X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/chloroplast13.git/blobdiff_plain/cd18f1df3aeca00c0c28a2437b36b590a0066a55..cb31aeeaab35d3ab9cfe6e64735071509935d224:/annotated.tex

diff --git a/annotated.tex b/annotated.tex
index 30ad502..9d98987 100644
--- a/annotated.tex
+++ b/annotated.tex
@@ -1,106 +1,274 @@
-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 centers present various types of annotations tools (i.e cost-effective sequencing methods\cite{Bakke2009}) on different annotation levels. Two method 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 homology\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 according to the probability 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 accuracy\cite{Bakke2009} of the genome. A general overview of the system is illustrated in Figure \ref{Fig1}.\\
 
-\begin{figure}[H]  
+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=0.7\textwidth]{generalView}
-\caption{A general overview of the system}\label{Fig1}
+    \includegraphics[width=0.75\textwidth]{generalView}
+\caption{A general overview of the annotation-based approach}\label{Fig1}
 \end{figure}
 
-In Figure 1, we illustrate the general overview of system pipeline: \textit{Database, Genomes annotation, Core extraction, } and \textit{relationships}. We will give a short discussion for each stage in the model in order to understand all core extraction process. Good database (as a first stage) will produce good results, however, many international Banks for nucleotide sequence databases like (GenBank in USA, EMBL-Bank in Europe, and DDBJ in Japon) where exists to store various genomes and DNA species. A lot of Biological tool interact with these databases for (Genome Annotation, Gene extraction, alignments, ... , etc). The database in this model must be taken from any confident data source that store annotated and/or unannotated chloroplast genomes. We will consider GenBank- NCBI database to be our nucleotide sequences database. Annotation (as the second stage) is consider to be the first important task for Extract Gene Features. Thanks to good annotation tool that lead us to extract good gene features. In this paper, two annotation techniques from \textit{NCBI, and Dogma} will be used to extract \textit{genes features}. Extracting Gene feature (as a third stage) can be anything like (genes names, gene sequences, protein sequence,...etc). Our methodologies in this paper will consider gene names, gene counts, and gene sequences for extracting core genes and producing chloroplast evolutionary tree. \\
-In last stage, for achieving our goals with what the biological expert needs, we used the form of (tables, phylogenetic trees, graphs,...,etc) to organize and represent genomes relationships and gene evolution. In addition, comparing these forms with the results from another annotation tool like Dogma\cite{RDogma} for large population of chloroplast genomes that give us biological perspective to the nature of chloroplast evolution. \\
-A Local database attached with each pipe stage used to store all information of extraction process. The output from each stage in our system will be an input to the second stage and so on.
-
-\subsection{Genomes Samples}
-In this research, we retrieved 107 genomes of Chloroplasts from NCBI. Ninety nine genomes of them were considered to work with. These genomes lies in the 11 type of chloroplast families, as shown in Table \ref{Tab1}. The distribution of genomes illustrated in detail in Table \ref{Tab2}.
-
-\begin{table}[H]
-\caption{distribution on Chloroplast Families}\label{Tab1}
-\centering
-\begin{tabular}{c c}
-\hline\hline
-Family & Genome Counts \\ [0.5ex]
-\hline
-Brown Algae & 11 \\
-Red Algae & 03 \\
-Green Algae & 17 \\
-Angiosperms & 46 \\
-Brypoytes & 03 \\
-Dinoflagellates & 02 \\
-Euglena & 02 \\
-Fern & 05 \\
-Gymnosperms & 07 \\
-Lycopodiophyta & 02 \\
-Haptophytes & 01 \\ [1ex]
-\hline
-\end{tabular}
-\end{table}
-
-\input{population_Table}	
-
-\subsection{Genome Annotation Techniques}
-Genome annotation is considered as the second stage in the model pipeline. Many annotation techniques were developed for annotate chloroplast genomes but the problem is that they vary in the number and type of predicting genes (i.e the ability to predict genes and \textit{for example: Transfere 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. Figure \ref{NCBI_annotation}, illstrate two annotation technique.\\
-
-\begin{figure}[H]
-\centering
-\includegraphics[width=0.7\textwidth]{NCBI_annotation}
-\caption{Genome annotation using either NCBI or Dogma}\label{NCBI_annotation}
-\end{figure}
-
-With each annotation model, we provide a quality check class for the flow of chloroplast genomes, as illustrated in figure \ref{NCBI:Annotation}. This class has a direct access to NCBI taxonomy database based on genome accession number to retrieve information for the genome. These information contains \textit{[Scientific name, lineage, Division, taxonomy ID, parentID, and Accession No]}. Examining each genome with this class (i.e based on some parameters), can ignore some genomes from this competition that not match a specific control condition.   
-
-\subsubsection{genome annotation from NCBI} 
-The objective from this step is to organize genes, solve genes duplications, and generate sets of genes from each genome. The input to the system is our list of chloroplast genomes, annotated from NCBI\cite{Sayers01012011}. All genomes stored as \textit{.fasta} files include collection of Protein coding genes\cite{parra2007cegma,RDogma}(gene that produce proteins) 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. A process starts by converting each genome in fasta format to GenVision\cite{geneVision} formats 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 each genome, their genes names and gene 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. Orthographe 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.\\
-The whole process of extracting core genome based on genes names and counts among genomes is illustrate in Figure \ref{NCBI:Annotation}.\\
-
-\begin{figure}[H]
-  \centering
-    \includegraphics[width=0.7\textwidth]{NCBI_GeneName}
-  \caption{NCBI Annotation for Chloroplast genomes}
-  \label{NCBI:Annotation}
-\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 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. There are no gene duplication with dogma after solving gene fragmentation. \\
-Genome Anntation with dogma can be the key difference of extracting core genes. In figure \ref{dog:Annotation}, The step of annotation divided into two tasks: First, It starts to annotate complete choloroplast genomes (i.e \textit{Unannotated genomes} from NCBI by using Dogma web tool. The whole annotation process was done manually. The output from dogma is considered to be 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 immediately after the first task to solve fragments of coding genes for each genome to avoid gene duplication. This process will looks on fragement orientation, if it is negative, then the process apply reverse complement operations on gene sequence. All genomes after this stage are fully annotated, their genes were de-fragmented, genes lists and counts were identified. These information stored in local database.\\
-\begin{figure}[H]
-  \centering
-    \includegraphics[width=0.7\textwidth]{Dogma_GeneName}
-    \caption{Dogma Annotation for Chloroplast genomes}\label{dog:Annotation}
-\end{figure}
 
