X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/chloroplast13.git/blobdiff_plain/fb06fc26916e64c8e6c452ee7424b6433e255f0f..0f2f8faba6ce0dbfd8f3e8df9d31f67756cff6c1:/classEquiv.tex?ds=sidebyside

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-This step considers as input the set 
-$\{((g_1,g_2),r_{12}), (g_1,g_3),r_{13}), (g_{n-1},g{n}),r_{n-1.n})\}$ of 
-$\frac{n(n-1)}{2}$ elements. 
-Each one $(g_i,g_j),r_{ij})$ where $i < j$, 
-is a pair that gives the similarity rate $r_{ij}$ between the two genes  
-$g_{i}$ and $g_{j}$.
-
-The first step of this stage consists in building the following non-oriented
-graph further denoted as to \emph{similarity graph}.
-In this one, the vertices are the genes. There is an edge between 
-$g_{i}$ and $g_{j}$ if the rate $r_{ij}$ is greater than a given similarity 
-threshold $t$.
-
-We then define the relation $\sim$  such that
-$ x \sim y$ if $x$ and $y$ belong in the same connected component.
-Mathematically speaking, it is obvious that this 
-defines an equivalence relation. 
-Let $\dot{x}= \{y  | x \sim y\}$
-denotes the equivalence class to which $x$ belongs.
-All the genes which are  equivalent to each other
-are also elements of the same equivalence class.
-Let us then consider the set of all equivalence classes of the set of genes 
-by $\sim$, denoted $X/\sim = \{\dot{x} | x \textrm{ is a gene}\}$. 
-defined by $\pi(x) = \dot{x}$
-which maps each gene  into it respective equivalence class by $\sim$.
-
-
-
-
-For each genome $[g_l,\ldots,g{l+m}]$, the second step computes 
-the projection of each gene according to $\pi$. 
-The resulting genome  which is 
-$$
-[\pi(g_l),\ldots,\pi(g{l+m})]
-$$ 
-is again of size $m$.
-
-Intuitively speaking, for two genes $g_i$ and $g_j$ 
-in the same equivalence class, there is path from  $g_i$ and $g_j$.
-It signifies that  each evolution step 
-(represented by an edge in the similarity graph) 
-has produced a gene s.t. the similarity with the previous one 
-is greater than $t$. 
-Genes $g_i$ and $g_j$ may thus have a common ancestor.
-
-
-We compute the core genome as follow.
-Each genome is projected according to $\pi$. We then consider the 
-intersection of all the projected genomes which are considered as sets of genes
-and not as sequences of genes.
-This results as the set of all the class $\dot{x}$
-such that each genome has an gene $x$ in  $\dot{x}$.
-The pan genome is computed similarly: the union of all the 
-projected genomes in computed here.
 
+The first method, described below, considers NCBI annotations and uses
+a distance-based similarity measure. We start with the following
+preliminary Definition:
+
+\begin{definition}
+\label{def1}
+Let $A=\{A,T,C,G\}$  be the nucleotides alphabet, and  $A^\ast$ be the
+set  of finite  words on  $A$  (\emph{i.e.}, of  DNA sequences).   Let
+$d:A^{\ast}\times   A^{\ast}\rightarrow[0,1]$   be   a   distance   on
+$A^{\ast}$. Consider a given value $T\in[0,1]$ called a threshold. For
+all   $x,y\in  A^{\ast}$,   we   will  say   that  $x\sim_{d,T}y$   if
+$d(x,y)\leqslant T$.
+\end{definition}
+
+\noindent $\sim_{d,T}$ is obviously an equivalence relation and when $d=1-\Delta$, where $\Delta$ is the similarity scoring function embedded into the emboss package (Needleman-Wunch released by EMBL), we will simply denote $\sim_{d,0.1}$ by $\sim$.
+
+The method begins by building  an undirected graph based on similarity
+rates $r_{ij}$ between DNA~sequences $g_{i}$ and $g_{j}$ (\emph{i.e.},
+$r_{ij}=\Delta\left(g_{i},g_{j}\right)$).  In this latter graph, nodes
+are  constituted by all  the coding  sequences of  the set  of genomes
+under consideration, and there is  an edge between $g_{i}$ and $g_{j}$
+if the  similarity rate  $r_{ij}$ is greater  than a  given similarity
+threshold. The  Connected Components (CC) of  the ``similarity'' graph
+are thus computed.
+
+This process also results in an equivalence relation between sequences
+in the  same CC  based on Definition~\ref{def1}.   Any class  for this
+relation   is  called   ``gene''  here,   where   its  representatives
+(DNA~sequences)  are the ``alleles''  of this  gene.  Thus  this first
+method   produces   for   each    genome   $G$,   which   is   a   set
+$\left\{g_{1}^G,...,g_{m_G}^G\right\}$    of   $m_{G}$    DNA   coding
+sequences, the  projection of each sequence according  to $\pi$, where
+$\pi$ maps each sequence into its gene (class) according to $\sim$. In
+other     words,      a     genome     $G$      is     mapped     into
+$\left\{\pi(g_{1}^G),...,\pi(g_{m_G}^G)\right\}$.    Note    that    a
+projected genome has no duplicated gene since it is a set.
+
+Consequently, the core  genome (resp.  the pan genome)  of two genomes
+$G_{1}$  and $G_{2}$  is defined  as  the intersection  (resp. as  the
+union) of their projected  genomes.  We then consider the intersection
+of  all the  projected genomes,  which  is the  set of  all the  genes
+$\dot{x}$  such  that   each  genome  has  at  least   one  allele  in
+$\dot{x}$. The  pan genome is computed  similarly as the  union of all
+the projected  genomes. However  such approach suffers  from producing
+too small core genomes,  for any chosen similarity threshold, compared
+to   what  is   usually   expected  by   biologists  regarding   these
+chloroplasts. We are  then left with the following  questions: how can
+we improve the confidence put in  the produced core? Can we thus guess
+the evolution scenario of these genomes?