adding the directory of paper2

[chloroplast13.git] / annotated.tex

diff --git a/annotated.tex b/annotated.tex
index dd57e11dd760cf0fbef6fcfa783cfa26c7678ebb..359b062a2d7797e30bdcd4569bd19d4ce22917b3 100644 (file)
--- a/annotated.tex
+++ b/annotated.tex
@@ -57,33 +57,15 @@ summarizes their distribution in our dataset.
 
 Annotation,  which  is the  first  stage,  is  an important  task  for
 extracting gene features. Indeed, to extract good gene feature, a good
 
 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.
+annotation tool  is obviously  required. 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}
 
 
 \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.
+To obtain  relevant annotated genomes, two annotation  techniques from NCBI and Dogma  are used. 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} 
 


@@ -117,16 +99,13 @@ 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)},
 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.
+verify their start and end  positions. further more, there is no gene duplication with gene annotations from Dogma after applying gene de-fragmentation process. 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 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
+the  gene   duplication  problem  and   therefore  we  have   used  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
 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


@@ -161,12 +140,9 @@ 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
 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.
+given  threshold,  the method is operational when the sequences are not similar. The problem of attribution of a sequence to a gene in the core genome come to light.

 
 

-The second method is based on  the underlying idea: we can predict the
-the best annotated  genome by merging the annotated  genomes from NCBI
+The second method is based on  the underlying idea that it is possible to 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
 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


@@ -266,15 +242,15 @@ core genes with its two genomes parents.
 \begin{algorithmic} 
 \REQUIRE $L \leftarrow \text{genomes sets}$
 \ENSURE $B1 \leftarrow \text{Max Core set}$ 
 \begin{algorithmic} 
 \REQUIRE $L \leftarrow \text{genomes sets}$
 \ENSURE $B1 \leftarrow \text{Max Core set}$ 

-\FOR{$i \leftarrow 0:len(L)-1$}
+\FOR{$i \leftarrow 1:len(L)-1$}

         \STATE $score \leftarrow 0$
        \STATE $core1 \leftarrow set(GenomeList[L[i]])$
        \STATE $g1 \leftarrow L[i]$
        \FOR{$j \leftarrow i+1:len(L)$}
                \STATE $core2 \leftarrow set(GenomeList[L[j]])$
         \STATE $score \leftarrow 0$
        \STATE $core1 \leftarrow set(GenomeList[L[i]])$
        \STATE $g1 \leftarrow L[i]$
        \FOR{$j \leftarrow i+1:len(L)$}
                \STATE $core2 \leftarrow set(GenomeList[L[j]])$

-               \STATE $Core \leftarrow core1 \cap core2$
-               \IF{$len(Core) > score$}
-                  \STATE $score \leftarrow len(Core)$
+               \STATE $core \leftarrow core1 \cap core2$
+               \IF{$len(core) > score$}
+                  \STATE $score \leftarrow len(core)$

                  \STATE $g2 \leftarrow L[j]$
                 \ENDIF
        \ENDFOR
                  \STATE $g2 \leftarrow L[j]$
                 \ENDIF
        \ENDFOR


@@ -286,7 +262,7 @@ core genes with its two genomes parents.
 
 \subsection{Features visualization}
 
 
 \subsection{Features visualization}
 

-The goal is to visualize results  by building a tree of evolution. All
+The goal is to visualize results  by building an evolutionary tree. All

 core  genes generated  represent  an important information  in the  tree,
 because they  provide ancestor information of two  or more
 genomes. Each  node in the  tree represents one chloroplast  genome or
 core  genes generated  represent  an important information  in the  tree,
 because they  provide ancestor information of two  or more
 genomes. Each  node in the  tree represents one chloroplast  genome or


@@ -294,8 +270,8 @@ one predicted core and labelled as \textit{(Genes count:Family name\_Scientific
 names\_Accession number)}. While an edge is labelled with the number of
 lost  genes from  a leaf  genome or  an intermediate  core  gene. Such
 numbers are  very interesting because  they give an  information about
 names\_Accession number)}. While an edge is labelled with the number of
 lost  genes from  a leaf  genome or  an intermediate  core  gene. Such
 numbers are  very interesting because  they give an  information about

