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J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments
BACKGROUND: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270769/ https://www.ncbi.nlm.nih.gov/pubmed/35804300 http://dx.doi.org/10.1186/s12859-022-04779-8 |
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author | Angaroni, Fabrizio Guidi, Alessandro Ascolani, Gianluca d’Onofrio, Alberto Antoniotti, Marco Graudenzi, Alex |
author_facet | Angaroni, Fabrizio Guidi, Alessandro Ascolani, Gianluca d’Onofrio, Alberto Antoniotti, Marco Graudenzi, Alex |
author_sort | Angaroni, Fabrizio |
collection | PubMed |
description | BACKGROUND: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. RESULT: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. CONCLUSION: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl. |
format | Online Article Text |
id | pubmed-9270769 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92707692022-07-10 J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments Angaroni, Fabrizio Guidi, Alessandro Ascolani, Gianluca d’Onofrio, Alberto Antoniotti, Marco Graudenzi, Alex BMC Bioinformatics Software BACKGROUND: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. RESULT: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. CONCLUSION: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl. BioMed Central 2022-07-08 /pmc/articles/PMC9270769/ /pubmed/35804300 http://dx.doi.org/10.1186/s12859-022-04779-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Angaroni, Fabrizio Guidi, Alessandro Ascolani, Gianluca d’Onofrio, Alberto Antoniotti, Marco Graudenzi, Alex J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments |
title | J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments |
title_full | J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments |
title_fullStr | J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments |
title_full_unstemmed | J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments |
title_short | J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments |
title_sort | j-space: a julia package for the simulation of spatial models of cancer evolution and of sequencing experiments |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270769/ https://www.ncbi.nlm.nih.gov/pubmed/35804300 http://dx.doi.org/10.1186/s12859-022-04779-8 |
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