Evidence map›Paper›PMID 35804300›Full record

ArticleBMC bioinformatics2022

J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments.

Fabrizio Angaroni, Alessandro Guidi, Gianluca Ascolani, Alberto d'Onofrio, Marco Antoniotti, Alex Graudenzi

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Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Fabrizio Angaroni *Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy. fabrizio.angaroni@unimib.it.ORCID http://orcid.org/0000-0002-3375-6686
Alessandro GuidiDept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.
Gianluca AscolaniDept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.
Alberto d'OnofrioDepartment of Mathematics and Geosciences, Univ. of Trieste, Trieste, Italy.
Marco AntoniottiDept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.
Alex Graudenzi *Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.

Funding

Cancer Research UK 22790Università degli Studi di Milano-Bicocca Bicocca 2020 Starting Grant
6 · The paper itself

Abstract

backgroundThe 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.

resultWe 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.

conclusionJ-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 .

Indexed as

NeoplasmsSoftwareComputer SimulationHigh-Throughput Nucleotide SequencingHumansPhylogenyCancer EvolutionNext-generation sequencingSpatial dynamicsStochastic Simulation

Identifiers

PMID35804300
PMCPMC9270769

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.