Marco Beccuti

This paper introduces a stochastic model of tumor evolution that integrates genetic and environmental factors to simulate clonal dynamics and demonstrates how ecological feedbacks shape cancer progression.

The paper introduces a new modeling paradigm, UnifiedGreatMod, that integrates fine-grained and coarse-grained biological information to study system behavior, demonstrating its application in simulating E. coli metabolism and analyzing cell responses to infection.

The paper introduces ORCA, an open-source Shiny application for semi-automated analysis of cellular biology experiments, promoting reproducibility and open science practices.

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

Stardust improves spatial transcriptomics clustering by integrating spatial and transcriptomic data through modularity optimization, achieving more stable and biologically coherent results.

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.

The paper evaluates how sample prep and bioinformatics analysis affect alternative splicing detection, finding that low-input methods like NuGEN v2 are less effective than high-input methods like TruSeq.