
-\footnotesize
-\setlength\LTleft{-30pt}
-\setlength\LTright{-30pt}
-\begin{longtable}{@{\extracolsep{\fill}}llllllllll@{}}
-
-\caption[NCBI Genomes Families]{List of family groups of Chloroplast Genomes from NCBI\label{Tab1}}\\
- % Heading
- \hline\hline
- {\textbf{Category}} & {\textbf{Counts}} & {\textbf{Accession No}} & {\textbf{Scientific Name}} \\
- \hline
- %Entering First line
- & & NC\_001713.1 & Odontella sinensis \\
- & & NC\_008588.1 & Phaeodactylum tricornutum \\
- & & NC\_010772.1 & Heterosigma akashiwo \\
- & & NC\_011600.1 & Vaucheria litorea \\
- & & NC\_012903.1 & Aureoumbra lagunensis \\
- Algues Brunes & 11 & NC\_014808.1 & Thalassiosira oceanica \\
- & & NC\_015403.1 & Fistulifera sp \\
- & & NC\_016731.1 & Synedra acus \\
- & & NC\_016735.1 & Fucus vesiculosus \\
- & & NC\_018523.1 & Saccharina japonica \\
- & & NC\_020014.1 & Nannochloropsis gadtina \\ [1ex]
- %Entering second group
- & & NC\_000925.1 & Porphyra purpurea \\
- Algues Rouges & 3 & NC\_001840.1 & Cyanidium caldarium \\
- & & NC\_006137.1 & Gracilaria tenuistipitata \\ [1ex]
- %Entering third group
- & & NC\_000927.1 & Nephroselmis olivacea \\
- & & NC\_002186.1 & Mesotigma viride \\
- & & NC\_005353.1 & Chlamydomonas reinhardtii \\
- & & NC\_008097.1 & Chara vulgaris \\
- & & NC\_008099.1 & Oltmannsiellopsis viridis \\
- & & NC\_008114.1 & Pseudoclonium akinetum \\
- & & NC\_008289.1 & Ostreococcus tauri \\
- & & NC\_008372.1 & Stigeoclonium helveticum \\
- Algues Vertes & 17 & NC\_008822.1 & Chlorokybus atmophyticus \\
- & & NC\_011031.1 & Oedogonium cardiacum \\
- & & NC\_012097.1 & Pycnococcus provaseolii \\
- & & NC\_012099.1 & Pyramimonas parkeae \\
- & & NC\_012568.1 & Micromonas pusilla \\
- & & NC\_014346.1 & Floydiella terrestris \\
- & & NC\_015645.1 & Schizomeris leibleinii \\
- & & NC\_016732.1 & Dunaliella salina \\
- & & NC\_016733.1 & Pedinomonas minor \\ [1ex]
- %Entering fourth group
- & & NC\_001319.1 & Marchantia polymorpha \\
- Bryophytes & 3 & NC\_004543.1 & Anthoceros formosae \\
- & & NC\_005087.1 & Physcomitrella patens \\ [1ex]
- %Entering fifth group
- & & NC\_014267.1 & Kryptoperidinium foliaceum \\
- Dinoflagelles & 2
- & NC\_014287.1 & Durinskia baltica \\ [1ex]
- %Entering sixth group
- & & NC\_001603.2 & Euglena gracilis \\
- Euglenes & 2 & NC\_020018.1 & Monomorphina aenigmatica \\ [1ex]
- %Entering seventh group
- & & NC\_003386.1 & Psilotum nudum \\
- & & NC\_008829.1 & Angiopteris evecta \\
- Filicophytes & 5 & NC\_014348.1 & Pteridium aquilinum \\
- & & NC\_014699.1 & Equisetum arvense \\
- & & NC\_017006.1 & Mankyua chejuensis \\ [1ex]
- % Entering eighth group
- & & NC\_001568.1 & Epifagus virginiana \\
- & & NC\_001666.2 & Zea Mays \\
- & & NC\_005086.1 & Amborella trichopoda \\
- & & NC\_006050.1 & Nymphaea alba \\
- & & NC\_006290.1 & Panax ginseng \\
- & & NC\_007578.1 & Lactuca sativa \\
- & & NC\_007957.1 & vitis vinifera \\
- & & NC\_007977.1 & Helianthus annuus \\
- & & NC\_008325.1 & Daucus carota \\
