In this research work, we studied two %three 
methodologies for extracting core genes from a large set of chloroplasts genomes, and we developed 
Python programs to evaluate them in practice. 
%Extracted core genomes 
%depend on both gene names and sequences.
% Furthermore, that extract these core genes with the three methodologies. 

We firstly considered to extract core genomes by the way of comparisons 
(global alignment) of DNA sequences downloaded from NCBI database. 
However this method failed to produce biologically 
relevant core genomes, no matter the chosen similarity threshold, probably
due to annotation errors. We then considered to use the DOGMA annotation tool 
to enhance the genes prediction process. The second method consisted in extracting 
gene names either from NCBI gene features or from DOGMA results. A first
``intersection core matrix (ICM)'' where built, in which each coefficient 
stored the intersection cardinality of the two genomes placed at the extremities
of its row and column. New ICMs are 
then constructed by selecting the maximum intersection score (IS)  in this matrix, 
removing the two genomes having this score, and adding the corresponding 
core genome in a new ICM construction. %Finally, in the third method, a genes quality test has been added before the ICMs computation, to ensure that the genes obtained in the NCBI annotation files are the same %(\emph{i.e.}, gene name and sequence) than the ones produced by DOGMA. 
% A genes quality test has then been introduced  to construct new ICMs
% on genomes 
% only constituted by the genes that successfully passed 
% a specific similarity threshold of 65\% on their sequences.
% % , ICM 
% % then will take place to extract the core genes.
% 

Core trees have finally been generated for each method, to investigate 
the distribution of chloroplasts and core genomes. The tree from second 
method based on DOGMA has revealed the best distribution of
 chloroplasts regarding their evolutionary history. In particular, it appears to
us that each endosymbiosis event is well branched in the DOGMA core tree.     

In future work, we intend to deepen the methodology evaluation by considering
new gene prediction tools and various similarity measures on both
gene names and sequences. Additionally, we will investigate new clustering
methods on the first approach, to improve the results quality in this promising way to 
obtain core genes. Finally, the results produced with DOGMA will be 
further investigated, biologically speaking: the genes content of each core
will be studied while phylogenetic relations between all these species
will be questioned.
