2 These last years the cost of sequencing genomes has been greatly
3 reduced, and thus more and more genomes are sequenced. Therefore
4 automatic annotation tools are required to deal with this continuously
5 increasing amount of genomical data. Moreover, a reliable and accurate
6 genome annotation process is needed in order to provide strong
7 indicators for the study of life\cite{Eisen2007}.
9 Various annotation tools (\emph{i.e.}, cost-effective sequencing
10 methods\cite{Bakke2009}) producing genomic annotations at many levels
11 of detail have been designed by different annotation centers. Among
12 the major annotation centers we can notice NCBI\cite{Sayers01012011},
13 Dogma \cite{RDogma}, cpBase \cite{de2002comparative},
14 CpGAVAS \cite{liu2012cpgavas}, and
15 CEGMA\cite{parra2007cegma}. Usually, previous studies used one out of
16 three methods for finding genes in annoted genomes using data from
17 these centers: \textit{alignment-based}, \textit{composition based},
18 or a combination of both~\cite{parra2007cegma}. The alignment-based
19 method is used when trying to predict a coding gene (\emph{i.e.}.
20 genes that produce proteins) by aligning a genomic DNA sequence with a
21 cDNA sequence coding an homologous protein \cite{parra2007cegma}.
22 This approach is also used in GeneWise\cite{birney2004genewise}. The
23 alternative method, the composition-based one (also known
24 as \textit{ab initio}) is based on a probabilistic model of gene
25 structure to find genes according to the gene value probability
26 (GeneID \cite{parra2000geneid}). Such annotated genomic data will be
27 used to overcome the limitation of the first method described in the
28 previous section. In fact, the second method we propose finds core
29 genes from large amount of chloroplast genomes through genomic
32 Figure~\ref{Fig1} presents an overview of the entire method pipeline.
33 More precisely, the second method consists of three
34 stages: \textit{Genome annotation}, \textit{Core extraction},
35 and \textit{Features Visualization} which highlights the
36 relationships. To understand the whole core extraction process, we
37 describe briefly each stage below. More details will be given in the
38 coming subsections. The method uses as starting point some sequence
39 database chosen among the many international databases storing
40 nucleotide sequences, like the GenBank at NBCI \cite{Sayers01012011},
41 the \textit{EMBL-Bank} \cite{apweiler1985swiss} in Europe
42 or \textit{DDBJ} \cite{sugawara2008ddbj} in Japan. Different
43 biological tools can analyze and annotate genomes by interacting with
44 these databases to align and extract sequences to predict genes. The
45 database in our method must be taken from any confident data source
46 that stores annotated and/or unannotated chloroplast genomes. We have
47 considered the GenBank-NCBI \cite{Sayers01012011} database as sequence
48 database: 99~genomes of chloroplasts were retrieved. These genomes
49 lie in the eleven type of chloroplast families and Table \ref{Tab2}
50 summarizes their distribution in our dataset.
54 \includegraphics[width=0.75\textwidth]{generalView}
55 \caption{A general overview of the annotation-based approach}\label{Fig1}
58 Annotation, which is the first stage, is an important task for
59 extracting gene features. Indeed, to extract good gene feature, a good
60 annotation tool is obviously required. To obtain relevant annotated
61 genomes, two annotation techniques from NCBI and Dogma are used. The
62 extraction of gene feature, the next stage, can be anything like gene
63 names, gene sequences, protein sequences, and so on. Our method
64 considers gene names, gene counts, and gene sequence for extracting
65 core genes and producing chloroplast evolutionary tree. The final
66 stage allows to visualize genomes and/or gene evolution in
67 chloroplast. Therefore we use representations like tables,
68 phylogenetic trees, graphs, etc. to organize and show genomes
69 relationships, and thus achieve the goal of representing gene
70 evolution. In addition, comparing these representations with ones
71 issued from another annotation tool dedicated to large population of
72 chloroplast genomes give us biological perspectives to the nature of
73 chloroplasts evolution. Notice that a local database linked with each
74 pipe stage is used to store all the informations produced during the
77 \input{population_Table}
79 % MICHEL : TO BE CONTINUED FROM HERE
81 \subsection{Genome Annotation Techniques}
82 Genome annotation is the second stage in the model pipeline. Many techniques were developed to annotate chloroplast genomes but the problem is that they vary in the number and type of predicted genes (\emph{i.e.} the ability to predict genes and \textit{for example: Transfer RNA (tRNA)} and \textit{Ribosomal RNA (rRNA)} genes). Two annotation techniques from NCBI and Dogma are considered to analyse chloroplast genomes to examine the accuracy of predicted coding genes.
84 \subsubsection{Genome annotation from NCBI}
85 The objective from this step is to organize genes, solve gene duplications, and generate sets of genes from each genome. The input to the system is a list of chloroplast genomes, annotated from NCBI. All genomes stored as \textit{.fasta} files which have a collection of protein coding genes\cite{parra2007cegma,RDogma} (gene that produce proteins) with its coding sequences.
