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
\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 $Core \leftarrow core1 \cap core2$
- \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
\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 family or not. By the principle of
-classification, a small number of genes lost among species indicates
-that those species are close to each other and belong to same family,
-while a large lost means that we have an evolutionary relationship
-between species from different families. 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:
\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]
- \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}
-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{tiny}
\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
- & \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 & $\surd$ & - & - & $\surd$ & ? & - & ? & - & 0 & -\\[0.5ex]
+Gene prediction & $\surd$ & - & - & $\surd$ & 1.7 & - & ? & - & 0 & -\\[0.5ex]
Gene Features & $\surd$ & $\surd$ & $\surd$ & - & 4.98 & 1.52 & 28 & 10 & 1 & 0\\[0.5ex]
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{tiny}
\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]
+\centering
\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 & Gen. tree & Core Seq. \\
+Method& & Load Gen. & Conv. gV & Read gV & ICM & Core tree & Core Seq. \\
\hline
-Gene prediction & ~ & ~ & ~ & ~ & ~ & ~ & ~\\
+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$2.7G & 16.1 & 17 & 17.1 & 24.4\\
+Gene Quality & ~ & 15.3 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\
\hline
\end{tabular}
+}
\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.\\
