Name: Translational Bioinformatics Laboratory
Type: Organization
Bio: The Translational Bioinformatics Laboratory at the Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy
Twitter: dany9685
Location: Italy
Translational Bioinformatics Laboratory's Projects
Welcome to the Translational Bioinformatics Laboratory (TBL) at the Department of Medicine and Surgery of the University of Milano-Bicocca, Milano, Italy!
ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) is a novel framework for the inference of a set of statistically significant temporal patterns involving alternations in driver genes from cancer genomics data. Preprint at https://www.nature.com/articles/s41467-023-41670-3
Implementations in both Matlab and R of the CIMLR method. The manuscript of the method is available at: https://www.nature.com/articles/s41467-018-06921-8
H-ESBCNs (Hidden Extended Suppes-Bayes Causal Networks). The manuscript of the method is available at: https://academic.oup.com/bioinformatics/article/38/3/754/6396863
LACE 2.0 is a new release of the LACE R Bioconductor package, which provides an interactive user interface to perform clonal evolution analyses for single-cell sequencing data, including longitudinal experiments. The tool also allows to annotate and retrieve the relevant variants based on user-defined criteria.
OncoScore is a tool to measure the association of genes to cancer based on citation frequency in biomedical literature. The manuscript of the method is published on Scientific Reports and available at http://www.nature.com/articles/srep46290. The tool can also be used as a Web interface at https://www.galseq.com/next-generation-sequencing/oncoscore
GitHub repository of the PMCE Framework. F. Angaroni, K. Chen, C. Damiani, G. Caravagna, A. Graudenzi, D. Ramazotti, PMCE: efficient inference of expressive models of cancer evolution with high prognostic power, Bioinformatics, 2021; btab717, https://doi.org/10.1093/bioinformatics/btab717
RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization) is an R package for the the efficient extraction and assignment of mutational signatures from cancer genomes.
Implementations in both Matlab and R of the SIMLR method. The manuscript of the method is available at: https://www.nature.com/articles/nmeth.4207
Extracting mutational signatures via LASSO. The manuscript of the method is published on PLOS Computational Biology and available at: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009119
Source code and input data to reproduce the case studies presented in the TRaIT framework: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2795-4
Repository of the TRanslational ONCOlogy library, which includes various algorithms (such as CAPRESE and CAPRI) and the Pipeline for Cancer Inference (PICNIC).
Viral Evolution ReconStructiOn (VERSO). The manuscript of the method is available at: https://www.cell.com/patterns/fulltext/S2666-3899(21)00022-2
The VirMutSig protocol is available at: Maspero, D. et al. "VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data", STAR Protocols 2.4 (2021): 100911. https://doi.org/10.1016/j.xpro.2021.100911