- & & NC\_008336.1 & Nandina domestica \\
- & & NC\_008359.1 & Morus indica \\
- & & NC\_008407.1 & Jasminum nudiflorum \\
- & & NC\_008456.1 & Drimys granadensis \\
- & & NC\_008457.1 & Piper cenocladum \\
- & & NC\_009601.1 & Dioscorea elephantipes \\
- & & NC\_009765.1 & Cuscuta gronovii \\
- & & NC\_009808.1 & Ipomea purpurea \\
- Angiospermes & 45 & NC\_010361.1 & Oenothera biennis \\
- & & NC\_010433.1 & Manihot esculenta \\
- & & NC\_010442.1 & Trachelium caeruleum \\
- & & NC\_013707.2 & Olea europea \\
- & & NC\_013823.1 & Typha latifolia \\
- & & NC\_014570.1 & Eucalyptus \\
- & & NC\_014674.1 & Castanea mollissima \\
- & & NC\_014676.2 & Theobroma cacao \\
- & & NC\_015830.1 & Bambusa emeiensis \\
- & & NC\_015899.1 & Wolffia australiana \\
- & & NC\_016433.2 & Sesamum indicum \\
- & & NC\_016468.1 & Boea hygrometrica \\
- & & NC\_016670.1 & Gossypium darwinii \\
- & & NC\_016727.1 & Silene vulgaris \\
- & & NC\_016734.1 & Brassica napus \\
- & & NC\_016736.1 & Ricinus communis \\
- & & NC\_016753.1 & Colocasia esculenta \\
- & & NC\_017609.1 & Phalaenopsis equestris \\
- & & NC\_018357.1 & Magnolia denudata \\
- & & NC\_019601.1 & Fragaria chiloensis \\
- & & NC\_008796.1 & Ranunculus macranthus \\
- & & NC\_013991.2 & Phoenix dactylifera \\
- & & NC\_016068.1 & Nicotiana undulata \\ [1ex]
- %Entering ninth group
- & & NC\_009618.1 & Cycas taitungensis \\
- & & NC\_011942.1 & Gnetum parvifolium \\
- & & NC\_016058.1 & Larix decidua \\
- Gymnosperms & 7 & NC\_016063.1 & Cephalotaxus wilsoniana \\
- & & NC\_016065.1 & Taiwania cryptomerioides \\
- & & NC\_016069.1 & Picea morrisonicola \\
- & & NC\_016986.1 & Gingko biloba \\ [1ex]
- %Entering tenth group
- Haptophytes & 1 & NC\_007288.1 & Emiliana huxleyi\\ [1ex]
- %Entering eleventh group
- Lycophytes & 2 & NC\_014675.1 & Isoetes flaccida \\
- & & NC\_006861.1 & Huperzia lucidula \\
- \hline
-\end{longtable}
-
-\subsection{Gene Extraction Techniques from annotated NCBI genomes}
-With NCBI, the idea is to use the existing annotations of NCBI with chloroplast genomes. To extract the core and pan genes: Core extraction techniques with NCBI are based on two techniques: Gene count and Gene contents based on some similarity issues.
-
-\subsubsection{Core genes based on NCBI Gene names and Counts}
-The trivial and simple idea to construct the core genome is based on the extraction of Genes names (as gene presence or absence). For instant, in this stage neither sequence comparison nor new annotation were made, we just want to extract all gene counts stored in each chloroplast genome then find the intersection core genes based on gene names.\\
-
-\textbf{Step I: pre-processing}\\
-The objective from this step is to organize, solve genes duplications, and generate sets of genes for each genome. The input to the system is a list of genomes from NCBI stored as \textit{.fasta} files that include a collection of Protein coding genes\cite{parra2007cegma,RDogma}(genes that produce protein) with its coding sequences.
