Research

The Computational Evolutionary Genomics group at CBGP focuses on the use and development of comparative (meta-)genomic methods to decipher what makes each organism and ecosystem unique. We study processes such as gene loss and duplication, speciation, functional conservation and horizontal gene transfer. For this, we combine theoretical knowledge in evolutionary biology, sequencing data, and high performance computational resources. Our current research lines are:

Comparative metagenomics

Discovering functional novelty out of massive metagenomic data. We are especially interested in identifying novel gene functions with potential applications in biotechnology and human health. Our current projects aim at discovering novel enzymes, uncovering functional signatures in metagenomics samples and developing methods for pathogen diagnosis.

Evolution at the gene family level

We are interested in different aspects of gene family evolution, such as dating the emergence of specific functions, studying gene duplication, identifying horizontal gene transfers, and characterizing gene fusions. We are specialized in large scale phylogenomic analysis, where hundreds of genomes are compared at once. Besides obtaining basic biological insights on genomes evolution, we aim at using such information to improve plant breeding programs.

Phylogenetic diversity within microbial communities

Metagenomics data is incomplete, noisy and quite challenging for classic evolutionary analysis. We work in the application of phylogenetic techniques for functional and taxonomic annotation of massive collections of metagenomic sequences. We are particularly interested in exploring the unknown fraction of microbial biodiversity, particularly in eukaryotes, where new lineages and unexpected biologies, might be found.

Phylogenomic methods and tools

We develop functional prediction methods, metagenomic frameworks, orthology resources and genomic databases. Many of those tools are the result of our own needs, but we also work on providing open source implementations that are useful to other researchers in the field.