Laboratorio Data and Computational BiologyLaboratorio Data and Computational BiologyThe 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 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.
Last update on October 4th, 2023 at 10:25 am
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