86 As a preprocessing step to build the set of core genes, we need to analyse these genomes (using \textit{BioPython} package\cite{chapman2000biopython}). The process starts by converting each genome from fasta format to GenVision\cite{geneVision} format from DNASTAR. The outputs from this operation are lists of genes for each genome, their gene names and gene counts. In this stage, we accumulate some gene duplications for each treated genome. These gene name duplication can come from gene fragments, (e.g. gene fragments treated with NCBI), and from chloroplast DNA sequences. To ensure that all the duplications are removed, each list of genes is translated into a set of genes. NCBI genome annotation produce genes except \textit{Ribosomal rRNA}.
88 \subsubsection{Genome annotation from Dogma}
89 Dogma is an annotation tool developed in the university of Texas in 2004. Dogma is an abbreviation of (\textit{Dual Organellar GenoMe Annotator}) for plant chloroplast and animal mitochondrial genomes.
90 It has its own database for translating the genome in all six reading frames and it queries the amino acid sequence database using Blast\cite{altschul1990basic}(\emph{i.e.} Blastx) with various parameters. Furthermore, identify protein coding genes in the input genome based on sequence similarity of genes in Dogma database. In addition, it can produce the \textit{Transfer RNAs (tRNA)}, and the \textit{Ribosomal RNAs (rRNA)} and verifies their start and end positions rather than NCBI annotation tool. There is no gene duplication with dogma after solving gene fragmentation. \\
91 Genome annotation with dogma can be the key difference of extracting core genes. The step of annotation is divided into two tasks: first, It starts to annotate complete chloroplast genomes (\emph{i.e.} \textit{Unannotate genome from NCBI} by using Dogma web tool. This process is done manually. The output from dogma is considered to be a collection of coding genes files for each genome in the form of GeneVision file format.
92 The second task is to solve gene fragments. Two methods are used to solve gene duplication. First, for the method based on gene name, all the duplications are removed, where each list of genes is translated into a set of genes. Second, for the method of gene quality test, a defragment process used to avoid gene duplication. \\
93 In each iteration, this process starts by taking one gene from gene list, searches for gene duplication, if exists, it looks on the orientation of the fragment sequence: if it is positive, then it appends fragment sequence to a gene files. Otherwise, the process applies reverse complement operations on gene sequences and appends it to gene files. An additional process is then applied to check start and stop codons in case of missing. All genomes after this stage are fully annotated, their genes are de-fragmented, and counts are identified.\\
95 \subsection{Core Genes Extraction}
96 The goal of this step is to extract maximum core genes from sets of genes. The methodology of finding core genes is as follow: \\
98 \subsubsection{Pre-Processing}
99 We apply two pre-processing methods to organize and prepare genomes data: the first method based on gene name and count, and the second one is based on sequence quality control test.\\
100 In the first method, we extract a list of genes from each chloroplast genome. Then we store this list of genes in the database under genome name. Genes counts can be extracted by a specific length command. \textit{Intersection Core Matrix} then applied to extract the core genes. The problem with this method is how can we ensure that the gene which is predicted in core genes is the same gene in leaf genomes? The answer of this question is as follows: if the sequence of any gene in a genome annotated from dogma and NCBI are similar with respect to a threshold, we do not have any problem with this method. Otherwise, we have a problem, because we can not decide which sequence goes to a gene in core genes.
101 The second pre-processing method states: we can predict the best annotated genome by merging the annotated genomes from NCBI and dogma if we follow the quality of genes names and sequences test. To generate all quality genes of each genome, the hypothesis state: any gene will be in predicted genome if and only if the annotated genes between NCBI and Dogma pass a specific threshold of \textit{quality control test}. To accept the quality test, we applied Needle-man Wunch algorithm to compare two gene sequences with respect to a threshold. If the alignment score passes the threshold, then the gene will be in the predicted genome. Otherwise, the gene is ignored. After predicting all genomes, \textit{Intersection Core Matrix} is applied on these new genomes to extract core genes, as shown in Algorithm \ref{Alg3:thirdM}.
104 \caption{Extract new genome based on Gene Quality test}
107 \REQUIRE $Gname \leftarrow \text{Genome Name}, Threshold \leftarrow 65$
108 \ENSURE $geneList \leftarrow \text{Quality genes}$
109 \STATE $dir(NCBI\_Genes) \leftarrow \text{NCBI genes of Gname}$
110 \STATE $dir(Dogma\_Genes) \leftarrow \text{Dogma genes of Gname}$
111 \STATE $geneList=\text{empty list}$
112 \STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
113 \FOR{$\text{gene in common}$}
114 \STATE $g1 \leftarrow open(NCBI\_Genes(gene)).read()$
115 \STATE $g2 \leftarrow open(Dogma\_Genes(gene)).read()$
116 \STATE $score \leftarrow geneChk(g1,g2)$
117 \IF {$score > Threshold$}
118 \STATE $geneList \leftarrow gene$
125 \textbf{geneChk} is a subroutine, it is used to find the best similarity score between two gene sequences after applying operations like \textit{reverse, complement, and reverse complement}. The algorithm of geneChk is illustrated in Algorithm \ref{Alg3:genechk}.
