Software Archive

The DCB group has developed several software tools that can be used to analyze and simulate several  datasets.  We use our tools to work mostly on cancer, although more recently we have applied them to analysis of SARS-CoV-2 sequences.


J-SPACE is a Julia package for SPAtial Cancer Evolution. It allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings. Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm.

Available on GitHub

LACE 2.0

LACE 2.0 is an R package to analyze single-cell mutational profiles from longitudinal sequencing experiments of cancer samples via standard pipelines for variant calling to either DNA- or (full-length) RNA- NGS data. LACE 2.0 infers the development of the disease, retrieves a longitudinal clonal tree tagged with coherent time coordinates and includes all sanity checks for the time order logic.

Available on GitHub



pyTSA is a Python tool to make time-series analysis as intuitive as possible.

Its scripts can be pipelined with any simulation tool outputting time-series, and intuitive commands allow to perform complex analyses in a intuitive way.

A brief description of pyTSA’s features is available here; the source code, the project wiki and the examples are hosted on GitHub.


An R suite for state-of-the-art algorithms for the reconstruction of causal models of cancer progressions from genomic cross-sectional data.

Discover more on TRONCO official website.


CABERNET is a Cytoscape app for the generation, the simulation, the analysis and the visualization of Boolean models of gene regulatory networks, particularly focused on the investigation of their robustness.

GESTODifferent – a plugin for Cytoscape for the identification of Boolean gene regulatory networks describing the stochastic differentiation process. Please use CABERNET (it is an evolution of GESTODifferent).

You can find the description of GESTODifferent and the link for download here.