Ph.D
Group : Bioinformatics

Algorithmique de l'alignement structure-séquence d'ARN: une approche générale et paramétr

Starts on 01/10/2009
Advisor : DENISE, Alain

Funding : CDD sur contrat UPS
Affiliation : Université Paris-Saclay
Laboratory : LRI BIO-INFO

Defended on 05/12/2012, committee :

Research activities :

Abstract :
Non-coding RNA macromolecules are involved in the metabolism of all living beings. From the computational point of view, their two major biological problems are: the prediction of their structure to better understand their functions and their detection in databases or genomes. The RNA structure-sequence alignment addresses these two issues. The RNA structure-sequence aligment is to align a known structure of a first RNA with the sequence of a second RNA. The structure is represented as an arc-annotated sequence and the sequence represents the RNA nucleotide sequence. To solve this problem, we want to optimize the alignment according to a cost function. So this is an optimization problem, which is NP-hard. Accordingly, different works define several reduced structure classes for which they propose specific algorithms but with polynomial complexity.

The presented work unifies and generalizes all previous approaches by building a unique non-specific class algorithm with parametrized complexity. This algorithm uses a technique from graph theory: the decomposition tree, that is to say, it transforms the given structure into a tree-decomposition and then I will explain how to align this decomposition with the sequence. I will then highlight why the implementation of this approach requires a reformulation of the problem as well as a substantial modification to the conventional use of dynamic programming for tree decompositions. This leads to a parameterized algorithm whose parameter is entirely related to the tree-decomposition.

The construction of tree decomposition for which alignment is the most effective is unfortunately also a NP-hard problem. However, I will outline a heuristic decompositions construction adapted to RNA structures and show that the complexity of the approach (solving the problem in its generality) equals or outperforms all previous approaches in their respective structure classes. I will finish by presenting new structure classes which extend existing ones without degrating the complexity of the alignment but which can represent the majority of known structures containing many important elements not previously taken into account (such as RNA tertiary motifs).

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.