X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/chloroplast13.git/blobdiff_plain/449a44e17a928aa5b2905e62860896ccf83c0ceb..9771923779498f5cbf4e21e9389062ac5d5cbb11:/annotated.tex

diff --git a/annotated.tex b/annotated.tex
index 90ca53a..dd57e11 100644
--- a/annotated.tex
+++ b/annotated.tex
@@ -295,11 +295,7 @@ names\_Accession number)}. While an edge is labelled with the number of
 lost  genes from  a leaf  genome or  an intermediate  core  gene. Such
 numbers are  very interesting because  they give an  information about
 the evolution:  how many genes  were lost between two  species whether
-they  belong  to  the  same  family  or not.   By  the  principle  of
-classification, a  small number of genes lost  among species indicates
-that those species are close to  each other and belong to same family,
-while a  large lost  means that we  have an  evolutionary relationship
-between species  from different families. To depict  the links between
+they  belong  to  the  same  lineage  or not. Phylogenetic relationships are mainly built by comparison of sets of coding and non-coding sequences. Phylogenies of photosynthetic plants are important to assess the origin of chloroplasts (REF) and the modalities of gene loss among lineages. These phylogenies are usually done using less than ten chloroplastic genes (REF), and some of them may not be conserved by evolution process for every taxa. As phylogenetic relationships inferred from data matrices complete for each species included and with the same evolution history are better assumptions, we selected core genomes for a new investivation of photosynthetic plants phylogeny. To depict  the links between
 species   clearly,  we   built   a  phylogenetic   tree  showing   the
 relationships based on the distances among genes sequences. Many tools
 are    available   to    obtain    a   such    tree,   for    example:
@@ -336,7 +332,7 @@ We implemented the three algorithms using dell laptop model latitude E6430 with
  & \multicolumn{2}{c}{Annotation} & \multicolumn{2}{c}{Features} & \multicolumn{2}{c}{E. Time} & \multicolumn{2}{c}{C. genes} & \multicolumn{2}{c}{Bad Gen.} \\
 ~ & N & D & Name & Seq & N & D & N & D & N & D \\
 \hline
-Gene prediction & $\surd$ & - & - & $\surd$ & ? & - & ? & - & 0 & -\\[0.5ex]
+Gene prediction & $\surd$ & - & - & $\surd$ & 1.7 & - & ? & - & 0 & -\\[0.5ex]
 Gene Features & $\surd$ & $\surd$ & $\surd$ & - & 4.98 & 1.52 & 28 & 10 & 1 & 0\\[0.5ex]
 Gene Quality & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{$\simeq$3 days + 1.29} & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{1}\\[1ex]
 \hline
@@ -355,12 +351,12 @@ The second important factor is the amount of memory usage in each methodology. T
 \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 & ~ & ~ & ~ & ~ & ~ & ~ & ~\\
+Gene prediction & NCBI & 100 & - & - & - & 108 & -\\
 \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$2.7G & 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}