-the evolution:  how many genes  were lost between two  species whether
-they  belong  to  the  same  lineage  or not. Phylogenetic relationships are mainly built by comparison of sets of coding and non-coding sequences. Phylogenies of photosynthetic plants are important to assess the origin of chloroplasts (REF) and the modalities of gene loss among lineages. These phylogenies are usually done using less than ten chloroplastic genes (REF), and some of them may not be conserved by evolution process for every taxa. As phylogenetic relationships inferred from data matrices complete for each species included and with the same evolution history are better assumptions, we selected core genomes for a new investivation of photosynthetic plants phylogeny. To depict  the links between
+evolution:  how many genes  were lost between two  species whether
+they  belong  to  the  same  lineage  or not. To depict  the links between

 species   clearly,  we   built   a  phylogenetic   tree  showing   the
 relationships based on the distances among genes sequences. Many tools
 are    available   to    obtain    a   such    tree,   for    example:
 species   clearly,  we   built   a  phylogenetic   tree  showing   the
 relationships based on the distances among genes sequences. Many tools
 are    available   to    obtain    a   such    tree,   for    example:


@@ -311,25 +287,30 @@ The procedure used to built a phylogenetic tree is as follows:
 \item For each gene in a core gene, extract its sequence and store it in the database.
 \item Use multiple alignment tools such as (****to be write after see christophe****) 
 to align these sequences with each others.
 \item For each gene in a core gene, extract its sequence and store it in the database.
 \item Use multiple alignment tools such as (****to be write after see christophe****) 
 to align these sequences with each others.

-\item we use an outer-group genome from cyanobacteria to calculate distances.
-\item Submit the resulting aligned sequences to RAxML program to compute the distances and finally draw the phylogenetic tree.
+\item Use an outer-group genome from cyanobacteria to calculate distances.
+\item Submit the resulting aligned sequences to RAxML program to compute 
+the distances and finally draw the phylogenetic tree.

 \end{enumerate} 
 
 \begin{figure}[H]
 \end{enumerate} 
 
 \begin{figure}[H]

-  \centering \includegraphics[width=0.75\textwidth]{Whole_system}
-  \caption{Overview of the pipeline}\label{wholesystem}
+  \centering \includegraphics[width=0.75\textwidth]{Whole_system} \caption{Overview
+  of the pipeline}\label{wholesystem}

 \end{figure}
 
 \section{Implementation}
 \end{figure}
 
 \section{Implementation}

-We implemented the three algorithms using dell laptop model latitude E6430 with 6 GB of memory, and Intel core i5 processor of 2.5 Ghz$\times 4$ with 3 MB of CPU cash. We built the code using python version 2.7 under ubuntu 12.04 LTS. We also used python packages such as os, Biopython, memory\_profile, re, numpy, time, shutil, and xlsxwriter to extract core genes from large amount of chloroplast genomes. Table \ref{Etime}, show the annotation type, execution time, and the number of core genes for each method:
+
+All the different  algorithms have  been implemented using  Python on a personal computer running Ubuntu~12.04 with 6~GiB memory and
+a  quad-core Intel  core~i5~processor with  an operating  frequency of
+2.5~GHz. All the programs can be downloaded at \url{http://......} .
+genes  from large  amount of  chloroplast  genomes.

 
 \begin{center}
 
 \begin{center}

-\begin{tiny}

 \begin{table}[H]
 \begin{table}[H]

-\caption{Type of Annotation, Execution Time, and core genes for each method}\label{Etime}
-\begin{tabular}{p{2.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.5cm}p{0.2cm}}
+\caption{Type of annotation, execution time, and core genes.}\label{Etime}
+{\scriptsize
+\begin{tabular}{p{2cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.2cm}}

