Camila P. E. de Souza

This study investigates the pervasiveness and variation of genomic signatures across eukaryotic genomes using k-mer-based FCGR analysis, identifying localized exceptions and developing tools for improved taxonomic classification.

CopyMix jointly infers cell clusters and copy number profiles from single-cell DNA sequencing data, avoiding clustering artifacts and parameter tuning.

SomaticSiMu simulates mutational signatures in genomic sequences and evaluates its reliability using machine learning and mutational signature extraction.

Epiclomal clusters sparse single-cell DNA methylation data and imputes missing values, outperforming non-probabilistic methods and revealing sub-clonal methylation patterns in tumour genomes.

This paper uses machine learning and genomic signatures to classify novel pathogens, demonstrating high accuracy in identifying the origin and taxonomy of the COVID-19 virus.