genome from each others. To achieve this, we combine XXX metrics which are
detailed in this part.
-\subsection{Core SNP based metric}
+\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
% plus il y a de diff, plus le nombre est élevé
-\subsection{Symmetric Difference based metric}
+\subsection{Symmetric Difference based Metric}
The third metric consider the symmetric difference $\Delta$
between the two sets $G_1$ and $G_2$ of genes recalled hereafter
$$
This metric is equal to the Hamming distance between the two corresponding
vectors of Boolean values.
+% plus il y a de diff, plus le nombre est élevé
+
+
+
+% 4/ Using EPFL method
\subsection{Adjacency based metric}
23424133
-% 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.
+
+
+
+
+
+
+\subsection{Shared Synteny based Metric}
+Given two genomes abstracted as sequences of classes, it is classical
+to computes all the maximum shared synteny chains.
+
+% Attention ici, moins il y a de diff, plus le nombre est élevé
+There are then three issues with such a set of shared synteny chains:
+\begin{itemize}
+\item let $m_{Y}$ be the metric, which returns the
+length of the largest chains;
+\item let $m_{\overline{Y}}$ be the metric, which returns the
+average length of synteny chains;
+\item finally, let $m_{|Y|}$ be the metric, which returns the
+number of synteny chains.
+\end{itemize}