 \hline\hline
 \hline\hline

- & \multicolumn{2}{c}{Annotation} & \multicolumn{2}{c}{Features} & \multicolumn{2}{c}{E. Time} & \multicolumn{2}{c}{C. genes} & \multicolumn{2}{c}{Bad Gen.} \\
+ Method & \multicolumn{2}{c}{Annotation} & \multicolumn{2}{c}{Features} & \multicolumn{2}{c}{Exec. time (min.)} & \multicolumn{2}{c}{Core genes} & \multicolumn{2}{c}{Bad genomes} \\

 ~ & N & D & Name & Seq & N & D & N & D & N & D \\
 \hline
 Gene prediction & $\surd$ & - & - & $\surd$ & 1.7 & - & ? & - & 0 & -\\[0.5ex]
 ~ & N & D & Name & Seq & N & D & N & D & N & D \\
 \hline
 Gene prediction & $\surd$ & - & - & $\surd$ & 1.7 & - & ? & - & 0 & -\\[0.5ex]


@@ -337,33 +318,70 @@ Gene Features & $\surd$ & $\surd$ & $\surd$ & - & 4.98 & 1.52 & 28 & 10 & 1 & 0\
 Gene Quality & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{$\simeq$3 days + 1.29} & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{1}\\[1ex]
 \hline
 \end{tabular}
 Gene Quality & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{$\simeq$3 days + 1.29} & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{1}\\[1ex]
 \hline
 \end{tabular}

+}

 \end{table}
 \end{table}

-\end{tiny}

 \end{center} 
 
 \end{center} 
 

-In table \ref{Etime}, we show that all methods need low execution time to finish extracting core genes from large chloroplast genomes except in gene quality method where we need about 3-4 days for sequence comparisons to construct quality genomes then it takes just 1.29 minute to extract core genes. This low execution time give us a privilage to use these methods to extract core genes on a personal comuters rather than main frames or parallel computers. In the table, \textbf{N} means NCBI, \textbf{D} means DOGMA, and \textbf{Seq} means Sequence. Annotation is represent the type of algorithm used to annotate chloroplast genome. We can see that the two last methods used the same annotation sources. Features means the type of gene feature used to extract core genes, and this is done by extracting gene name, gene sequence, or both of them. The execution time is represented the whole time needed to extract core genes in minutes. We can see in the table that the second method specially with DOGMA annotation has the lowest execution time of 1.52 minute. In last method We needs approxemetly three days (this period is depend on the amount of genomes) to finish the operation of extracting quality genomes only, while the execution time will be 1.29 minute if we have quality genomes. The number of core genes is represents the amount of genes in the last core genome. The main goal is to find the maximum core genes that simulate biological background of chloroplasts. With NCBI we have 28 genes for 96 genomes instead of 10 genes with DOGMA for 97 genomes. But the biological distribution of genomes with NCBI in core tree did not reflect good biological perspective. While in the core tree with DOGMA, the distribution of genomes are biologically good. Bad genomes are the number of genomes that destroy core genes because of the low number of gene intersection. \textit{NC\_012568.1 Micromonas pusilla}, is the only genome that observed to destroy the core genome with NCBI based on the method of gene features and in the third method of gene quality.  \\
+\vspace{-1cm}
+
+Table~\ref{Etime}  presents  for  each  method  the  annotation  type,
+execution time,  and the  number of core  genes. We use  the following
+notations:  \textbf{N}  denotes NCBI,  while  \textbf{D} means  DOGMA,
+and \textbf{Seq}  is for sequence. The first two {\it Annotation} columns
+represent the algorithm used to annotate chloroplast genomes. The next two ones {\it
+Features} columns mean  the kind  of gene feature used to extract core
+genes: gene name, gene sequence, or  both of them. It can be seen that
+almost all methods need low {\it Execution time} expended in minutes to extract core genes
+from the large set of chloroplast genomes. Only the gene quality method requires
+several days of computation (about 3-4 days) for sequence comparisons. However,
+once the quality genomes are well constructed, it only takes 1.29~minutes to
+extract core gene. Thanks to this low execution times that gave us a privilege to use these
+methods to extract core genes  on a personal computer rather than main
+frames or parallel computers. The lowest execution time: 1.52~minutes,
+is obtained with the second method using Dogma annotations. The number
+of {\it  Core genes} represents the  amount of genes in  the last core
+genome. The main goal is to  find the maximum core genes that simulate
+biological background of chloroplasts. With  NCBI we have 28 genes for
+96   genomes,   instead   of    10   genes   for   97   genomes   with
+Dogma. Unfortunately, the biological distribution of genomes with NCBI
+in core tree do not  reflect good biological perspective, whereas with
+DOGMA the  distribution of genomes is biologically  relevant. Some a few genomes maybe destroying core genes due to
+low  number  of  gene  intersection. More precisely, \textit{NC\_012568.1  Micromonas pusilla} is the only genome who destroyes the core genome with NCBI
+annotations for both gene features and gene quality methods.
+
+The second important factor is the amount of memory nessecary in each
+methodology.   Table   \ref{mem}  shows  the  memory   usage  of  each
+method. In this table, the values are  presented in megabyte
+unit and \textit{gV} means  genevision~file~format. We can notice that
+the level  of memory which is  used is relatively low  for all methods
+and is available  on any personal computer. The  different values also
+show that the gene features  method based on Dogma annotations has the
+more   reasonable   memory   usage,   except  when   extracting   core
+sequences. The third method gives the lowest values if we already have
+the   quality   genomes,   otherwise   it  will   consume   far   more
+memory. Moreover, the  amount of memory, which is used by the third method also
+depends on the size of each genome.