-From these two tasks, we can obtain clearly one copy of coding genes. To ensure that genes produced from dogma annotation process is same as the genes in NCBI. We apply in parrallel a quality checking process that align each gene from dogma and NCBI with respect to a specific threshold.\\
+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}.
 
-\subsection{Extract Core Genes}
-The goal of this step is trying to extract maximum core genes from sets of genes (\textit{Vectors}) in the local database. The methodology of finding core genes is dividing to three methods: \\
+\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 $gen1 \leftarrow open(NCBI\_Genes(gene)).read()$ 	
+	\STATE $gen2 \leftarrow open(Dogma\_Genes(gene)).read()$
+	\STATE $score \leftarrow geneChk(gen1,gen2)$
+	\IF {$score > Threshold$}
+		\STATE $geneList \leftarrow gene$
+	\ENDIF 
+\ENDFOR
+\RETURN $geneList$
+\end{algorithmic}
+\end{algorithm}
 
-The hypothesis in first method is based on extracting core genes by finding common genes among chloroplast genomes based on extracting gene feature (i.e Gene names, genes counts). Genomes vary in genes counts according to the method of annotation used, so that extracting maximum core genes can be done by constructing Intersection Core Matrix (\textit{ICM}).\\
-While the hypothesis of second method is based on comparing the sequence of  reference genes of one annotated genome with other unannotated genomes sequences in Blast database, by using Blastn\cite{Sayers01012011} (nucleotide sequence alignment tool from NCBI). The last method, is based on merge all genes from NCBI and Dogma annotation, then apply a sequence similarity base method (Quality Control test) using Needle-man Wunch algorithm to predict a new genomes. Using predicted genomes to extract core genes using previous methods. Figure \ref{wholesystem}, illustrate the whole system operations.
+\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{figure}[H]
-  \centering
-    \includegraphics[width=0.7\textwidth]{Whole_system}
-    \caption{Total overview of the system pipeline}\label{wholesystem}
-\end{figure}
+\begin{algorithm}[H]
+\caption{Find the maximum similarity score between two sequences}
+\label{Alg3:genechk}
+\begin{algorithmic} 
+\REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
+\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 $max(Score1, Score2, Score3, Score4)$
+\end{algorithmic}
+\end{algorithm}  
+
+\subsubsection{Intersection Core Matrix (\textit{ICM})}
 
