From: bassam al-kindy Date: Tue, 10 Dec 2013 10:26:32 +0000 (+0100) Subject: update memory table X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/chloroplast13.git/commitdiff_plain/449a44e17a928aa5b2905e62860896ccf83c0ceb?ds=sidebyside;hp=-c update memory table --- 449a44e17a928aa5b2905e62860896ccf83c0ceb diff --git a/annotated.tex b/annotated.tex index 52ce278..90ca53a 100644 --- a/annotated.tex +++ b/annotated.tex @@ -325,7 +325,7 @@ to align these sequences with each others. \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} @@ -360,7 +360,7 @@ Method& & Load Gen. & Conv. gV & Read gV & ICM & Gen. tree & Core Seq. \\ 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$2.7G & 16.1 & 17 & 17.1 & 24.4\\ \hline \end{tabular} \end{table}