Our tools are hosted at the laboratory GitHub webpage. We use the issues tracking system to provide support to users that experience challenges in using our tools.

In preparation

evoverse.datasets - Data package

Input datasets and computation results from a number of packages that we have developed.

lineaGT/pylineagt - Bayesian multi-lineage inference from gene therapy assays

Multi-lineage inference from Gene Therapy assays that use insertional mutagenesis and somatic mutations to track clonal expansions in the hematopoietic compartment. Leveraging Bayesian models,Pyro implementation and stochastic variational inference (double package)

tapacloth - Classification of cancer mutation clonality and zygosity from target sequencing 

A model that uses panel read counts data to determine the clonality status of tumour mutations while estimating their zygosity, fundamental to understand the complex interplay between somatic mutations and aneuploidy from panel data.

basilica/pybasilica - Bayesian somatic signature learning with a catalogue

A semi-supervised Bayesian hierarchical model to detect somatic signatures using a catalogue, with a Pyro implementation and stochastic variational inference (double package).

Stable releases

A Copy Number Alterations quality check package to assess the concordance between clonal and subclonal copy number segments and somatic mutations called from bulk sequencing.

A method to determine the contamination of a tumour sample by normal cells, which can be used to quality control tumour-normal designs and confirm that normal samples are not contaminated by tumour variants.

A Bayesian method to call Copy Number Alterations from single-cell RNA and ATAC sequencing data, supporting multi-omics assays and cell clustering.

Tumour subclonal deconvolution from whole-genome DNA sequencing, exploiting joint Machine Learning and Population Genetics, allowing to determine the number of clones that can be associated to selection forces.

Subclonal deconvolution of read counts from multiple bulk sequencing biopsies, using a variational Bayesian approach.

Univariate Binomial and  Beta-Binomial mixture models for subclonal deconvolution of read counts from bulk assays. Differently from VIBER this package works with a single biopsy and a maximum-likelihood approach.

A model to infer repeated evolution from a cohort of multi-region samples of multiple tumour patients, with functions to stratify the cohort and determine evolutionary subgroups of tumours that evolve with different patterns.

Clones trees from multi-region bulk sequencing data, built of Cancer Cell Fractions clusters computed by subclonal deconvolution algorithms.

Mutation trees from multi-region bulk or single-cell sequencing data, where the presence or absence of a somatic mutation, a Copy Number event, or any other event available in binary format.