--- /dev/null
+The approache is further based on the ability to decide how far is each
+genome from each others. To achieve this, we combine XXX metrics which are
+detailed in this part.
+
+\subsection{Core SNP based metric}
+Due to the definition of the core genome, for each element $\dot{x}$
+in this set, there is a gene $x \in \dot{x}$ in each genome.
+Let us consider a class
+$\dot{x}= \{y | x \sim y\}$.
+
+\JFC{Il faudrait être cohérent: deux génomes proches devraient partout avoir
+soit une métrique élevée soit une métrique très faible}
+
+%1/ On SNPs of the core genome strict
+All the $y$ are thus aligned
+thanks to a global alignment tool. The SNPs may thus be extracted.
+For each genome, one can thus compute the vector of boolean values
+memorizing at index $i$ wether the SNP $i$ is present in one of its gene
+(postive value) or not (null value).
+A Hamming distance between two vectors allows to build the distance
+between two genes.
+This metric is further refered as to $m_S$.
+
+% plus il y a de diff, plus le nombre est élevé
+
+
+%2/ On SNPs of the core genome strict, each gene having the same weight
+The $m_S$ method does not consider genes to have the same incidence in the
+metric value. A gene with many SNPs has a larger influence in
+the metric computation than a gene with fewer ones.
+The metric further refered as to $m_{|S|}$ gives the same weight to each gene
+without considering the number of SNP it contains.
+
+% plus il y a de diff, plus le nombre est élevé
+
+
+%3/ On gene content (symmetric difference)
+The third metric consider the symetric difference $\Delta$
+between the two sets $G_1$ and $G_2$ of genes.
+$$
+G_1\Delta G2 =
+(G1\cup G_2)\setminus (G1\cap G_2) = (G1\setminus G_2)\cup(G_2\setminus G1)
+$$
+\end{document}
+
+% 4/ Using EPFL method
+% 5/ On size of the biggest syntheny bloc
+% 6/ On average size of syntheny blocs
+% 7/ On number of syntheny blocs.
