LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution
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,...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05221-3 |
| _version_ | 1827707971411378176 |
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| author | Gianluca Ascolani Fabrizio Angaroni Davide Maspero Francesco Craighero Narra Lakshmi Sai Bhavesh Rocco Piazza Chiara Damiani Daniele Ramazzotti Marco Antoniotti Alex Graudenzi |
| author_facet | Gianluca Ascolani Fabrizio Angaroni Davide Maspero Francesco Craighero Narra Lakshmi Sai Bhavesh Rocco Piazza Chiara Damiani Daniele Ramazzotti Marco Antoniotti Alex Graudenzi |
| author_sort | Gianluca Ascolani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:57:07Z |
| format | Article |
| id | doaj.art-a9d04b1a7dec40fd9e89ecb67e866a82 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-10T16:57:07Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a9d04b1a7dec40fd9e89ecb67e866a822023-11-20T11:06:38ZengBMCBMC Bioinformatics1471-21052023-03-0124111710.1186/s12859-023-05221-3LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolutionGianluca Ascolani0Fabrizio Angaroni1Davide Maspero2Francesco Craighero3Narra Lakshmi Sai Bhavesh4Rocco Piazza5Chiara Damiani6Daniele Ramazzotti7Marco Antoniotti8Alex Graudenzi9Department of Informatics, Systems and Communication, University of Milan-BicoccaDepartment of Informatics, Systems and Communication, University of Milan-BicoccaDepartment of Informatics, Systems and Communication, University of Milan-BicoccaDepartment of Informatics, Systems and Communication, University of Milan-BicoccaDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS)Department of Medicine and Surgery, University of Milan-Bicocca, University of Milan-BicoccaDepartment of Biotechnology and Biosciences, University of Milan-BicoccaDepartment of Medicine and Surgery, University of Milan-Bicocca, University of Milan-BicoccaDepartment of Informatics, Systems and Communication, University of Milan-BicoccaDepartment of Informatics, Systems and Communication, University of Milan-BicoccaAbstract 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.https://doi.org/10.1186/s12859-023-05221-3Single cellLongitudinalClonal analysisShinyInterface |
| spellingShingle | Gianluca Ascolani Fabrizio Angaroni Davide Maspero Francesco Craighero Narra Lakshmi Sai Bhavesh Rocco Piazza Chiara Damiani Daniele Ramazzotti Marco Antoniotti Alex Graudenzi LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution BMC Bioinformatics Single cell Longitudinal Clonal analysis Shiny Interface |
| title | LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution |
| title_full | LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution |
| title_fullStr | LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution |
| title_full_unstemmed | LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution |
| title_short | LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution |
| title_sort | lace 2 0 an interactive r tool for the inference and visualization of longitudinal cancer evolution |
| topic | Single cell Longitudinal Clonal analysis Shiny Interface |
| url | https://doi.org/10.1186/s12859-023-05221-3 |
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