Further modifications en section 3

diff --git a/annotated.tex b/annotated.tex
index 9810db82204ceb90017929cefef1ae44667bef72..9d98987f93da1dcd098484538e1d31921123625b 100644 (file)
--- a/annotated.tex
+++ b/annotated.tex
@@ -76,32 +76,111 @@ process.
 
 \input{population_Table}
        
 
 \input{population_Table}
        

-% MICHEL : TO BE CONTINUED FROM HERE
+\subsection{Genome annotation techniques}

 
 

-\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.   
+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} 
 
 \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.\\
+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.

 
 

-\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{Genome annotation from Dogma}

 
 

-\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}.         
+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]
 
 \begin{algorithm}[H]

-\caption{Extract new genome based on Gene Quality test}
+\caption{Extract new genome based on gene quality test}

 \label{Alg3:thirdM}
 \begin{algorithmic} 
 \REQUIRE $Gname \leftarrow \text{Genome Name}, Threshold \leftarrow 65$
 \label{Alg3:thirdM}
 \begin{algorithmic} 
 \REQUIRE $Gname \leftarrow \text{Genome Name}, Threshold \leftarrow 65$


@@ -111,9 +190,9 @@ The second pre-processing method states: we can predict the best annotated genom
 \STATE $geneList=\text{empty list}$
 \STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
 \FOR{$\text{gene in common}$}
 \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 $score \leftarrow geneChk(g1,g2)$
+       \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 
        \IF {$score > Threshold$}
                \STATE $geneList \leftarrow gene$
        \ENDIF 


@@ -122,10 +201,13 @@ The second pre-processing method states: we can predict the best annotated genom
 \end{algorithmic}
 \end{algorithm}
 
 \end{algorithmic}
 \end{algorithm}
 

-\textbf{geneChk} is a subroutine, 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}.
+\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]
 
 \begin{algorithm}[H]

-\caption{Find the Maximum similarity score between two sequences}
+\caption{Find the maximum similarity score between two sequences}

 \label{Alg3:genechk}
 \begin{algorithmic} 
 \REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
 \label{Alg3:genechk}
 \begin{algorithmic} 
 \REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$


@@ -140,22 +222,53 @@ The second pre-processing method states: we can predict the best annotated genom
 
 \subsubsection{Intersection Core Matrix (\textit{ICM})}
 
 
 \subsubsection{Intersection Core Matrix (\textit{ICM})}
 

-The idea behind extracting core genes is to iteratively collect the maximum number of common genes between two genomes. To do so, the system builds an \textit{Intersection Core Matrix (ICM)}. ICM is a two dimensional symmetric matrix where each row and each column represents one genome. Each position in ICM stores the \textit{Intersection Scores(IS)}. IS is the cardinality number of a core genes which comes from intersecting one genome with other ones. Maximum cardinality results to select two genomes with their maximum core. Mathematically speaking, if we have an $n \times n$ matrix where $n$  
-is the number of genomes 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}
 
 
 \begin{equation}
 Score=\max_{i<j}\vert x_i \cap x_j\vert
 \label{Eq1}
 \end{equation}
 

-\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}:
-$$\text{New Core} = \begin{cases} 
+% TO BE CONTINUED
+
+\noindent where $x_i, x_j$ are elements in the matrix. The generation of 
+a new core genes is depending on the cardinality value of intersection 
+scores, we call it \textit{Score}:
+$$
+\text{New Core} = 
+\begin{cases}

 \text{Ignored} & \text{if $\textit{Score}=0$;} \\
 \text{new Core id} & \text{if $\textit{Score}>0$.}
 \text{Ignored} & \text{if $\textit{Score}=0$;} \\
 \text{new Core id} & \text{if $\textit{Score}>0$.}

-\end{cases}$$
-
-if $\textit{Score}=0$ then we have \textit{disjoint relation} \emph{i.e.}, no common genes between two genomes. In this case the system ignores the genome that annul the core gene size. Otherwise, The system removes these two genomes from ICM and add new core genome with a \textit{coreID} of them to ICM for the calculation in next iteration. This process reduces the size of ICM and repeats until all genomes are treated \emph{i.e.} ICM has no more genomes.   
-We observe that ICM is very large because of the amount of data that it stores. This results to be time and memory consuming for calculating the intersection scores. To increase the speed of calculations, it is sufficient to only calculate the upper triangle scores. The time complexity for this process after enhancement is thus $O(\frac{n.(n-1)}{2})$. Algorithm \ref{Alg1:ICM} illustrates the construction of the ICM matrix and the extraction of the core genes where \textit{GenomeList}, represents the database where all genomes data are stored. At each iteration, it computes the maximum core genes with its two genomes parents.
+\end{cases}
+$$
+
+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}
 
 \begin{algorithm}[H]
 \caption{Extract Maximum Intersection Score}


@@ -189,7 +302,37 @@ We observe that ICM is very large because of the amount of data that it stores.
 \end{algorithm}
 
 \subsection{Features Visualization}
 \end{algorithm}
 
 \subsection{Features Visualization}

-The goal here is to visualize results by building a tree of evolution. All core genes generated with their genes are very important information in the tree, because they can be viewed as an ancestor information for two genomes or more. Further more, each node in the tree represents one chloroplast genome or one predicted core which named under the title of \textit{(Genes count:Family name\_Scientific names\_Accession number)}, Edges represent the number of lost genes from each leaf genome or from an intermediate core genes. 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 consider to 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:
+
+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{enumerate}
 \item Extract gene sequence for all gene in all core genes, store it in database.
 
 \begin{enumerate}
 \item Extract gene sequence for all gene in all core genes, store it in database.


@@ -203,6 +346,8 @@ The goal here is to visualize results by building a tree of evolution. All core
     \caption{Total overview of the system pipeline}\label{wholesystem}
 \end{figure}
 
     \caption{Total overview of the system pipeline}\label{wholesystem}
 \end{figure}
 

+% 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.
 
 \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.
 

diff --git a/main.tex b/main.tex
index ae1e9d7e5d6c7d864e72e17fc2500e460d12d14a..bf9d20463f6cfa1954b1d8a99df14c28891bc932 100755 (executable)
--- a/main.tex
+++ b/main.tex
@@ -55,7 +55,6 @@ University of Franche-Comt\'{e}, France \\
 
 \section{Conclusion}\label{sec:concl}
 
 
 \section{Conclusion}\label{sec:concl}
 

-

 \bibliographystyle{plain}
 
 \bibliography{biblio}
 \bibliographystyle{plain}
 
 \bibliography{biblio}