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
\STATE $geneList=\text{empty list}$
\STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
\FOR{$\text{gene in common}$}
- \STATE $gen1 \leftarrow open(NCBI\_Genes(gene)).read()$
- \STATE $gen2 \leftarrow open(Dogma\_Genes(gene)).read()$
- \STATE $score \leftarrow geneChk(gen1,gen2)$
+ \STATE $g1 \leftarrow open(NCBI\_Genes(gene)).read()$
+ \STATE $g2 \leftarrow open(Dogma\_Genes(gene)).read()$
+ \STATE $score \leftarrow geneChk(g1,g2)$
\IF {$score > Threshold$}
\STATE $geneList \leftarrow gene$
\ENDIF
geneChk subroutine.
\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 $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
+\REQUIRE $g1,g2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
\ENSURE $\text{Maximum similarity score}$
-\STATE $Score1 \leftarrow needle(gen1,gen2)$
-\STATE $Score2 \leftarrow needle(gen1,Reverse(gen2))$
-\STATE $Score3 \leftarrow needle(gen1,Complement(gen2))$
-\STATE $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$
-\RETURN $max(Score1, Score2, Score3, Score4)$
+\STATE $score1 \leftarrow needle(g1,g2)$
+\STATE $score2 \leftarrow needle(g1,Reverse(g2))$
+\STATE $score3 \leftarrow needle(g1,Complement(g2))$
+\STATE $score4 \leftarrow needle(g1,Reverse(Complement(g2)))$
+\RETURN $max(score1,score2,score3,score4)$
\end{algorithmic}
\end{algorithm}
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:
-
+the maximum score. Mathematically speaking, if we have $n$ genomes in
+local database, the ICM is an $n \times n$ matrix whose elements
+satisfy:
\begin{equation}
-Score=\max_{i<j}\vert x_i \cap x_j\vert
+score_{ij}=\vert g_i \cap g_j\vert
\label{Eq1}
\end{equation}
-
-% 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}:
-$$
-\text{New Core} =
-\begin{cases}
-\text{Ignored} & \text{if $\textit{Score}=0$;} \\
-\text{new Core id} & \text{if $\textit{Score}>0$.}
-\end{cases}
-$$
-
-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.
+\noindent where $1 \leq i \leq n$, $1 \leq j \leq n$, and $g_i, g_j$ are
+genomes. The generation of a new core gene depends obviously on the
+value of the intersection scores $score_{ij}$. More precisely, the
+idea is to consider a pair of genomes such that their score is the
+largest element in ICM. These two genomes are then removed from matrix
+and the resulting new core genome is added for the next iteration.
+The ICM is then updated to take into account the new core gene: new IS
+values are computed for it. This process is repeated until no new core
+gene can be obtained.
+
+We can observe that the ICM is very large due to the amount of
+data. As a consequence, the computation of the intersection scores is
+both time and memory consuming. However, since ICM is a symetric
+matrix we can reduce the computation overhead by considering only its
+triangular upper part. 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
+storing all genomes data. At each iteration, it computes the maximum
+core genes with its two genomes parents.
+
+% ALGORITHM HAS BEEN REWRITTEN
\begin{algorithm}[H]
\caption{Extract Maximum Intersection Score}
\label{Alg1:ICM}
\begin{algorithmic}
-\REQUIRE $L \leftarrow \text{genomes vectors}$
-\ENSURE $B1 \leftarrow Max core vector$
-\FOR{$i \leftarrow 0:len(L)-1$}
+\REQUIRE $L \leftarrow \text{genomes sets}$
+\ENSURE $B1 \leftarrow \text{Max Core set}$
+\FOR{$i \leftarrow 1:len(L)-1$}
+ \STATE $score \leftarrow 0$
\STATE $core1 \leftarrow set(GenomeList[L[i]])$
- \STATE $score1 \leftarrow 0$
- \STATE $g1,g2 \leftarrow$ " "
+ \STATE $g1 \leftarrow L[i]$
\FOR{$j \leftarrow i+1:len(L)$}
- \STATE $core2 \leftarrow set(GenomeList[L[i]])$
- \IF{$i < j$}
- \STATE $Core \leftarrow core1 \cap core2$
- \IF{$len(Core) > score1$}
- \STATE $g1 \leftarrow L[i]$
- \STATE $g2 \leftarrow L[j]$
- \STATE $Score \leftarrow len(Core)$
- \ELSIF{$len(Core) == 0$}
- \STATE $g1 \leftarrow L[i]$
- \STATE $g2 \leftarrow L[j]$
- \STATE $Score \leftarrow -1$
- \ENDIF
- \ENDIF
+ \STATE $core2 \leftarrow set(GenomeList[L[j]])$
+ \STATE $core \leftarrow core1 \cap core2$
+ \IF{$len(core) > score$}
+ \STATE $score \leftarrow len(core)$
+ \STATE $g2 \leftarrow L[j]$
+ \ENDIF
\ENDFOR
- \STATE $B1[score1] \leftarrow (g1,g2)$
+ \STATE $B1[score] \leftarrow (g1,g2)$
\ENDFOR
\RETURN $max(B1)$
\end{algorithmic}
\end{algorithm}
-\subsection{Features Visualization}
+\subsection{Features visualization}
-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
+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
-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:
+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
+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:
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:
+RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml}, BioNJ, and
+TNT\cite{goloboff2008tnt}}. In this work, we chose to use
+RAxML\cite{stamatakis2008raxml,stamatakis2005raxml} because it is
+fast, accurate, and can build large trees when dealing with a large
+number of genomic sequences.
