Ph.D
Group : Bioinformatics

Algorithmic Aspects of Genome Rearrangements: Duplications and Partial Orders

Starts on 01/10/2006
Advisor : DENISE, Alain
[VIALETTE, M. Stéphane]

Funding : Attaché temporaire d'enseignement et de recherche
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 06/11/2009, committee :

Research activities :
   - Algorithms
   - Bioinformatics
   - Combinatory
   - Complexity

Abstract :
Comparative genomics aims to better understand differences between species. Several methods for genome comparison exist; in this PhD, we have focused on the computation of three measures of (dis)similarities, namely the number of adjacencies, the number of breakpoints, and the number of commons intervals. In presence of duplicated genes or when the order of genes is only partially known, computing these measures is a NP-hard problem. Our contribution is twofold.
First, we want to compute the number of adjacencies and the number of breakpoints for three models (exemplar, maximum and the intermediate model introduced in this work) between two genomes with duplications. In order to obtain exact results, we have used a pseudo-boolean programming approach. After a test on 12 genomes of ?-proteobacteria, we got enough results to compare different combinations of measure/model. Additionally, we have proposed and evaluated (thanks to the above-mentioned results) a family of heuristics based on a search of a longest common subsequence, which gave very good results on these data.
In parallel, we proved that there exists no approximation algorithm (unless P=NP) to compute the number of adjacencies (on the exemplar model) and the number of breakpoints (on the exemplar and intermediate models).
Second, we set up a pseudo-boolean approach to compute the number of adjacencies and the number of common intervals between two partially ordered genomes. Using nearly 800 simulated genomes, we have studied the influence of parameters associated to partial orders and compared both measures.

More information: http://www.lri.fr/~thevenin/

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.