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The way to infer well-supported phylogenetic trees that precisely reflect theevolutionary process is a challenging task that completely depends on the waythe related core genes have been found. In previous computational biologystudies, many similarity based algorithms, mainly dependent on calculatingsequence alignment matrices, have been proposed to find them. In these kinds ofapproaches, a significantly high similarity score between two coding sequencesextracted from a given annotation tool means that one has the same genes. In aprevious work article, we presented a quality test approach (QTA) that improvesthe core genes quality by combining two annotation tools (namely NCBI, apartially human-curated database, and DOGMA, an efficient annotation algorithmfor chloroplasts). This method takes the advantages from both sequencesimilarity and gene features to guarantee that the core genome contains correctand well-clustered coding sequences (\emph{i.e.}, genes). We then show in thisarticle how useful are such well-defined core genes for biomolecularphylogenetic reconstructions, by investigating various subsets of core genes atvarious family or genus levels, leading to subtrees with strong bootstraps thatare finally merged in a well-supported supertree.

Improved Core Genes Prediction for Constructing well-supported Phylogenetic Trees in large sets of Plant Species