-As a preparation step to achieve the set of core genes, we need to translate these genomes using \textit{BioPython} package\cite{chapman2000biopython}, and extracting all information needed to find the core genes. The process starts by converting each genome in fasta format to GenVision\cite{geneVision} format from DNASTAR, and this is not an easy job. The output from this operation is a lists of genes stored in a local database for genomes, their genes names and genes counts. In this stage, we will accumulate some Gene duplications with each genome treated. In other words, duplication in gene name can comes from genes fragments as long as chloroplast DNA sequences. We defines \textit{Identical state} to be the state that each gene present only one time in a genome (i.e Gene has no copy) without considering the position or gene orientation. This state can be reached by filtering the database from redundant gene name. To do this, we have two solutions: first, we made an orthography checking. Orthography checking is used to merge fragments of a gene to form one gene.
-Second, we convert the list of genes names for each genome (i.e. after orthography check) in the database to be a set of genes names. Mathematically speaking, if $G=\left[g_1,g_2,g_3,g_1,g_3,g_4\right]$ is a list of genes names, by using the definition of a set in mathematics, we will have $set(G)=\{g_1,g_2,g_3,g_4\}$, and $|G|=4$ where $|G|$ is the cardinality number of the set $G$ which represent the number of genes in the set. With NCBI genomes, we do not have a problem of genes fragments because they already treated it, but there are a problem of genes orthography. In our method, this can generate the problem of gene lost and effect in turn the core genes.
-The whole process of extracting core genome based on genes names and counts among genomes is illustrate in Figure \ref{Fig2}.
+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.
-\begin{figure}[H]
+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=0.9\textwidth]{NCBI_GeneName}
-
- \caption{Extracting Core genes based on NCBI Gene name and Counts}
- \label{Fig2}
+ \includegraphics[width=0.75\textwidth]{generalView}
+\caption{A general overview of the annotation-based approach}\label{Fig1}
\end{figure}
-\textbf{Step II: Gene Intersection}\\
-The goal of this step is trying to find maximum core genes from sets of genes in the database. The idea for finding core genes is to collect in each iteration the maximum number of common genes. To do this, the system build an \textit{Intersection core matrix(ICM)}. ICM here is a two dimensional symmetric matrix where each row and column represent a set of genes for one genome in the local database. Each position in ICM stores the \textit{intersection scores}. Intersection Score(IS), is the cardinality number of a core genes comes from intersecting in each iteration the set of genes for one genome with all other gene sets belong to the rest of genomes in the database. Taking maximum cardinality from each row and then taking the maximum of them will result to select the best two genomes with their maximum core. Mathematically speaking, if we have an $mxn$ matrix where $m,n=$number of genomes in database. lets consider $Z=max_{i<j}(\vert x_i \cap x_j\vert)$ where $x_i, x_j$ are sets of row and column elements in a matrix. if $Z=0$ then we have \textit{disjoint relation} (i.e no common genes between to genomes). In this case the system ignore the set of genes that smash the core genes. Otherwise, The system remove these two genomes from ICM and add new core with a coreID of them to ICM for the calculation in next iteration. The partial core genes generated with its set of genes will store in a database for reused for drawing the tree. this process repeat until all genomes treated.
-We observe that ICM will result to be very large because of the huge amount of data that it stores. In addition, this will results to be time and memory consuming for calculating the intersection scores by using just genes names. To increase the speed of calculations, we can calculate the upper triangle scores only and exclude diagonal scores that is why we write $max_{i<j}$. This will reduce whole processing time and memory to half. The time complexity for this process after enhancement changed from $O(n^2)$ to $O((n-1)\log{n})$.\\
+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})}
-The Algorithm of construction the matrix and extracting maximum core genes where illustrated in Algorithm \ref{Alg1}. The output from this step is the maximum core genes with its genomes to draw it in a tree.
