Pau Piera Líndez

TaxVAMB improves metagenome binning by combining tetranucleotide frequencies and co-abundances with taxonomic annotations, outperforming existing methods on benchmark and real datasets.

PlasMAAG improves plasmid reconstruction from metagenomics data using assembly-alignment graphs and contrastive learning, outperforming existing methods in accuracy and efficiency.

Taxometer improves taxonomic classification of metagenomics contigs by combining abundance profiles and tetra-nucleotide frequencies, enhancing accuracy across multiple tools.

BinBencher provides a benchmarking suite for metagenomic binning that produces more biologically meaningful evaluations than existing methods.

FAVA infers high-quality functional protein interaction networks from scRNA-seq and proteomics data, outperforming existing methods and identifying interactions for understudied proteins.

This paper introduces AAMB, a deep learning method for metagenomic binning that improves genome reconstruction by integrating sequence co-abundances and tetranucleotide frequencies, outperforming existing methods in completeness and diversity.