Riccardo Bergamin

Tumour evolution as ground truth for cancer whole-genome sequencing

This paper introduces SCOUT, a synthetic whole-genome sequencing resource for benchmarking cancer genomic analysis, revealing performance issues in complex tumours and highlighting the importance of evolutionary ground truth.

BASCULE: Bayesian inference and clustering of mutational signatures leveraging biological priors

BASCULE uses a Bayesian framework to standardize and cluster mutational signatures across cancer types, identifying prognostic subtypes in over 8,000 samples.

Timing and clustering co-occurring genome amplifications incancers

This paper introduces TickTack, a Bayesian model to time and cluster co-occurring genome amplifications in cancer, revealing insights into tumor evolution and genomic instability.

Predicting tumour evolution and drug resistance from heterogenous longitudinal cancer data

This paper introduces a Bayesian framework to predict tumour evolution and drug resistance using longitudinal cancer data across three cancer types.

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

CONGAS+ is a Bayesian method that maps single-cell RNA and ATAC data to copy number clones, revealing subclonal architectures and genotype-phenotype relationships in cancer.

A Bayesian method to cluster single-cell RNA sequencing data using Copy Number Alterations

CONGAS clusters single-cell RNA sequencing data using copy number alterations to infer subclonal composition and RNA phenotypes in cancer data.

Computational validation of clonal and subclonal copy number alterations from bulk tumour sequencing

This paper introduces CNAqc, a toolset for validating clonal and subclonal copy number alterations in bulk tumor sequencing by addressing tumor purity, ploidy, and heterogeneity using simulations and single-cell data.