+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}
+\label{Alg1:ICM}
\begin{algorithmic}
-\REQUIRE $L \leftarrow sets of genomes genes$
-\ENSURE $B1 \leftarrow Max core$
+\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 $score1 \leftarrow 0$
- \STATE $g1,g2 \leftarrow$ " "
+ \STATE $g1 \leftarrow L[i]$
\FOR{$j \leftarrow i+1:len(L)$}
- \STATE $core2 \leftarrow set(GenomeList[L[i]])$
- \IF{$i < j$}
- \STATE $Core \leftarrow core1 \cap core2$
- \IF{$len(Core) > score1$}
- \STATE $g1 \leftarrow L[i]$
- \STATE $g2 \leftarrow L[j]$
- \STATE $Score \leftarrow len(Core)$
- \ELSIF{$len(Core) == 0$}
- \STATE $g1 \leftarrow L[i]$
- \STATE $g2 \leftarrow L[j]$
- \STATE $Score \leftarrow -1$
- \ENDIF
- \ENDIF
+ \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 $B1[score1] \leftarrow (g1,g2)$
+ \STATE $B1[score] \leftarrow (g1,g2)$
\ENDFOR
\RETURN $max(B1)$
\end{algorithmic}
\end{algorithm}
-\textit{GenomeList} represents the database.\\
+\subsection{Features visualization}
-\textbf{Step III: Drawing the Tree}\\
-The goal here is to visualizing the results by build a tree of evolution. The system can produce this tree automatically by using Dot graphs package\cite{gansner2002drawing} from Graphviz library and all information available in a database. Core genes generated with their genes can be very important information in the tree, because they can viewed as an ancestor information for two genomes or more. Further more, each node represents a genome or core as \textit{(Genes count:Family name, Scientific names, Accession number)}, Edges represent numbers of lost genes from genomes-core or core-core relationship. The number of lost genes here can be an important factor for evolution, it represents how much lost of genes for the species in same or different families. By the principle of classification, small number of gene lost among species indicate that those species are related together and belong to same family, while big genes lost means that species is far to be in the same family. To see the picture clearly, Phylogenetic tree is an evolutionary tree generated also by the system. Generating this tree is based on the distances among genes sequences. There are many resources to build such tree (for example: PHYML\cite{guindon2005phyml}, RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml}, BioNJ , and TNT\cite{goloboff2008tnt}}. We consider to use RAxML\cite{stamatakis2008raxml,stamatakis2005raxml} to generate this tree.
+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 main drawback from this method 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.
+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}
-\subsubsection{Extracting Core genome from NCBI gene contents}
-{to do later}
+\begin{figure}[H]
+ \centering \includegraphics[width=0.75\textwidth]{Whole_system}
+ \caption{Overview of the pipeline}\label{wholesystem}
+\end{figure}
-\subsection{Core genes from Dogma Annotation tool}
-In previous section, extracting core genes based on NCBI annotation caused some lost of genes due to annotation process. Annotation can play an important role for these losts, because it represents the first process of gene identification. Good annotation tool still be challenged subject. (Genis Parra in 2007) published a paper state that the subject of accurately genomic and/or gene annotation is still an open source problem, even in the best case scenario where any project has all the expert biologists resources to annotate gene structures, the catalogues of genes can still unclear and still less accurate than experts. Where \cite{Bakke2009} also state ("Errors in the annotations are routinely deposited in databases such as NCBI and used to validate subsequent annotation errors."). So, good core genes still needs good annotation tool. A lot of software today’s were developed for extracted core genes for eukaryote and prokaryote organisms such as CEGMA\cite{parra2007cegma}, Coregenes 3.0\cite{zafar2002coregenes}, and Dogma\cite{RDogma}. The appropriate annotation tool for plant chloroplast and mitochondrial genomes is Dogma.
