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.
-\subsection{}
+ % plus il y a de diff, plus le nombre est élevé
+
+
- % 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.
+
+ % 4/ Using EPFL method
+\subsection{Adjacency based metric}
+23424133
++
++
++
++
++
++
+ \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}