 
 

-The second important factor is the amount of memory usage in each methodology. Table \ref{mem} show the amounts of memory consumption by each method.

 
 

-\begin{center}
-\begin{tiny}

 \begin{table}[H]
 \begin{table}[H]

+\centering

 \caption{Memory usages in (MB) for each methodology}\label{mem}
 \caption{Memory usages in (MB) for each methodology}\label{mem}

-\begin{tabular}{p{2.5cm}p{1.5cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}}
+\tabcolsep=0.11cm
+{\scriptsize
+\begin{tabular}{p{2.5cm}@{\hskip 0.1mm}p{1.5cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}}

 \hline\hline
 Method& & Load Gen. & Conv. gV & Read gV & ICM & Core tree & Core Seq. \\
 \hline
 \hline\hline
 Method& & Load Gen. & Conv. gV & Read gV & ICM & Core tree & Core Seq. \\
 \hline

-Gene prediction & NCBI & 100 & - & - & - & 108 & -\\
+Gene prediction & NCBI & 108 & - & - & - & - & -\\

 \multirow{2}{*}{Gene Features} & NCBI & 15.4 & 18.9 & 17.5 & 18 & 18 & 28.1\\
               & DOGMA& 15.3 & 15.3 & 16.8 & 17.8 & 17.9 & 31.2\\
 Gene Quality  & ~ & 15.3 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\ 
 \hline
 \end{tabular}
 \multirow{2}{*}{Gene Features} & NCBI & 15.4 & 18.9 & 17.5 & 18 & 18 & 28.1\\
               & DOGMA& 15.3 & 15.3 & 16.8 & 17.8 & 17.9 & 31.2\\
 Gene Quality  & ~ & 15.3 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\ 
 \hline
 \end{tabular}

+}

 \end{table}
 \end{table}

-\end{tiny}
-\end{center}  
+ 

 
 

-We used a package from PyPI~(\textit{the Python Package Index}) where located at~ (https://pypi.python.org/pypi) named \textit{Memory\_profile} to extract all the values in table \ref{mem}. In this table, all the values are presented in mega bytes and \textit{gV} means genevision file format. We see that all memory levels in all methods are reletively low and can be available in any personal computer. All memory values shows that the method of gene features based on DOGMA annotation have the more resonable memory values to extract core genome from loading genomes until extracting core sequences. The third method, gives us the lowest values if we already have the quality genomes, but it will consume high memory locations if we do not have them. Also, the amount of memory locations in the third method vary according to the size of each genome.\\