Simone Ciccolella

Somatic Structural Variant Discovery from Tumor Specific Strings Computed Against a Draft Composite Germline Reference

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.

PanSpace: Fast and Scalable Indexing for Massive Bacterial Databases

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

Differential Analysis of Alternative Splicing Events in gene regions using Residual Neural Networks

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.

Differential quantification of alternative splicing events on spliced pangenome graphs

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.

PangeBlocks: customized construction of pangenome graphs via maximal blocks

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

Accurate and Fast Clade Assignment via Deep Learning and Frequency Chaos Game Representation

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.

RecGraph: adding recombinations to sequence-to-graph alignments

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

Simpler and Faster Development of Tumor Phylogeny Pipelines

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

Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses

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.

Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels

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: an integrated web application for viral variant calling

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

PhISCS - A Combinatorial Approach for Sub-perfect Tumor Phylogeny Reconstruction via Integrative use of Single Cell and Bulk Sequencing Data

PhISCS integrates single-cell and bulk sequencing data to reconstruct sub-perfect tumor phylogenies by minimizing false negatives, false positives, and ISA violations.

Does relaxing the infinite sites assumption give better tumor phylogenies? An ILP-based comparative approach

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.