-In the first method, the idea is to collect from each iteration the maximum number of common genes. To do so, the system build an \textit{Intersection core matrix(ICM)}. ICM here is a two dimensional symmetric matrix (considered as a vector space) where each row and column represent a vector for one genome. Each position in ICM stores the \textit{intersection scores}. Intersection Score(IS) is the cardinality number of a core genes comes from intersecting one vector with other vectors in vector space. Taking maximum cardinality from each row and then take the maximum of them will result to select the maximum cardinality in the vector space. Maximum cardinality results to select two genomes with their maximum core. Mathematically speaking, if we have an $n \times m$ vector space matrix where $n=m=\text{number of vectors in local database}$, then lets consider:\\
+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 an $n \times n$
+matrix where $n$ is the number  of genomes in local database, then let
+us consider:
 
 \begin{equation}
 Score=\max_{i<j}\vert x_i \cap x_j\vert
 \label{Eq1}
-\end{equation}\\
-
-Where $x_i, x_j$ are vectors in the matrix. Generate new core genes is depending on the value of intersecting two vectors, we call it $Score$:\\
-$$\text{New Core} = \begin{cases} 
-\text{Ignored} & \text{if $Score=0$;} \\
-\text{new Core id} & \text{if $Score>0$.}
-\end{cases}$$\\
-
-if $Score=0$ then we have \textit{disjoint relation} (i.e no common genes between two genomes). In this case the system ignore the vector that smash the core genes. Otherwise, The system will remove these two vectors from ICM and add new core vector with a \textit{coreID} of them to ICM for the calculation in next iteration. The partial core vectors generated with its values will store in the local database for reused to draw the tree. This process repeat until all vectors 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. This will reduce whole processing time and memory to half. The time complexity for this process after enhancement changed from $O(n^2-n)$ to $O(\frac{(n-1).n}{2})$. The Algorithm of construction the vector matrix and extracting the vector of maximum core genes where illustrated in Algorithm \ref{Alg1:ICM}. The output from this step is the maximum core vector with its two vectors to draw it in a tree.\\
+\end{equation}
+
+% TO BE CONTINUED
+
+\noindent where $x_i, x_j$ are elements in the matrix. The generation of 
+a new core genes is depending on the cardinality value of intersection 
+scores, we call it \textit{Score}:
+$$
+\text{New Core} = 
+\begin{cases}
+\text{Ignored} & \text{if $\textit{Score}=0$;} \\
+\text{new Core id} & \text{if $\textit{Score}>0$.}
+\end{cases}
+$$
+
+if     $\textit{Score}=0$     then     we    have     \textit{disjoint
+relation} \emph{i.e.},  no common genes between two  genomes.  In this
+case  the  system  ignores  the   genome  that  annul  the  core  gene
+size. Otherwise, The system removes these two genomes from ICM and add
+new  core  genome  with a  \textit{coreID}  of  them  to ICM  for  the
+calculation in  next iteration. This  process reduces the size  of ICM
+and repeats until all genomes  are treated \emph{i.e.} ICM has no more
+genomes.  We observe  that ICM is very large because  of the amount of
+data that it stores. This results  to be time and memory consuming for
+calculating  the  intersection  scores.   To  increase  the  speed  of
+calculations, it  is sufficient to  only calculate the  upper triangle
+scores. 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  where all genomes
+data are stored. At each iteration, it computes the maximum core genes
+with its two genomes parents.
 
 \begin{algorithm}[H]
 \caption{Extract Maximum Intersection Score}
@@ -132,76 +300,53 @@ We observe that ICM will result to be very large because of the huge amount of d
 \RETURN $max(B1)$
 \end{algorithmic}
 \end{algorithm}
-\textit{GenomeList} represents the local database.\\
 
