+\vspace{-1cm}
+
+Table~\ref{Etime} presents for each method the annotation type,
+execution time, and the number of core genes. We use the following
+notations: \textbf{N} denotes NCBI, while \textbf{D} means DOGMA,
+and \textbf{Seq} is for sequence. The first {\it Annotation} columns
+represent the algorithm used to annotate chloroplast genomes, the {\it
+Features} columns mean the kind of gene feature used to extract core
+genes: gene name, gene sequence, or both of them. It can be seen that
+almost all methods need low {\it Execution time} to extract core genes
+from large chloroplast genome. Only the gene quality method requires
+several days of computation (about 3-4 days) for sequence comparisons,
+once the quality genomes are construced it takes just 1.29~minutes to
+extract core gene. Thanks to this low execution times we can use these
+methods to extract core genes on a personal computer rather than main
+frames or parallel computers. The lowest execution time: 1.52~minutes,
+is obtained with the second method using Dogma annotations. The number
+of {\it Core genes} represents the amount of genes in the last core
+genome. The main goal is to find the maximum core genes that simulate
+biological background of chloroplasts. With NCBI we have 28 genes for
+96 genomes, instead of 10 genes for 97 genomes with
+Dogma. Unfortunately, the biological distribution of genomes with NCBI
+in core tree do not reflect good biological perspective, whereas with
+DOGMA the distribution of genomes is biologically relevant. {\it Bad
+genomes} gives the number of genomes that destroy core genes due to
+low number of gene intersection. \textit{NC\_012568.1 Micromonas
+pusilla} is the only genome which destroyed the core genome with NCBI
+annotations for both gene features and gene quality methods.
+
+The second important factor is the amount of memory being used by each
+methodology. Table \ref{mem} shows the memory usage of each
+method. We used a package from PyPI~(\textit{the Python Package
+Index}) named \textit{Memory\_profile} (located at~{\tt
+https://pypi.python.org/pypi}) to extract all the values in
+table~\ref{mem}. In this table, the values are presented in megabyte
+unit and \textit{gV} means genevision~file~format. We can notice that
+the level of memory which is used is relatively low for all methods
+and is available on any personal computer. The different values also
+show that the gene features method based on Dogma annotations has the
+more reasonable memory usage, except when extracting core
+sequences. The third method gives the lowest values if we already have
+the quality genomes, otherwise it will consume far more
+memory. Moreover, the amount of memory used by the third method also
+depends on the size of each genome.
