We are shedding light into the factors influencing the recovery of genomes from metagenomes towards the development of neutral and niche theories to study microbial interactions using multi-omics data.

Use of 'omics' databases to elucidate long-time assumptions in microbiology.

• Scaling modeling of microbial interactions to the diversity found in natural ecosystems: Definition of concepts and theories to use partial genome-scale metabolic modeling as a tool to explore microbial community interactions.

• Species in the era of metagenome assembled genomes: We are developing techniques to exploit the diversity of metagenome assembled genomes by resolving species (and potentially strain) variation in complex microbial communities.

• Exploration of regulatory network in complex microbial communities: We are developing deep learning approaches to map transcription factors (and potentially their binding sites) in complex microbial communities.

• High-throughput genomic analysis: with the exponential increase of genomic (and multi-omics) data available in public repositories, we are creating user-friendly pipelines that can be used by microbiologists and microbial ecologists to analyze hundreds or thousands of genomes simultaneously.

We are developing tools to adapt predictive analytics to relevant questions and hypothesis relevant to microbiologists, environmental microbiologists and microbial ecologists.

• Prediction of health and dysbiosis states in microbial communities: We are developing pipelines to detect health and dysbiosis from multi-omics bioindicators using machine learning.

• Prediction of ecosystem services based on omics data: We are developing pipeline mixing traditional omics and machine learning to determine ecosystem services in a multi-omics perspective. We are currently using benzoate degradation and carbon fixation pathways as a model pathways.