-In second Method, due to the number of annotated genomes, annotate each genome can be very exhausted task specially with Dogma, because dogma offer a web tool for annotation, so that, each genome must annotate using this web tool. This operation need to do manually. We prefer to recover this problem by choosing one reference chloroplast and querying each reference gene by using \textit{Blastn} to examin its existance in remaining unannotated genomes in blast database. Collect all match genomes from each gene hits, to satisfy the hypothesis "the gene who exists in maximum number of genomes also exist in a core genes". In addition, we can also extract the maximum core genes by examine how many genes present with each genome?. Algorithm \ref{Alg2:secondM}, state the general algorithm for second method. \\
+\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
+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
+genes lost from a leaf genome or an intermediate core gene.
+
+
+The number of lost genes here can represent an important factor
+for evolution:  it represents how much  is the lost of  genes from the
+species belongs  to same  or different families.  By the  principle of
+classification, a  small number of  gene lost among  species indicates
+that those species are close to  each other and belong to same family,
+while big genes  lost means that we have  an evolutionary relationship
+between species  from different families. 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 use
+RAxML\cite{stamatakis2008raxml,stamatakis2005raxml} because it is fast
+and  accurate  for  build  large  trees for  large  count  of  genomes
+sequences. 
+
+The  procedure of constructing phylogenetic  tree stated in
+the following steps:
 
-\begin{algorithm}[H]
-\caption{Extract Maximum Core genes based on Blast}
-\label{Alg2:secondM}
-\begin{algorithmic} 
-\REQUIRE $Ref\_Genome \leftarrow \text{Accession No}$
-\ENSURE $core \leftarrow \text{Genomes for each gene}$ 
-\FOR{$gene \leftarrow Ref\_Genome$}
-	\STATE $G\_list= \text{empty list}$	
-	\STATE $File \leftarrow Blastn(gene)$
-	\STATE $G\_list \leftarrow File[\text{Genomes names}]$
-	\STATE $Core \leftarrow [Accession\_No:G\_list]$
-\ENDFOR
-\RETURN $Core$
-\end{algorithmic}
-\end{algorithm}
-
-The hypothesis in last method state: we can predict the best annotated genome by merge the annotated genomes from NCBI and dogma based on the quality of genes names and sequences. 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 pass a threshold. If the alignment score pass this threshold, then the gene will be in the predicted genome, else the gene will be ignored. After predicting all genomes, one of previous two methods can be applied to extract core genes. As shown 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}
-
-Here, geneChk is a subroutine in python, it is used to find the best similarity score between two gene sequences after applying operations like \textit{reverse, complement, and reverse complement}. The algorithm of geneChk is illustrated in Algorithm \ref{Alg3:genechk}.
+\begin{enumerate}
+\item Extract gene sequence for all gene in all core genes, store it in database.
+\item Use multiple alignment tool such as (****to be write after see christophe****) to align these sequences with each others.
+\item aligned genomes sequences then submitted to RAxML program to compute the distances and draw phylogenetic tree.
+\end{enumerate} 
 
-\begin{algorithm}[H]
-\caption{Find the Maximum similarity score between two sequences}
-\label{Alg3:genechk}
-\begin{algorithmic} 
-\REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
-\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 $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$
-\IF {$max(Score1, Score2, Score3, Score4)==Score1$}
-	\RETURN $Score1$
-\ELSIF {$max(Score1, Score2, Score3, Score4)==Score2$}
-	\RETURN $Score2$
-\ELSIF {$max(Score1, Score2, Score3, Score4)==Score3$}
-	\RETURN $Score3$
-\ELSIF {$max(Score1, Score2, Score3, Score4)==Score4$}
-	\RETURN $Score4$
-\ENDIF
-\end{algorithmic}
-\end{algorithm}  
+\begin{figure}[H]
+  \centering
+    \includegraphics[width=0.7\textwidth]{Whole_system}
+    \caption{Total overview of the system pipeline}\label{wholesystem}
+\end{figure}
 
-\subsection{Visualizing Relationships}
-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 represent 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 close to each other 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.
+% STOP HERE
 
