\end{figure}
\section{Implementation}
-We implemented the three algorithms using dell laptop model latitude E6430 with 4 GB of memory and Intel core i5 processor of 2.6 Ghz and 3 MB of cash. We built the code using python version 2.7 under ubuntu 12.04 LTS. We also used python packages such as os, Biopython, memory\_profile, re, numpy, time, shutil, and xlsxwriter to extract core genes from large amount of chloroplast genomes. Table \ref{Etime}, show the annotation type, execution time, and the number of core genes for each method:
+We implemented the three algorithms using dell laptop model latitude E6430 with 6 GB of memory, and Intel core i5 processor of 2.5 Ghz$\times 4$ with 3 MB of CPU cash. We built the code using python version 2.7 under ubuntu 12.04 LTS. We also used python packages such as os, Biopython, memory\_profile, re, numpy, time, shutil, and xlsxwriter to extract core genes from large amount of chloroplast genomes. Table \ref{Etime}, show the annotation type, execution time, and the number of core genes for each method:
\begin{center}
\begin{tiny}
\caption{Memory usages in (MB) for each methodology}\label{mem}
\begin{tabular}{p{2.5cm}p{1.5cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}p{1cm}}
\hline\hline
-Method& & Load Gen. & Conv. gV & Read gV & ICM & Gen. tree & Core Seq. \\
+Method& & Load Gen. & Conv. gV & Read gV & ICM & Core tree & Core Seq. \\
\hline
Gene prediction & ~ & ~ & ~ & ~ & ~ & ~ & ~\\
\multirow{2}{*}{Gene Features} & NCBI & 15.4 & 18.9 & 17.5 & 18 & 18 & 28.1\\
& DOGMA& 15.3 & 15.3 & 16.8 & 17.8 & 17.9 & 31.2\\
-Gene Quality & ~ & 15.3 & $\le$200 & 16.1 & 17 & 17.1 & 24.4\\
+Gene Quality & ~ & 15.3 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\
\hline
\end{tabular}
\end{table}
