Background: Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. Results: The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. Conclusions: All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

Ascolani, G., Angaroni, F., Maspero, D., Craighero, F., Bhavesh, N., Piazza, R., et al. (2023). LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. BMC BIOINFORMATICS, 24(1) [10.1186/s12859-023-05221-3].

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution

Ascolani, Gianluca
Primo
;
Angaroni, Fabrizio
Secondo
;
Maspero, Davide;Craighero, Francesco;Piazza, Rocco;Damiani, Chiara;Ramazzotti, Daniele;Antoniotti, Marco
Penultimo
;
Graudenzi, Alex
Ultimo
2023

Abstract

Background: Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. Results: The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. Conclusions: All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.
Articolo in rivista - Articolo scientifico
Clonal analysis; Interface; Longitudinal; Shiny; Single cell;
English
17-mar-2023
2023
24
1
99
open
Ascolani, G., Angaroni, F., Maspero, D., Craighero, F., Bhavesh, N., Piazza, R., et al. (2023). LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. BMC BIOINFORMATICS, 24(1) [10.1186/s12859-023-05221-3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/406055
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