 \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.
@@ -210,50 +355,43 @@ We implemented four algorithms to extract maximum core genes from large amount o
 
 \subsubsection{Core Genes based on NCBI Annotation}
 The first 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 genes with counts stored in each chloroplast genome, then find the intersection core genes based on gene names. \\
-The pipeline of extracting core genes can summarize in the following steps:\\
-First, we apply the genome annotation method using NCBI annotation tool. Genome quality check can be used in this step to ensure that genomes pass some quality condition. Then, the system lunch annotation process using NCBI to extract code genes (i.e \textit{exons}) and solve gene fragments.  From NCBI, we did not observe any problem with genes fragments, but there are a problem of genes orthography (e.g two different genes sequences with same gene name). After we obtain all annotated genomes from NCBI to the local database, the code will then automatically will generate GenVision\cite{geneVision} file format to lunch the second step to extract coding genes names and counts. The competition will start by building intersection matrix to intersect genomes vectors in the local database with the others. New core vector for two leaf vectors will generate and a specific \textit{CoreId} will assign to it. an evolutionary tree will take place by using all data generated from step 1 and 2. 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. The whole operation illstrate in Figure \ref{NCBI:geneextraction}.
-
-\begin{figure}[H]
-  \centering
-    \includegraphics[width=0.7\textwidth]{NCBI_geneextraction}
-    \caption{Extract core genes based on NCBI gene names and counts}\label{NCBI:geneextraction}
-\end{figure}
-
-\subsubsection{Core Genes based on Dogma Annotation}  
-The main goal is to get as much as possible the core genes of maximum coding genes names. According to NCBI annotation problem based on \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 figure \ref{dog:Annotation}. From this figure, the pipeline of extracting core genes can summarize in the following steps:\\
-First, we apply the genome annotation method using Dogma annotation tool. Genome quality check can be used in this step to ensure that genomes pass some quality condition. Then, the system lunch annotation process using Dogma to extract code genes (i.e \textit{exons}) and solve gene fragments. The key difference here is that dogma can generate in addition transfer RNA and ribosomal RNA. As a result from annotation process with dogma is genomes files in GenVision\cite{geneVision} file format, the code will lunch genes de-fragments process to avoid genes duplications. little problems of genes orthography (e.g two different genes sequences with same gene name) where exists. After we obtain all annotated genomes from dogma, we store it in the local database. The code will then automatically lunch the second step to extract coding genes names and counts. The competition will start by building intersection matrix to intersect genomes vectors in the local database with the others. New core vector for two leaf vectors will generate and a specific \textit{CoreId} will assign to it. an evolutionary tree will take place by using all data generated from step 1 and 2. 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. The whole operation illustrate in Figure \ref{dogma:geneextraction}.
+The pipeline of extracting core genes can summarize in the following steps according to pre-processing method used:\\
 
-\begin{figure}[H]
-  \centering
-    \includegraphics[width=0.7\textwidth]{Dogma_geneextraction}
-    \caption{Extract core genes based on Dogma gene names and counts}\label{dogma:geneextraction}
-\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.
+\begin{enumerate}
+\item We downloads already annotated chloroplast genomes in the form of fasta coding genes (\emph{i.e.} \textit{exons}).
+\item Extract genes names and apply to solve gene duplication using first method. 
+\item Convert fasta file format to geneVision file format to generate ICM. 
+\item Calculate ICM matrix to find maximum core \textit{Score}. New core genes for two genomes will generate and a specific \textit{CoreId} will assign to it. This process continue until no elements remain in the matrix.
+\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}
 
-\subsection{Extract Core Genes based on Genes Sequences}
-We discussed before on the hypothesis of the second method. In this section, we will implement this hypothesis by using ncbi-Blast alignment tool. Implementation of this method is dividing into two parts: \textit{Core genes from NCBI Annotation} and \textit{Core Genes from Dogma Annotation}. For instance, for the two parts, selecting a reference genome can be a key difference among predicting Core genes. After choosing a reference genome, Local blast database will then created to store the rest of Un-annotated chloroplast genomes. \\
+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.
 
-We will present the algorithm in the following steps:
+\subsubsection{Core Genes based on Dogma Annotation}  
+The main goal is to get as much as possible the core genes of maximum coding genes names. According to NCBI annotation problem based on \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 figure \ref{wholesystem}.
 
