From: Michel Salomon <salomon@caseb.iut-bm.univ-fcomte.fr>
Date: Mon, 2 Dec 2013 13:09:16 +0000 (+0100)
Subject: Some further minor corrections
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/chloroplast13.git/commitdiff_plain/b772a8e67c211b283f3ba146cc61a50834da2f1b?ds=inline;hp=-c

Some further minor corrections
---

b772a8e67c211b283f3ba146cc61a50834da2f1b
diff --git a/annotated.tex b/annotated.tex
index 819cc33..8cc8016 100644
--- a/annotated.tex
+++ b/annotated.tex
@@ -47,11 +47,11 @@ that stores annotated and/or unannotated chloroplast genomes.  We have
 considered the GenBank-NCBI \cite{Sayers01012011} database as sequence
 database:  99~genomes of chloroplasts  were retrieved.   These genomes
 lie in  the eleven type  of chloroplast families and  Table \ref{Tab2}
-summarizes their distribution in our dataset.\\
+summarizes their distribution in our dataset.
 
 \begin{figure}[h]  
   \centering
-    \includegraphics[width=0.8\textwidth]{generalView}
+    \includegraphics[width=0.75\textwidth]{generalView}
 \caption{A general overview of the annotation-based approach}\label{Fig1}
 \end{figure}
 
@@ -190,9 +190,9 @@ to extract core genes, as explained in Algorithm \ref{Alg3:thirdM}.
 \STATE $geneList=\text{empty list}$
 \STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
 \FOR{$\text{gene in common}$}
-	\STATE $gen1 \leftarrow open(NCBI\_Genes(gene)).read()$ 	
-	\STATE $gen2 \leftarrow open(Dogma\_Genes(gene)).read()$
-	\STATE $score \leftarrow geneChk(gen1,gen2)$
+	\STATE $g1 \leftarrow open(NCBI\_Genes(gene)).read()$ 	
+	\STATE $g2 \leftarrow open(Dogma\_Genes(gene)).read()$
+	\STATE $score \leftarrow geneChk(g1,g2)$
 	\IF {$score > Threshold$}
 		\STATE $geneList \leftarrow gene$
 	\ENDIF 
@@ -210,18 +210,16 @@ geneChk subroutine.
 \caption{Find the Maximum Similarity Score between two sequences}
 \label{Alg3:genechk}
 \begin{algorithmic} 
-\REQUIRE $gen1,gen2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
+\REQUIRE $g1,g2 \leftarrow \text{NCBI gene sequence, Dogma gene sequence}$
 \ENSURE $\text{Maximum similarity score}$
-\STATE $Score1 \leftarrow needle(gen1,gen2)$
-\STATE $Score2 \leftarrow needle(gen1,Reverse(gen2))$
-\STATE $Score3 \leftarrow needle(gen1,Complement(gen2))$
-\STATE $Score4 \leftarrow needle(gen1,Reverse(Complement(gen2)))$
-\RETURN $max(Score1, Score2, Score3, Score4)$
+\STATE $score1 \leftarrow needle(g1,g2)$
+\STATE $score2 \leftarrow needle(g1,Reverse(g2))$
+\STATE $score3 \leftarrow needle(g1,Complement(g2))$
+\STATE $score4 \leftarrow needle(g1,Reverse(Complement(g2)))$
+\RETURN $max(score1,score2,score3,score4)$
 \end{algorithmic}
 \end{algorithm}  
 
-% THIS SUBSECTION MUST BE IMPROVED 
-
 \subsubsection{Intersection Core Matrix (\textit{ICM})}
 
 To extract  core genes, we  iteratively collect the maximum  number of
@@ -321,7 +319,7 @@ to align these sequences with each others.
 \end{enumerate} 
 
 \begin{figure}[H]
-  \centering \includegraphics[width=0.8\textwidth]{Whole_system}
+  \centering \includegraphics[width=0.75\textwidth]{Whole_system}
   \caption{Overview of the pipeline}\label{wholesystem}
 \end{figure}