Commit 09b4f07a authored by Vladimir Reinharz's avatar Vladimir Reinharz
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update abstract / intro rna-dna, rna-lig

parent 7985d99e
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\begin{abstract}
Systematic structure probing experiments (e.g. SHAPE) of RNA mutants such as the mutate-and-map protocol give us a direct access into the genetic robustness of ncRNA structures. Comparative studies of homologous sequences provide a distinct, yet complementary, approach to analyze structural and functional properties of non-coding RNAs.\\
In this paper, we introduce a formal framework to combine the biochemical signal collected from mutate-and-map experiments, with the evolutionary information available in multiple sequence alignments. We apply neutral theory principles to detect complex long-range dependencies between nucleotides of a single stranded RNA, and implement these ideas into a software called \soft. We illustrate the biological significance of this signal and show that the nucleotides networks calculated with \soft are correlated with nucleotides located in RNA-RNA and RNA-protein interfaces. \soft is freely available at \softweb.
In this paper, we introduce a formal framework to combine the biochemical signal collected from mutate-and-map experiments, with the evolutionary information available in multiple sequence alignments. We apply neutral theory principles to detect complex long-range dependencies between nucleotides of a single stranded RNA, and implement these ideas into a software called \soft. We illustrate the biological significance of this signal and show that the nucleotides networks calculated with \soft are correlated with nucleotides located in RNA-RNA, RNA-protein{\color{red}, RNA-DNA and RNA-ligand} interfaces. \soft is freely available at \softweb.
\end{abstract}
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This paper attempts to look beyond RNA structure determination, and introduces a novel concept to leverage the information embedded in experimental structure probing data sets of mutant RNAs. We apply neutral theory principles \cite{Schuster:1994aa} to detect functional dependencies between distant nucleotides in a single stranded RNA. More precisely, we first use mutate-and-map data to identify mutations that significantly destabilize the native structure of the molecule, i.e. the mutations associated with the most divergent SHAPE profiles. Then, we retrieve from RNA multiple sequence alignments (\rfam database~\cite{burge2012rfam}) homologous sequences containing those destabilizing mutations, and compare their nucleotide distribution to the background distribution observed in the \rfam multiple sequence alignment. Finally, the ensemble of positions with highest mutual information is used to reveal nucleotide networks of functional dependencies. This protocol aims to capture non-trivial covariations or geometric conservations that are key to guarantee the stability and specificity of the binding site structure.
We implement our model in a software named \soft. We illustrate potential applications of the signal captured with our theoretical framework, and apply \soft to analyze mutate-and-map data sets available on the RNA Mapping Database~\cite{Cordero:2012aa}. Our experiments reveal non-trivial long-range dependencies within ncRNA primary structures of 5S ribosomal RNA, the c-di-GMP riboswitch and the glycine riboswitch. We investigate the biological significance of these patterns by looking at the distribution of these nucleotides on the RNA 3D structures. Interestingly, we find significant correlations between the sets of nucleotides produced by our method and those identified as participating in RNA-RNA and RNA-protein interfaces.
We implement our model in a software named \soft. We illustrate potential applications of the signal captured with our theoretical framework, and apply \soft to analyze mutate-and-map data sets available on the RNA Mapping Database~\cite{Cordero:2012aa}. Our experiments reveal non-trivial long-range dependencies within ncRNA primary structures of 5S ribosomal RNA, {\color{red}the yeast phenylalanine tRNA and the cobalamin, adenine, and glycine riboswitches}. We investigate the biological significance of these patterns by looking at the distribution of these nucleotides on the RNA 3D structures. Interestingly, we find significant correlations between the sets of nucleotides produced by our method and those identified as participating in RNA-RNA, RNA-protein{\color{red}, RNA-DNA and RNA-ligand} interfaces.
\section{Methods}
\label{sec:methods}
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