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}
-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}
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 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
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
\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
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:
\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
-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}
-\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
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
-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
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.
-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
-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
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.
-\begin{center}
+
\begin{table}[H]
+\centering
\caption{Memory usages in (MB) for each methodology}\label{mem}
+\tabcolsep=0.11cm
{\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
\end{tabular}
}
\end{table}
-\end{center}
+
