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 ... ... @@ -10,7 +10,9 @@ \pubyear{XXXXX} \jvolume{XXXXX} \jissue{XXXXX} \makeatletter \newcommand{\removelatexerror}{\let\@latex@error\@gobble} \makeatother \usepackage{graphicx} %\usepackage{caption} \usepackage{amssymb} ... ... @@ -81,7 +83,7 @@ \usepackage[noend,ruled,vlined]{algorithm2e} %\usepackage{tikz} %\usepackage{todonotes} \usepackage{todonotes} %\newtheorem{theorem}{Theorem}[section] %\newtheorem{lemma}[theorem]{Lemma} ... ... @@ -274,15 +276,15 @@ In RNA, a large proportion of observed covariations are adequately explained by Since this structure is, to a large extent, already revealed by comparative analysis (and already present in the \rfam{} profile taken as input to the method), it does not constitute the primary object of interest of our study. In order to minimize the probability of detecting a local structural compensation, we require a minimal distance $\gamma$ between the mutation and the loops identified for their good NPMI values. This definition is formalized in Algo.~\ref{algo:pos}. \subsubsection{Interchain Positions} \subsubsection{\todo{not really interchain anymore}Interchain Positions} Since both negative and positive correlations can indicate positions of interest, we use two different, $\zeta^-$ and $\zeta^+$, thresholds for the \NPMI{}s. $\zeta^+$ will be a bound on the positive values of the \NPMI and $\zeta^-$ on the negative ones. Due to the high number of possible combinations, \NPMI{}s having values $-1$ are frequent and uninformative. They are discarded. For those loops deemed as regions of interest, we predict that the positions having an \NPMI above $\zeta^+$ (resp. below $\zeta^-$) have an interchain interactions while the others do not. \begin{algorithm}[t] have an \todo{idem}interchain interactions while the others do not. {\removelatexerror \begin{algorithm} \DontPrintSemicolon \SetAlgoLined \SetKwFunction{shapeDisruption}{shapeDisruption} ... ... @@ -300,9 +302,10 @@ $l = D = \varnothing$\; \Return{D} \caption{disruptiveMutations$\left(MaM, \delta\right)$} \label{algo:mut} \end{algorithm} \end{algorithm}} \begin{algorithm}[t] {\removelatexerror \begin{algorithm}[H] \DontPrintSemicolon \SetAlgoLined \SetKwFunction{getAllNPMIs}{getAllNPMIs} ... ... @@ -326,9 +329,10 @@ $\zeta^-\leftarrow -1\times\percentile(-1\times a[-1  ... ... @@ -225,14 +225,16 @@ The results for c-di-GMP having as positive positions the ones interacting in th \end{figure} \section{Dataset binding positions} \begin{table}[ht!] \rotatebox{90}{ \begin{tabular}{lllll} RNA & Binding to & RFAM & PDB(s) &Binding Positions on PDB \\\hline\hline 5S & Prots. & RF00001 & 2WWQ &$7-13,27-33,38,41-57,59-60,70,73-84,88-104,112-116$\\ & & & 3OAS &$6-12,26-33,37-38,41-52,54-57,59,70,73-84,88-104,112-116$\\ & & & 3OFC &$6-12,27-31,33,37-38,41-52,54-59,73-84,88-104,112-117$\\ & & & 3ORB &$6-12,27-31,33,37-38,41-52,54-59,73-84,88-104,112-116$\\\hline tRNA & Prots. and DNA & RF00005 &1EHZ &$1, 19, 34-36, 56-57, 73-76$\\\hline tRNA & anticodon & RF00005 &1EHZ &$34-36$\\\hline c-di-GMP ribo. & c-di-GMP & RF01051 & 3IWN &$8-10,28,38,53-64,66-72,82$\\ & & & 3MUT &$18-20,38,48,61-64,75,92$\\ & & & 3MUV &$18-20,34,38,48,60-64,75,92$\\ ... ... @@ -241,7 +243,8 @@ The results for c-di-GMP having as positive positions the ones interacting in th adenine ribo. & adenine & RF00167 & 1Y26 &$21-22,47,50-52,73-75$\\\hline glycine ribo. & glycine & RF00504 & 3P49 &$35-39, 46, 48-42, 110-114,137, 139-143\$\\ \end{tabular} \caption{{\color{red}For each RNA some info}} } \caption{For each RNA some info} \label{table:datasetinfo} \end{table} ... ...
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