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gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data
BACKGROUND: Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze:...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725124/ https://www.ncbi.nlm.nih.gov/pubmed/33297943 http://dx.doi.org/10.1186/s12859-020-03736-7 |
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author | Ciccolella, Simone Soto Gomez, Mauricio Patterson, Murray D. Della Vedova, Gianluca Hajirasouliha, Iman Bonizzoni, Paola |
author_facet | Ciccolella, Simone Soto Gomez, Mauricio Patterson, Murray D. Della Vedova, Gianluca Hajirasouliha, Iman Bonizzoni, Paola |
author_sort | Ciccolella, Simone |
collection | PubMed |
description | BACKGROUND: Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. RESULTS: We present a new tool, gpps, that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gpps. CONCLUSIONS: gpps provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data. |
format | Online Article Text |
id | pubmed-7725124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77251242020-12-10 gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data Ciccolella, Simone Soto Gomez, Mauricio Patterson, Murray D. Della Vedova, Gianluca Hajirasouliha, Iman Bonizzoni, Paola BMC Bioinformatics Research BACKGROUND: Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. RESULTS: We present a new tool, gpps, that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gpps. CONCLUSIONS: gpps provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data. BioMed Central 2020-12-09 /pmc/articles/PMC7725124/ /pubmed/33297943 http://dx.doi.org/10.1186/s12859-020-03736-7 Text en © The Author(s) 2020 Open Access This 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/. 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 in a credit line to the data. |
spellingShingle | Research Ciccolella, Simone Soto Gomez, Mauricio Patterson, Murray D. Della Vedova, Gianluca Hajirasouliha, Iman Bonizzoni, Paola gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data |
title | gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data |
title_full | gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data |
title_fullStr | gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data |
title_full_unstemmed | gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data |
title_short | gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data |
title_sort | gpps: an ilp-based approach for inferring cancer progression with mutation losses from single cell data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725124/ https://www.ncbi.nlm.nih.gov/pubmed/33297943 http://dx.doi.org/10.1186/s12859-020-03736-7 |
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