-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}.
+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}
+
+% MICHEL : TO BE CONTINUED FROM HERE
+
+\subsection{Genome Annotation Techniques}
+Genome annotation is the second stage in the model pipeline. Many techniques were developed to annotate chloroplast genomes but the problem is that they vary in the number and type of predicted genes (\emph{i.e.} the ability to predict genes and \textit{for example: Transfer RNA (tRNA)} and \textit{Ribosomal RNA (rRNA)} genes). Two annotation techniques from NCBI and Dogma are considered to analyse chloroplast genomes to examine the accuracy of predicted coding genes.
+
+\subsubsection{Genome annotation from NCBI}
+The objective from this step is to organize genes, solve gene duplications, and generate sets of genes from each genome. The input to the system is a list of chloroplast genomes, annotated from NCBI. All genomes stored as \textit{.fasta} files which have a collection of protein coding genes\cite{parra2007cegma,RDogma} (gene that produce proteins) with its coding sequences.
+As a preprocessing step to build the set of core genes, we need to analyse these genomes (using \textit{BioPython} package\cite{chapman2000biopython}). The process starts by converting each genome from fasta format to GenVision\cite{geneVision} format from DNASTAR. The outputs from this operation are lists of genes for each genome, their gene names and gene counts. In this stage, we accumulate some gene duplications for each treated genome. These gene name duplication can come from gene fragments, (e.g. gene fragments treated with NCBI), and from chloroplast DNA sequences. To ensure that all the duplications are removed, each list of genes is translated into a set of genes. NCBI genome annotation produce genes except \textit{Ribosomal rRNA}.
+
+\subsubsection{Genome annotation from Dogma}
+Dogma is an annotation tool developed in the university of Texas in 2004. Dogma is an abbreviation of (\textit{Dual Organellar GenoMe Annotator}) for plant chloroplast and animal mitochondrial genomes.
+It has its own database for translating the genome in all six reading frames and it queries the amino acid sequence database using Blast\cite{altschul1990basic}(\emph{i.e.} Blastx) with various parameters. Furthermore, identify protein coding genes in the input genome based on sequence similarity of genes in Dogma database. In addition, it can produce the \textit{Transfer RNAs (tRNA)}, and the \textit{Ribosomal RNAs (rRNA)} and verifies their start and end positions rather than NCBI annotation tool. There is no gene duplication with dogma after solving gene fragmentation. \\
+Genome annotation with dogma can be the key difference of extracting core genes. The step of annotation is divided into two tasks: first, It starts to annotate complete chloroplast genomes (\emph{i.e.} \textit{Unannotate genome from NCBI} by using Dogma web tool. This process is done manually. The output from dogma is considered to be a collection of coding genes files for each genome in the form of GeneVision file format.
+The second task is to solve gene fragments. Two methods are used to solve gene duplication. First, for the method based on gene name, all the duplications are removed, where each list of genes is translated into a set of genes. Second, for the method of gene quality test, a defragment process used to avoid gene duplication. \\
+In each iteration, this process starts by taking one gene from gene list, searches for gene duplication, if exists, it looks on the orientation of the fragment sequence: if it is positive, then it appends fragment sequence to a gene files. Otherwise, the process applies reverse complement operations on gene sequences and appends it to gene files. An additional process is then applied to check start and stop codons in case of missing. All genomes after this stage are fully annotated, their genes are de-fragmented, and counts are identified.\\
+
+\subsection{Core Genes Extraction}
+The goal of this step is to extract maximum core genes from sets of genes. The methodology of finding core genes is as follow: \\
+
+\subsubsection{Pre-Processing}
+We apply two pre-processing methods to organize and prepare genomes data: the first method based on gene name and count, and the second one is based on 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 name. Genes counts can be extracted by a specific length command. \textit{Intersection Core Matrix} then applied to extract the core genes. The problem with this method is how can we ensure that the gene which is predicted in core genes is the same gene in leaf genomes? The answer of this question is as follows: if the sequence of any gene in a genome annotated from dogma and NCBI are similar with respect to a threshold, we do not have any problem with this method. Otherwise, we have a problem, because we can not decide which sequence goes to a gene in core genes.
+The second pre-processing method states: we can predict the best annotated genome by merging the annotated genomes from NCBI and dogma if we follow the quality of genes names and sequences test. To generate all quality genes of each genome, the hypothesis state: any gene will be in predicted genome if and only if the annotated genes between NCBI and Dogma pass a specific threshold of \textit{quality control test}. To accept the quality test, we applied Needle-man Wunch algorithm to compare two gene sequences with respect to a threshold. If the alignment score passes the threshold, then the gene will be in the predicted genome. Otherwise, the gene is ignored. After predicting all genomes, \textit{Intersection Core Matrix} is applied on these new genomes to extract core genes, as shown in Algorithm \ref{Alg3:thirdM}.
