Eugeni Belda

CurateMake is a workflow for harmonizing and validating ITS reference databases, improving taxonomic coherence and error correction through nomenclatural standardization and phylogenetic validation.

CroCoDeEL detects and quantifies cross-sample contamination in metagenomic data without negative controls or prior knowledge, showing high accuracy even at low contamination rates.

This study characterizes gut microbiota composition along the gastrointestinal tract in mice under high-fat diet and prebiotic conditions, revealing species-specific responses and challenges the translational relevance of rodent models.

MetagenBERT uses Transformer models to embed DNA reads for disease classification, achieving comparable performance to existing methods for cirrhosis and better results for type 2 diabetes.

This paper reviews deep learning methods in metagenomics, focusing on their application to human gut microbiome analysis and their potential to improve disease prediction and patient stratification.

The paper develops interpretable metagenomics prediction models based on microbial taxon abundance ratios, additions, and subtractions, achieving performance comparable to or better than traditional machine learning methods.

The paper identifies novel RNA viruses in Anopheles mosquitoes from Senegal and Cambodia, revealing virome diversity and potential interactions with small RNA pathways.