Gianluca Della Vedova

VESPA-SS detects somatic structural variants from long-read sequencing data without requiring read mapping or a high-quality germline reference, achieving high F1 scores on cancer cell lines.

The paper introduces PlasBin-HMF, a method for plasmid binning from short-read assemblies, which outperforms existing methods by preserving explainability.

PanSpace is a convolutional neural network-based tool for fast and scalable indexing of bacterial genome databases, reducing disk space and improving query speed.

This paper introduces deepSpecas, a deep learning method for differential analysis of alternative splicing events in RNA-seq samples, achieving higher precision with reduced sensitivity.

RecAlign improves recombination-aware sequence-to-graph mapping by using an A* algorithm with haplotype information and a heuristic function, achieving faster and more efficient performance.

The paper introduces pantas, a pangenomic method for detecting and differentially quantifying alternative splicing events using RNA-Seq data, showing competitive accuracy compared to linear reference-based approaches.

This paper introduces a method to construct pangenome graphs by solving an exact cover problem on blocks, showing how different objectives affect graph properties.

The paper develops CouGaR-g, a deep learning tool using FCGR and CNNs for accurate and fast clade assignment of SARS-CoV-2 sequences, achieving 96.29% accuracy on GISAID data.

The paper introduces RecGraph, a tool that extends sequence-to-graph alignment by incorporating recombinations, and evaluates its performance on bacterial pangenome graphs.

The paper develops a pipeline called plastic for tumor phylogeny analysis, integrating data simplification, phylogeny inference, and comparison with improved speed and visualization.

Shark removes irrelevant RNA-Seq reads to improve analysis efficiency without affecting results.

The paper introduces SASC, a method for inferring cancer progression from single-cell sequencing data that allows mutation losses, showing high accuracy on simulated and real datasets.

The paper introduces MP3, a similarity measure for tumor phylogenies that handles multiple mutations, and shows it correctly classifies trees on simulated and real data.

MALVIRUS efficiently calls viral variants from sequencing data and generates population SNP catalogs, validated against GISAID data for SARS-CoV-2.

ASGAL aligns RNA-Seq data to a splicing graph to detect novel alternative splicing events, outperforming methods that align to a reference genome.

The paper evaluates whether relaxing the infinite sites assumption improves tumor phylogenies using an ILP-based approach, finding that allowing mutation losses provides better interpretations of real and simulated datasets.

HapCHAT improves haplotype assembly by adapting error thresholds for higher coverage long reads, enhancing accuracy and reducing computational resources.