+extracting core genes based on genes names and counts summarized in the following steps:\\
 \begin{enumerate}
-\item Select a reference genome: we need to select good reference genome from our population, To do so, we can choose \textit{Lycopersicon esculentum cultivar LA3023 chloroplast NC\_007898.3} to be the reference genome if we consider the version of annotation, or \textit{Zea Mays NC\_001666.2} if we consider the largest number of coding genes based on NCBI annotation.The aim is to extract the maximum core genes. In order to achieve this goal, we choose \textit{Zea Mays NC\_001666.2} to be our reference genome.
-\item Build Blast database for the rest of unannotated genomes.
-\item Compare reference Genes: based on the genomes in the database. We querying each reference gene with the database by using \textbf{Blastn}. The result with alignment scores for each gene will store in separated file.
-\item Generate match table: In this table, each row represent referenced genes, while columns represent genomes. To fill this table, a developed code will open each output file for reference genes and extract the number of genomes and a list of genomes names where gene sequence have hits. 
+\item We apply the genome annotation manually using Dogma annotation tool. 
+\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 core genome can be extracted from the table by taking as possible the maximum number of genes that exists in the maximum number of genomes.                
+
+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}
-The main idea from this method is to focus on genes quality to predict maximum core genes. By comparing only genes names or genes sequences from one annotation tool is not enough. The question here, does the predicted gene from NCBI is the same gene predicted by Dogma based on gene name and gene sequence?. If yes, then we can predict new quiality genomes based on quality control test with a specific threshold. Predicted Genomes comes from merging two annotation techniques. While if no, we can not depending neither on NCBI nor Dogma because of annotation error. Core genes can by predicted by using one of the previous methods.
+The main idea from this method is to focus on genes quality to predict maximum core genes. By comparing only genes names from one annotation tool is not enough. The question here, does the predicted gene from NCBI is the same gene predicted by Dogma based on gene name and gene sequence?. If yes, then we can predict new quality genomes based on quality control test with a specific threshold. Predicted Genomes comes from merging two annotation techniques. While if no, we can not depending neither on NCBI nor Dogma because of annotation error. Core genes can by predicted by using one of the 
 
+\subsubsection{Core genes based on NCBI and Dogma Annotation}
 This method summarized in the following steps:\\
 
 \begin{enumerate}
 \item Retrieve the annotation of all genomes from NCBI and Dogma: in this step, we apply the annotation of all chloroplast genomes in the database using NCBI annotation and Dogma annotation tool.
-\item Predict quality genomes: the process is to pick a genome annotation from two techniques, extracting all common genes based on genes names, then applying Needle-man wunch algorithm to align the two sequences based on a specific threshold. If the alignment score pass the threshold, then this gene will removed from the competition and store it in quality genome by saving its name with the largest gene sequence with respect to start and end codons. All quality genomes will store in the form of GenVision file format.    
-\item Extract Core genes: from the above two steps, we will have new genomes with quality genes, ofcourse, we have some genes lost here, because dogma produced tRNA and rRNA genes while NCBI did not generate them and vise-versa. Using first method to extract core genes will be sufficient because we already check their sequences.
+\item Convert NCBI genomes to GeneVision file format, then apply the second method of gene defragmentation methods for NCBI and dogma genomes.  
+\item Predict quality genomes: the process is to pick a genome annotation from two sources, extracting all common genes based on genes names, then applying Needle-man wunch algorithm to align the two sequences based on a threshold equal to 65\%. If the alignment score pass the threshold, then this gene will removed from the competition and store it in quality genome by saving its name with the largest gene sequence with respect to start and end codons. All quality genomes will store in the form of GenVision file format.    
+\item Extract Core genes: from the above two steps, we will have new genomes with quality genes, ofcourse, we have some genes lost here, because dogma produced tRNA and rRNA genes while NCBI did not generate rRNA genes and vise-versa. Build ICM to extract core genes will be sufficient because we already check genes sequences.
 \item Display tree: An evolution tree then will be display based on the intersections of quality genomes.   
 \end{enumerate}
-\pagebreak
\ No newline at end of file
