The huge amount of data generated by new DNA sequencing techniques may possible to improve the accuracy of species circumscriptions and genome annotations. Our team is developing and applying genome-scale tools and pipelines with the aim of predicting microbial and enzymes performance and/ stability allowing us to select those of outstanding properties. Our studies are applied to strains of agronomic, clinical or industrial interest. We are focused on the following topics:

Taxonomic resolution of highly phylogenetically related strains.

Despite the existence of guidelines and recommendations to guarantee stability, reproducibility and coherence in the taxonomy, the methodology to circumscribe the strains in species is still subjective and arbitrary. Furthermore, the species classification of many Firmicutes (Enterococcus, Lactococcus, Lactobacillus or Bacillus) is not defined by a theoretical concept, but generally by a pragmatic or industrial praxis purpose. However, precise species status is extremely important for commercial acceptance in agronomic or industrial applications. Therefore, in the laboratory we focus on making highly phylogenetic related strain species assignments, achieving coherence between phylogenetic, genomic and phenotypic characteristics.

Gene mining and microbial profiles

The biological functions encoded in an organism eventually determine its phenotype and behavior in the environment or health. However, predicting protein function is a complex task, being current in silico solutions focused on orthology. We have developed a tool that establishes the bacterial functional profile based on the biological functions encoded in its genome. We mainly focus on the plant growth promoting rhizobacteria of the genus Bacillus.

Data mining and enzymes for industrial use

There is a growing demand for new proteins or proteins with exceptional characteristics for their application in various industrial processes and/or the development of genetically modified organisms. Although advances in genomics and data mining allow the search for hypothetical proteins that share homology with known proteins or enzymes, a large proportion of the proteins available in databases share little identity with proteins with known activity. In the laboratory we use ad hoc procedures that combine bioinformatic tools and statistical models to search protein databases for specific industrial purposes.