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Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
BACKGROUND: A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485126/ https://www.ncbi.nlm.nih.gov/pubmed/31023236 http://dx.doi.org/10.1186/s12859-019-2795-4 |
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author | Ramazzotti, Daniele Graudenzi, Alex De Sano, Luca Antoniotti, Marco Caravagna, Giulio |
author_facet | Ramazzotti, Daniele Graudenzi, Alex De Sano, Luca Antoniotti, Marco Caravagna, Giulio |
author_sort | Ramazzotti, Daniele |
collection | PubMed |
description | BACKGROUND: A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. RESULTS: We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. CONCLUSIONS: We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2795-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6485126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64851262019-05-03 Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data Ramazzotti, Daniele Graudenzi, Alex De Sano, Luca Antoniotti, Marco Caravagna, Giulio BMC Bioinformatics Methodology Article BACKGROUND: A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. RESULTS: We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. CONCLUSIONS: We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2795-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-25 /pmc/articles/PMC6485126/ /pubmed/31023236 http://dx.doi.org/10.1186/s12859-019-2795-4 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Ramazzotti, Daniele Graudenzi, Alex De Sano, Luca Antoniotti, Marco Caravagna, Giulio Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data |
title | Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data |
title_full | Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data |
title_fullStr | Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data |
title_full_unstemmed | Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data |
title_short | Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data |
title_sort | learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485126/ https://www.ncbi.nlm.nih.gov/pubmed/31023236 http://dx.doi.org/10.1186/s12859-019-2795-4 |
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