Simone Zaccaria

Chromosomal instability shapes spatial and temporal phenotypic diversity in a malignant peripheral nerve sheath tumour

This study uses single-cell and spatial multi-omics to analyze a malignant peripheral nerve sheath tumor, revealing chromosomal instability drives phenotypic diversity and spatial-temporal evolution.

Characterizing the Solution Space of Migration Histories of Metastatic Cancers with MACH2

The paper introduces MACH2, a method to systematically enumerate all plausible migration histories of metastatic cancers, outperforming existing methods and detecting extensive uncertainty in real cancer data.

CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data

CNAViz improves copy-number segmentation in DNA sequencing data by combining local and global approaches, achieving better accuracy than existing methods in simulated and real data.

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

This paper introduces DeCiFer, a method to more accurately estimate cancer cell fractions and infer tumor evolution by addressing limitations of existing methods in the presence of copy-number aberrations and mutation losses.

Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data

HATCHet improves CNA and WGD analysis in multi-sample tumor data by jointly inferring clone-specific alterations across samples, outperforming existing methods and revealing new insights into tumor evolution.

Characterizing the allele- and haplotype-specific copy number landscape of cancer genomes at single-cell resolution with CHISEL

The paper introduces CHISEL, a method to infer allele- and haplotype-specific copy numbers in single cells, revealing previously undetected copy-number aberrations in breast cancer genomes.

Single-cell tumor phylogeny inference with copy-number constrained mutation losses

This paper introduces a new method for inferring tumor phylogenies from single-cell sequencing data that accounts for copy-number aberrations, improving accuracy in tumors with such aberrations.

HapCHAT: Adaptive haplotype assembly for efficiently leveraging high coverage in long reads

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