Séminaire de Bioinformatique et Biologie Computationnelle sur la thématique d’analyse de données métagénomiques.
L’exposé sera suivi d’une pause café.
Orateur : Guillaume Bernard (Sorbonne Université, MNHN, Paris)
TITRE : Next-generation phylogenomics: alignment-free approaches, sequence similarity networks and more
Lieu : CGFB – salle de conférences
From the 2000’s with the development of Next Generation Sequencing (NGS), biologists have been able to sequence microbes directly from the environment. The ‘microbial dark matter’ represented in metagenomes could contain a huge amount of new information about Earth’s microbial diversity and the origins of life, as well as solutions to medical problems or the adaptation to climate changes. NGS brought a deluge of collected sequence data in which we found a huge diversity and uncharacterized organisms that weren’t cultivated in the laboratory. Alternative approaches to the classical phylogenetic methods based on multiple sequence alignment (MSA), such as sequence similarity networks (SSN) and alignment-free (AF) methods, have been increasingly used in evolutionary analyses to cope with the increasingly large amount of data. These latter approaches are faster and more scalable than their MSA-based counterpart, and can be applied to a broader range of data (sequencing reads, whole genomes, etc). I will start with a brief introduction to the AF approaches followed by an overview of the different methods available. Next, I will show the network-based methods and their applications. Finally, I will present a novel approach combining the SSN and the AF methods to quickly identify gene/proteins of interest in metagenomic data and infer proxies of phylogenies, robust to long branch attraction, when the data are too large or divergent to perform a MSA.