Commit 16d987b4 authored by Carlos GO's avatar Carlos GO
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garl intro

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\newcommand{\norm}[1]{\left\lVert#1\right\rVert}
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\title{PhD Proposal Exam \\ Computational Tools for Complex RNA Structure Analysis}
......@@ -53,18 +55,19 @@ The central goal of my PhD research is to develop tools for understanding comple
Successful development of such tools would represent a step towards a systematic understanding of the patterns governing key biological processes.
{\bf Project I} addresses the question of how RNA molecules could have evolved the basis of higher-order structural patterns in pre-biotic setting.
To this end, we develop an evolutioary reactor for studying the thermodynamic and environmental scenarios which could lead to the emergence of complex RNA structures.
To this end, we develop an evolutionary reactor for studying the thermodynamic and environmental scenarios which could lead to the emergence of complex RNA structures.
In {\bf Projects II-IV} we build a set of tools for extracting useful patterns from RNA at the 3D level.
The main challenge addressed by these tools is the computational complexity imposed by modeling higher-order interactions which break some of the central assumptions of earlier models such as planarity of graphs and the convenience of experimental energy models.
The result is \RNAmigos, \garl, and \vernal.
These tools respectively attempt to solve the problem of (i) building predictive models on complex RNA networks, (ii) making efficient comparisons and alignments (iii) mining recurrent structural signatures.
The result is \RNAmigos, and \vernal, and \garl.
These tools respectively attempt to solve the problem of (i) building predictive models on complex RNA networks, (ii) mining recurrent structural signatures (iii) making efficient comparisons and alignments .
\begin{itemize}
\item \maternal: complex structures evolve autonomously at 2D level
\item \RNAmigos: at 3D level we can learn useful functional signatures using graph representations
\item \garl: tool for automated structure matching with custom cost functions
\item \vernal: we can isolate new complex signatures using graph neural networks
\item \garl: tool for automated structure matching with custom cost functions
\end{itemize}
......@@ -103,7 +106,7 @@ These tools respectively attempt to solve the problem of (i) building predictive
\section{Background}
RNAs possess multiple levels of structural organization ranging from the secondary structure made, of Watson-Crick (\texttt{A-U, C-G}) and Wobble (\texttt{G-U}) base pairs to the full tertiary structure modelling the position of all atoms.
RNAs possess multiple levels of structural organization ranging from the secondary structure, made of Watson-Crick (\texttt{A-U, C-G}) and Wobble (\texttt{G-U}) base pairs to the full tertiary structure modelling the position of all atoms.
In a seminal work, Leontis and Westhof expanded the base-pairing nomenclature by identifying 12 different types of base-pairing interactions according to the relative 3D geometry of the participating nucleotides ~\cite{leontis2001geometric,leontis1998conserved}.
Among them, the canonical pairs (i.e. \texttt{A-U, C-G}) are the most studied class.
Notably, they create series of stable stacks that form a scaffold for the full structure ~\cite{tinoco1999rna}.
......@@ -112,14 +115,24 @@ Non-canonical pairs on the other hand are enriched in loops (i.e. regions witho
These interactions fine-tune the specificity of RNA interactions by determining structure at the 3D level~\cite{leontis2006building}.
\begin{figure}
\begin{figure}[h!]
\centering
\includegraphics[width=0.7\textwidth]{struc.jpeg}
\includegraphics[width=0.6\textwidth]{struc.jpeg}
\caption{This is an RNA. \cite{reinharz2016algorithmic}}
\end{figure}
\section{{\bf Project I:} \maternal -- Explaining the emergence of complex RNA}
It is widely hypothesized that life began with RNA molecules.
This is known as the `RNA world' hypothesis.
As outlined in the previous section, RNA possess a key duality: they are stable information carriers (like DNA) and they are flexible enough to catalyze chemical reactions (like protein).
RNA is therefore a parsimonious candidate for the earliest form of life which could have kickstarted natural selection.
This duality also raises a fundamental evolutionary tradeoff.
The need for stable/rigid structures preserves structural integrity and promotes the ability to store information.
In opposition, the lack of stability (flexibility) allows the RNA to adopt complex structures which can support key biological functions such as self replication.
However, observing the current diversity of RNA structures, and holding the assumption that RNA were the first biomolecules, it remains to be explained how this tradeoff was resolved by nature.
In this project, I use customized algorithms to explore the evolutionary landscape of RNA molecules in search of intrinsic forces that could have shaped RNA structural complexity in early life.
\subsection{Problem}
\subsection{Proposed Solution}
......@@ -176,11 +189,30 @@ Finally, the fingerprint is used in a similarity search to identify molecules ma
\end{figure}
\subsection{Results}
\subsection{Next Steps}
\section{{\bf Project III:} \garl -- Learning to compare complex RNA}
%https://arxiv.org/pdf/1901.06003.pdf
%https://arxiv.org/pdf/1410.3301.pdf
A fundamental problem in bioinformatics is the aligning of biological objects.
That is, given a pair of biological objects (sequences, phylogenetic trees, protein interaction networks, structure graphs) we seek a mapping between their constituent elements (nucleotides, species, amino acids) that implies the smallest amount of distortion between the two objects.
For DNA sequences, it is a matching between nucleotides that introduces the fewest insertions, deletions and substitutions.
Parsimonious alignments can then be interpreted to highlight conserved regions or better understand regions of variation.
With an alignment we also obtain a measure of similarity between the two objects which is often very useful in many pattern recognition tasks.
Aligning RNA graphs has resulted in many interesting tools (including \RNAmigos) such as CARNAVAL (discussed previously), and BayesPairing which uses RNA graph alignments to learn statistical models over the sequences forming these graphs and allows for sequence-based structure prediction.
Unlike sequence alignments, graphs are very high dimensional objects which makes them difficult to align.
Indeed, the complexity of aligning two graphs optimally is NP-Hard.
Furthermore, (inexact matching)
\subsection{Problem}
\begin{figure}
\centering
\includegraphics[width=0.7\textwidth]{GARL.pdf}
......@@ -218,6 +250,9 @@ Finally, the fingerprint is used in a similarity search to identify molecules ma
% \label{algo:dqn}
%\end{algorithm}
\subsection{Results}
\subsection{Next Steps}
\section{{\bf Project IV:} \vernal -- Searching for conserved RNA structures}
......@@ -237,6 +272,7 @@ We propose \vernal, the first tool which addresses both limitations of current m
\begin{figure}
\centering
\includegraphics[width=\textwidth]{vernal.pdf}
\caption{VERNAL}
\end{figure}
\begin{equation}
......@@ -276,5 +312,10 @@ We propose \vernal, the first tool which addresses both limitations of current m
\bibliography{biblio}
\subsection{Results}
\subsection{Next Steps}
\section{Conclusion}
\end{document}
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