Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patients (cross-sectional data) have been defined in the literature since late the 90s. These algorithms differ in the way they extract a graphical model of the events modelling the progression, e.g., somatic mutations or copy-number alterations. TRONCO is an R package for TRanslational ONcology which provides a series of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results, and visualize the models. Availability. Freely available at http://www.bioconductor.org/ under GPL license; project hosted at http://bimib.disco.unimib.it/ and https://github.com/BIMIB-DISCo/TRONCO. Contact. tronco@disco.unimib.it
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For attribution, please cite this work as
Antoniotti, et al., "Design of the TRONCO BioConductor Package for TRanslational ONCOlogy", The R Journal, 2016
BibTeX citation
@article{RJ-2016-032,
author = {Antoniotti, Marco and Caravagna, Giulio and Sano, Luca De and Graudenzi, Alex and Mauri, Giancarlo and Mishra, Bud and Ramazzotti, Daniele},
title = {Design of the TRONCO BioConductor Package for TRanslational ONCOlogy},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-032},
doi = {10.32614/RJ-2016-032},
volume = {8},
issue = {2},
issn = {2073-4859},
pages = {39-59}
}