Open-source methods
Software & tools
The Translational Bioinformatics Laboratory (TBL) develops open-source and web-based tools for studying cancer evolution, mutational processes, multi-omics integration and translational bioinformatics.
Most of our software is released as R or R/Matlab packages and is available on public repositories; selected clinical and translational models are also exposed through web-based calculators and route viewers.
Tool categories
Evolution & progression models Mutational signatures & processes Multi-omics integration & stratification Translational tools Other software
Evolution & progression models
ASCETIC
Toolkit for the inference of evolutionary signatures from cancer genomic data, identifying recurrent temporal patterns and selective constraints.
Domain: cancer evolution
Language: R
LACE / LACE 2.0
Tools for reconstructing longitudinal cancer evolution from single-cell sequencing, integrating clonal structure and temporal dynamics.
Domain: longitudinal evolution
Language: R
PMCE
Efficient inference of expressive cancer evolution models with prognostic power, capturing complex progression trajectories.
Domain: cancer evolution
Language: R
TRONCO
Inference of cancer progression models from heterogeneous genomic datasets, supporting cross-sectional and multi-region data.
Domain: progression models
Language: R
VERSO
Framework for robust phylogeny inference and quantification of intra-host genomic diversity, originally developed for SARS-CoV-2.
Domain: viral evolution
Language: R
Mutational signatures & processes
RESOLVE
Toolkit for robust estimation and decomposition of mutational signatures in large cancer cohorts.
Domain: mutational signatures
Language: R
SparseSignatures
LASSO-regularised NMF framework to identify sparse and stable mutational signatures from large tumour datasets.
Domain: mutational signatures
Language: R
VirMutSig
Pipeline for identifying viral mutational signatures from sequencing data, including SARS-CoV-2 samples.
Domain: viral signatures
Language: R
Multi-omics integration & stratification
ASTUTE
Integrative tool for genotype–phenotype mapping using cancer genomic and transcriptomic data, aimed at identifying biologically coherent patient subgroups and expression signatures.
Domain: multi-omics, genotype–phenotype mapping
Language: R
CIMLR
Integrative clustering of multi-omics profiles using joint similarity learning across data types.
Domain: multi-omics
Languages: R, Matlab
SIMLR
Similarity learning framework for large-scale transcriptomic and single-cell data, enabling robust clustering and visualisation.
Domain: clustering, similarity
Languages: R, Matlab
Translational tools
OncoScore
Tool to quantify the oncogenic potential of genes based on literature evidence, supporting gene prioritisation in sequencing studies.
Domain: gene prioritisation
Language: R
ProgEvo / IPSS-M-Evo
Evolution-informed framework and web-based score for myelodysplastic syndromes, extending IPSS-M with directional mutation trajectories and molecular evolution features.
Domain: MDS, clonal evolution, prognostic modelling
Type: web-based calculator and evolutionary route viewer
IPSS-M-Evo calculator →
Evolution graphs →
NEJM Evidence paper →
Other software
Additional repositories, experimental code and pipelines are available on our GitHub: