Commit 81039242 authored by Carlos GO's avatar Carlos GO
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\title{PhD Proposal Exam \\ Computational Tools for Complex RNA Structure Analysis}
\author{Carlos G. Oliver}
\date{\today} % Activate to display a given date or no date
RNA (Ribonucleic Acid) is a family of functional molecules which control vital processes in all kingdoms of life.
Complex 3D structures in these molecules encode a signature required to specify complex functions such as: regulating gene expression, catalyzing reactions, and SOMETHING.
The unique flexibility of RNA molecules allows for a large range of complex structures to be adopted which can determine very specific functions.
In this thesis we address two major questions: 1) the emergence of such structures through evolutionary processes, and 2) the analysis of complex RNA structures as functional signatures.
A major question is how complex structures could have arisen in prebioitic conditions.
Project I addresses this question with a unique evolutioary reactor for studying the thermodynamic and environmental scenarios which could lead to the emergence of complex RNA structures.
While statistical and thermodynamic methods have been successful at modelling RNA at a 2D (planar) level, these are ultimately a proxy for the true determinants of structure at the 3D level.
In Project II we
\section{Thesis Objectives and Contributions}
\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
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\section{{\bf Project I:} Maternal}
\section{{\bf Project II:} RNAmigos}
\section{{\bf Project III:} GARL}
\section{{\bf Project IV:} VERNAL}
Motivation: Functional RNA molecules adopt detailed 3D structural patterns (motifs) to carry out complex functions. Recent work has shown that such motifs are conserved across different RNA molecules [1], suggesting that the set of structural motifs may constitute an alphabet out of which evolution can build complex RNA functions.
Problem: We can think of a motif as a sub-structure which is identical or similar some sub-structure in other RNA molecules. Hence, the task of identifying structural motifs from a large set of full RNA crystal structures requires 1) structure comparison (recognizing similar sub-structures) , and 2) sub-structure searching (a way to navigate large structures to test for similarity). The main challenge is that comparing structures is computationally expensive, and the number of possible structures to explore explodes for even small structures.
Current approaches: State-of-the-art techniques for mining structural motifs rely on two major constraints to address the computational challenges. The first is that they apply strong limitations on which sub-structures to evaluate. And the second is that they assume instances of motifs will be exactly identical to each other. For a molecule as flexible as RNA, it is verly likely that motifs can adopt a range of possible conformations. Therefore, our current view of the repertoire of RNA structural motifs repertoire is narrow.
Our approach: We propose VERNAL, the first tool which addresses both limitations of current methods. Our tool is built on a graph neural networks which encodes local structural information across very large sets of structures into a vector space. Similar sub-structures are thus represented as nearby vectors. The problem of comparing structures is reduced to comparing vectors, and the problem of searching the structures becomes a vector-based search. We show that this method produces richer motifs (more flexible) in a fully scalable manner.
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% COPYRIGHT 2014 - Claudio Fiandrino
% Released under the LaTeX Project Public License v1.3c or later
% email: <claudio dot fiandrino at gmail dot com>
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