Martina Olivero

CONNECTOR analyzes longitudinal tumor growth data to identify clusters and reveal new molecular associations with therapy response.

The paper compares RNA profiles in plasma-derived large and small extracellular vesicles from neurodegenerative disease patients and healthy controls, identifying disease-specific miRNA signatures.

The paper evaluates the sparsely-connected autoencoder (SCA) for functional mining of single-cell RNA-seq clusters, showing its ability to uncover hidden features and improve cluster reconstruction.

rCASC provides a reproducible RNA-seq analysis workflow for single-cell data, enabling functional and computational reproducibility from counts generation to cell sub-population signature identification.

The paper introduces a reproducible bioinformatics project framework using Docker and R to ensure consistent and replicable analysis workflows across different systems.

The paper compares two RNA-seq library preparation protocols, showing ACCESS detects more coding SNVs/INDELs but POLYA is better for UTRs, with no advantage for fusion transcripts.