Olivier Gascuel

Méthodes et Algorithmes pour la Bioinformatique
LIRMM-CNRS, 161 rue Ada,
34392 - Montpellier - FRANCE
Tel. 33 (0) 4 67 41 85 47
Fax. 33 (0) 4 67 41 85 00
gascuel@lirmm.fr

· Recherches - research

· Laboratoire - lab

· Famille - family

Research interest

My research interest is in the field of evolution and phylogeny. My focus is on the mathematical and computational tools and concepts, which form now an essential basis of evolutionary studies (2005 book). My current researches follow three complementary directions:

· I am pursuing works on distance-based phylogeny inference methods, in particular with Richard Desper (NCBI, USA). We recently published a new method (FastME) which is not only faster than NJ, but also turns out to be much more accurate (JCB 2002, MBE 2004).

· I am studying the evolutionary history of repeated sequences, and specifically of multigenic families. This reflexion initiated by Fitch (1977) first had relatively little echoes, likely due to lack of data and algorithms. It is known now that repeats are very abundant in genomes, and the first algorithms answering the model of Fitch were proposed simultaneously and independently by Waterman’s team and ourselves in 2002. We thus showed the biological validity of Fitch’s model (MBE 2002), studied its combinatorial properties (Syst Biol 2003), notably with Mike Hendy (NZ), and designed efficent algorithms to infer duplication trees (IEEE-TCBB 2005, JDA 2005).

· I am working on algorithms for maximum likelihood phylogenetic inference, which is now recognized as the most accurate approach, but which is penalized by the heaviness of calculations. With Stéphane Guindon we thus proposed (Syst Biol 2003, ISI Fast Breaking Paper) a new algorithm which decreases by one order of magnitude the calculation times, allowing the treatment of more than 500 taxa and tens of thousands of sites. The corresponding method, PHYML, has been downloaded by numerous users and a webserver provides PHYML online (NAR 2005). We recently published a new improved version that includes SPR topological rearrangements (Bioinformatics 2005).