adding the directory of paper2

[chloroplast13.git] / annotated.tex

diff --git a/annotated.tex b/annotated.tex
index b43666b5d07e6819aec023ab3a8a5b65bfb16c7b..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


@@ -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:


@@ -323,18 +299,14 @@ the distances and finally draw the phylogenetic tree.
 
 \section{Implementation}
 
 
 \section{Implementation}
 

-The different  algorithms have  been implemented using  Python version
-2.7,  on  a  laptop  running Ubuntu~12.04~LTS.   More  precisely,  the
-computer is a Dell Latitude laptop - model E6430 with 6~GiB memory and
+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
 a  quad-core Intel  core~i5~processor with  an operating  frequency of

-2.5~GHz. Many python packages  such as os, Biopython, memory\_profile,
-re,  numpy, time,  shutil, and  xlsxwriter were  used to  extract core
+2.5~GHz. All the programs can be downloaded at \url{http://......} .

 genes  from large  amount of  chloroplast  genomes.
 
 \begin{center}
 genes  from large  amount of  chloroplast  genomes.
 
 \begin{center}

-\begin{table}[b]
-\caption{Type of annotation, execution time, and core genes 
-for each method}\label{Etime}
+\begin{table}[H]
+\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
 {\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


@@ -355,15 +327,15 @@ Gene Quality & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{$\sime
 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,
 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  {\it Annotation} columns
-represent the algorithm used to annotate chloroplast genomes, the {\it
+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
 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} to extract core genes
-from large chloroplast genome.   Only the gene quality method requires
-several days of computation (about 3-4 days) for sequence comparisons,
-once the quality genomes are  construced it takes just 1.29~minutes to
-extract core gene. Thanks to this low execution times we can use these
+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
 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


@@ -373,18 +345,13 @@ 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
 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. {\it Bad
-genomes} gives  the number of genomes  that destroy core  genes due to
-low  number  of  gene  intersection.  \textit{NC\_012568.1  Micromonas
-pusilla} is the only genome which destroyed the core genome with NCBI
+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.
 
 annotations for both gene features and gene quality methods.
 

-The second important factor is the amount of memory being used by each
+The second important factor is the amount of memory nessecary in each

 methodology.   Table   \ref{mem}  shows  the  memory   usage  of  each
 methodology.   Table   \ref{mem}  shows  the  memory   usage  of  each

-method.  We  used  a  package from  PyPI~(\textit{the  Python  Package
-Index})     named     \textit{Memory\_profile}    (located     at~{\tt
-https://pypi.python.org/pypi})   to   extract   all  the   values   in
-table~\ref{mem}. In  this table, the values are  presented in megabyte
+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
 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


@@ -392,14 +359,16 @@ 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
 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 used by  the third method also
+memory. Moreover, the  amount of memory, which is used by the third method also

 depends on the size of each genome.
 
 depends on the size of each genome.
 

-\begin{center}
+

 \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}

+\tabcolsep=0.11cm

 {\scriptsize
 {\scriptsize

-\begin{tabular}{p{2.5cm}p{1.5cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}}
+\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


@@ -411,7 +380,7 @@ Gene Quality  & ~ & 15.3 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\
 \end{tabular}
 }
 \end{table}
 \end{tabular}
 }
 \end{table}

-\end{center}  
+