128 \caption{Find the Maximum similarity score between two sequences}
131 \REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
132 \ENSURE $\text{Maximum similarity score}$
133 \STATE $Score1 \leftarrow needle(gen1,gen2)$
134 \STATE $Score2 \leftarrow needle(gen1,Reverse(gen2))$
135 \STATE $Score3 \leftarrow needle(gen1,Complement(gen2))$
136 \STATE $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$
137 \RETURN $max(Score1, Score2, Score3, Score4)$
141 \subsubsection{Intersection Core Matrix (\textit{ICM})}
143 The idea behind extracting core genes is to iteratively collect the maximum number of common genes between two genomes. To do so, the system builds an \textit{Intersection Core Matrix (ICM)}. ICM is a two dimensional symmetric matrix where each row and each column represents one genome. Each position in ICM stores the \textit{Intersection Scores(IS)}. IS is the cardinality number of a core genes which comes from intersecting one genome with other ones. Maximum cardinality results to select two genomes with their maximum core. Mathematically speaking, if we have an $n \times n$ matrix where $n$
144 is the number of genomes in local database, then lets consider:\\
147 Score=\max_{i<j}\vert x_i \cap x_j\vert
151 \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}:
152 $$\text{New Core} = \begin{cases}
153 \text{Ignored} & \text{if $\textit{Score}=0$;} \\
154 \text{new Core id} & \text{if $\textit{Score}>0$.}
157 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.
158 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.
161 \caption{Extract Maximum Intersection Score}
164 \REQUIRE $L \leftarrow \text{genomes vectors}$
165 \ENSURE $B1 \leftarrow Max core vector$
166 \FOR{$i \leftarrow 0:len(L)-1$}
167 \STATE $core1 \leftarrow set(GenomeList[L[i]])$
168 \STATE $score1 \leftarrow 0$
169 \STATE $g1,g2 \leftarrow$ " "
170 \FOR{$j \leftarrow i+1:len(L)$}
171 \STATE $core2 \leftarrow set(GenomeList[L[i]])$
173 \STATE $Core \leftarrow core1 \cap core2$
174 \IF{$len(Core) > score1$}
175 \STATE $g1 \leftarrow L[i]$
176 \STATE $g2 \leftarrow L[j]$
177 \STATE $Score \leftarrow len(Core)$
178 \ELSIF{$len(Core) == 0$}
179 \STATE $g1 \leftarrow L[i]$
180 \STATE $g2 \leftarrow L[j]$
181 \STATE $Score \leftarrow -1$
185 \STATE $B1[score1] \leftarrow (g1,g2)$
191 \subsection{Features Visualization}
192 The goal here is to visualize results by building a tree of evolution. All core genes generated with their genes are very important information in the tree, because they can be viewed as an ancestor information for two genomes or more. Further more, each node in the tree represents one chloroplast genome or one predicted core which named under the title of \textit{(Genes count:Family name\_Scientific names\_Accession number)}, Edges represent the number of lost genes from each leaf genome or from an intermediate core genes. 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: PHYML\cite{guindon2005phyml}, RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml}, BioNJ , and TNT\cite{goloboff2008tnt}}. We consider to 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:
195 \item Extract gene sequence for all gene in all core genes, store it in database.
196 \item Use multiple alignment tool such as (****to be write after see christophe****) to align these sequences with each others.
197 \item aligned genomes sequences then submitted to RAxML program to compute the distances and draw phylogenetic tree.
202 \includegraphics[width=0.7\textwidth]{Whole_system}
203 \caption{Total overview of the system pipeline}\label{wholesystem}
206 \section{Implementation}
207 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.
209 \subsection{Extract Core Genes based on Gene Contents}
211 \subsubsection{Core Genes based on NCBI Annotation}
212 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. \\
213 The pipeline of extracting core genes can summarize in the following steps according to pre-processing method used:\\
216 \item We downloads already annotated chloroplast genomes in the form of fasta coding genes (\emph{i.e.} \textit{exons}).
217 \item Extract genes names and apply to solve gene duplication using first method.
218 \item Convert fasta file format to geneVision file format to generate ICM.
219 \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.
220 \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.
223 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.
225 \subsubsection{Core Genes based on Dogma Annotation}
226 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}.
228 extracting core genes based on genes names and counts summarized in the following steps:\\
230 \item We apply the genome annotation manually using Dogma annotation tool.
231 \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.
232 \item Generate ICM matrix to calculate maximum core genes.
233 \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.
238 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.
240 \subsection{Extract Core Genes based on Gene Quality Control}
241 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
243 \subsubsection{Core genes based on NCBI and Dogma Annotation}
244 This method summarized in the following steps:\\
247 \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.
248 \item Convert NCBI genomes to GeneVision file format, then apply the second method of gene defragmentation methods for NCBI and dogma genomes.
249 \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.
250 \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.
251 \item Display tree: An evolution tree then will be display based on the intersections of quality genomes.