+The procedure used to built a phylogenetic tree is as follows:
\begin{enumerate}
-\item Extract gene sequence for all gene in all core genes, store it in database.
-\item Use multiple alignment tool such as (****to be write after see christophe****) to align these sequences with each others.
-\item aligned genomes sequences then submitted to RAxML program to compute the distances and draw phylogenetic tree.
+\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 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=0.7\textwidth]{Whole_system}
- \caption{Total overview of the system pipeline}\label{wholesystem}
+ \centering \includegraphics[width=0.75\textwidth]{Whole_system} \caption{Overview
+ of the 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.
-\subsection{Extract Core Genes based on Gene Contents}
+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{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
+ 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]
+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{center}
+
+\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.
+
+
+\begin{table}[H]
+\centering
+\caption{Memory usages in (MB) for each methodology}\label{mem}
+\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
+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}
+}
+\end{table}
+
-\subsubsection{Core Genes based on NCBI Annotation}
-The first idea to construct the core genome is based on the extraction of Genes names (as gene presence or absence). For instant, in this stage neither sequence comparison nor new annotation were made, we just want to extract all genes with counts stored in each chloroplast genome, then find the intersection core genes based on gene names. \\
-The pipeline of extracting core genes can summarize in the following steps according to pre-processing method used:\\
-\begin{enumerate}
-\item We downloads already annotated chloroplast genomes in the form of fasta coding genes (\emph{i.e.} \textit{exons}).
-\item Extract genes names and apply to solve gene duplication using first method.
-\item Convert fasta file format to geneVision file format to generate ICM.
-\item Calculate ICM matrix to find maximum core \textit{Score}. New core genes for two genomes will generate and a specific \textit{CoreId} will assign to it. This process continue until no elements remain in the matrix.
-\item Evolutionary tree will take place by using all data generated from step 1 and 4. The tree will also display the amount of genes lost from each intersection iteration. A specific excel file will be generated that store all the data in local database.
-\end{enumerate}
-There main drawback with this method is genes orthography (e.g two different genes sequences with same gene name). In this case, Gene lost is considered by solving gene duplication based on first method to solve gene duplication.
-\subsubsection{Core Genes based on Dogma Annotation}
-The main goal is to get as much as possible the core genes of maximum coding genes names. According to NCBI annotation problem based on \cite{Bakke2009}, annotation method like dogma can give us more reliable coding genes than NCBI. This is because NCBI annotation can carry some annotation and gene identification errors. The general overview of whole process of extraction illustrated in figure \ref{wholesystem}.
-extracting core genes based on genes names and counts summarized in the following steps:\\
-\begin{enumerate}
-\item We apply the genome annotation manually using Dogma annotation tool.
-\item Analysing genomes to store lists of code genes names (\textit{i.e. exons}). solve gene fragments is done by using first method in solve gene fragments. The output from annotation process with dogma is genomes files in GenVision file format. Sets of genes were stored in the database.
-\item Generate ICM matrix to calculate maximum core genes.
-\item Draw the evolutionary tree by extracted all genes sequences from each core. Then applying multiple alignment process on the sequences to calculate the distance among cores to draw a phylogenetic tree.
-
-\end{enumerate}
-
-
-The main drawback from the method of extracting core genes based on gene names and counts is that we can not depending only on genes names because of three causes: first, the genome may have not totally named (This can be found in early versions of NCBI genomes), so we will have some lost sequences. Second, we may have two genes sharing the same name, while their sequences are different. Third, we need to annotate all the genomes.
-
-\subsection{Extract Core Genes based on Gene Quality Control}
-The main idea from this method is to focus on genes quality to predict maximum core genes. By comparing only genes names from one annotation tool is not enough. The question here, does the predicted gene from NCBI is the same gene predicted by Dogma based on gene name and gene sequence?. If yes, then we can predict new quality genomes based on quality control test with a specific threshold. Predicted Genomes comes from merging two annotation techniques. While if no, we can not depending neither on NCBI nor Dogma because of annotation error. Core genes can by predicted by using one of the
-
-\subsubsection{Core genes based on NCBI and Dogma Annotation}
-This method summarized in the following steps:\\
-
-\begin{enumerate}
-\item Retrieve the annotation of all genomes from NCBI and Dogma: in this step, we apply the annotation of all chloroplast genomes in the database using NCBI annotation and Dogma annotation tool.
-\item Convert NCBI genomes to GeneVision file format, then apply the second method of gene defragmentation methods for NCBI and dogma genomes.
-\item Predict quality genomes: the process is to pick a genome annotation from two sources, extracting all common genes based on genes names, then applying Needle-man wunch algorithm to align the two sequences based on a threshold equal to 65\%. If the alignment score pass the threshold, then this gene will removed from the competition and store it in quality genome by saving its name with the largest gene sequence with respect to start and end codons. All quality genomes will store in the form of GenVision file format.
-\item Extract Core genes: from the above two steps, we will have new genomes with quality genes, ofcourse, we have some genes lost here, because dogma produced tRNA and rRNA genes while NCBI did not generate rRNA genes and vise-versa. Build ICM to extract core genes will be sufficient because we already check genes sequences.
-\item Display tree: An evolution tree then will be display based on the intersections of quality genomes.
-\end{enumerate}