+\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:
-\subsubsection{Why Dogma rather than NCBI annotation?}
-Dogma is an annotation tool developed in the university of Texas by \cite{RDogma} in 2004. Dogma is an abbreviation of \textit{Dual Organellar GenoMe Annotator}\cite{RDogma} for plant chloroplast and animal mitochondrial genomes.
-It has its own database for translated the genome in all six reading frames and query the amino acid sequence database using Blast\cite{altschul1990basic}(i.e Blastx) with various parameters, and to identify protein coding genes\cite{parra2007cegma,RDogma} in the input genome based on sequence similarity of genes in Dogma database. Further more, it can produce the \textit{Transfer RNAs (tRNA)}\cite{RDogma}, and the \textit{Ribosomal RNAs (rRNA)}\cite{RDogma} and verifying their start and end positions rather than NCBI annotation tool.
+\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}
-\subsubsection{Core genes based on Dogma Genes names and counts}
-The main goal is to get as much as possible the core genes of maximum coding genes. According to \cite{Bakke2009}, annotation method like dogma can give us more reliable coding genes than NCBI. This is because NCBI annotation can carry some annotation and gene identification errors. The general overview of whole process of extraction illustrated in fiqure \ref{dog:Fig1}, the pipeline of extracting core genes can summarize in the following steps:\\
+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. \\
-\begin{figure}[H]
- \centering
- \includegraphics[width=0.7\textwidth]{Dogma_GeneName}
- \label{dog:Fig1}
- \caption{Core genome based on Dogma Gene Name and count}
-\end{figure}
+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.
-\textbf{Step I: Pre-processing}\\
-Pre-processing step represents the key difference between methods of extracting core genes. In figure \ref{dog:Fig1}, The pre-processing step can be divided into two tasks: First, It starts to annotate our genome samples in the form of complete genomes (i.e \textit{Unannotated genomes} from NCBI by using Dogma web tool. The whole annotation process by using dogma website is done manually. The output from the annotation process is considered to be a collection of coding genes file for each genome in the form of GeneVision\cite{geneVision} file format.\\
-Where the second task is to solve gene fragments. Defragment process starts here to solve fragments of coding genes for each genome, this process can avoid gene duplication. All genomes now are fully annotated, their genes were de-fragmented, and genes list and counts identified. These information stored in local database.\\
+\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.\\
-From these two tasks, we can obtain clearly one copy of coding genes. To ensure that genes produces from dogma annotation process is same as the genes in NCBI. We apply separately a quality check process that align the same gene from dogma and NCBI with respect to a specific threshold.\\
-\textbf{Step II: Extraction Core genes}\\
-Extracting core genes will use the same process presented in the section of extracting core genes based on NCBI genes. ICM matrix will be considered by calculating the upper triangular cardinality cores to save time and to find the maximum length of core genes from each iteration see algorithm \ref{Alg1}. From each iteration, two genomes are considered to draw with their maximum cardinality core genes until no genome remain in the database. The key point here is that the intersection genome that smash the core genes in each iteration will be ignored from this competition.\\
-\textbf{Step III: Draw the tree}
-To build the tree of evolution for genomes. The algorithm is considered to take from the data base the first coreID generated from step two and draw sequentially all the genomes that create this core. Sometimes, we have a core genome that intersect with another one. This tree also represented as a set of nodes which represent genome names and a set of edges, which represent the number of gene lost from each genome. Phylogenetic tree also considered here by using RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml} based on calculating the distances among core genes in the database.
-\subsubsection{Core genome from Dogma gene contents}
-[To do Later]
-\begin{figure}[H]
- \centering
- \includegraphics[width=0.7\textwidth]{Dogma_GeneContent}
- \label{dog:Fig2}
- \caption{Core genes based on the comparison of Dogma Genes Sequences}
-\end{figure}
\ No newline at end of file
