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SCNVSim: somatic copy number variation and structure variation simulator

BACKGROUND: Somatically acquired structure variations (SVs) and copy number variations (CNVs) can induce genetic changes that are directly related to tumor genesis. Somatic SV/CNV detection using next-generation sequencing (NGS) data still faces major challenges introduced by tumor sample characteri...

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Autores principales: Qin, Maochun, Liu, Biao, Conroy, Jeffrey M, Morrison, Carl D, Hu, Qiang, Cheng, Yubo, Murakami, Mitsuko, Odunsi, Adekunle O, Johnson, Candace S, Wei, Lei, Liu, Song, Wang, Jianmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349766/
https://www.ncbi.nlm.nih.gov/pubmed/25886838
http://dx.doi.org/10.1186/s12859-015-0502-7
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author Qin, Maochun
Liu, Biao
Conroy, Jeffrey M
Morrison, Carl D
Hu, Qiang
Cheng, Yubo
Murakami, Mitsuko
Odunsi, Adekunle O
Johnson, Candace S
Wei, Lei
Liu, Song
Wang, Jianmin
author_facet Qin, Maochun
Liu, Biao
Conroy, Jeffrey M
Morrison, Carl D
Hu, Qiang
Cheng, Yubo
Murakami, Mitsuko
Odunsi, Adekunle O
Johnson, Candace S
Wei, Lei
Liu, Song
Wang, Jianmin
author_sort Qin, Maochun
collection PubMed
description BACKGROUND: Somatically acquired structure variations (SVs) and copy number variations (CNVs) can induce genetic changes that are directly related to tumor genesis. Somatic SV/CNV detection using next-generation sequencing (NGS) data still faces major challenges introduced by tumor sample characteristics, such as ploidy, heterogeneity, and purity. A simulated cancer genome with known SVs and CNVs can serve as a benchmark for evaluating the performance of existing somatic SV/CNV detection tools and developing new methods. RESULTS: SCNVSim is a tool for simulating somatic CNVs and structure variations SVs. Other than multiple types of SV and CNV events, the tool is capable of simulating important features related to tumor samples including aneuploidy, heterogeneity and purity. CONCLUSIONS: SCNVSim generates the genomes of a cancer cell population with detailed information of copy number status, loss of heterozygosity (LOH), and event break points, which is essential for developing and evaluating somatic CNV and SV detection methods in cancer genomics studies.
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spelling pubmed-43497662015-03-06 SCNVSim: somatic copy number variation and structure variation simulator Qin, Maochun Liu, Biao Conroy, Jeffrey M Morrison, Carl D Hu, Qiang Cheng, Yubo Murakami, Mitsuko Odunsi, Adekunle O Johnson, Candace S Wei, Lei Liu, Song Wang, Jianmin BMC Bioinformatics Software BACKGROUND: Somatically acquired structure variations (SVs) and copy number variations (CNVs) can induce genetic changes that are directly related to tumor genesis. Somatic SV/CNV detection using next-generation sequencing (NGS) data still faces major challenges introduced by tumor sample characteristics, such as ploidy, heterogeneity, and purity. A simulated cancer genome with known SVs and CNVs can serve as a benchmark for evaluating the performance of existing somatic SV/CNV detection tools and developing new methods. RESULTS: SCNVSim is a tool for simulating somatic CNVs and structure variations SVs. Other than multiple types of SV and CNV events, the tool is capable of simulating important features related to tumor samples including aneuploidy, heterogeneity and purity. CONCLUSIONS: SCNVSim generates the genomes of a cancer cell population with detailed information of copy number status, loss of heterozygosity (LOH), and event break points, which is essential for developing and evaluating somatic CNV and SV detection methods in cancer genomics studies. BioMed Central 2015-02-28 /pmc/articles/PMC4349766/ /pubmed/25886838 http://dx.doi.org/10.1186/s12859-015-0502-7 Text en © Qin et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 Software
Qin, Maochun
Liu, Biao
Conroy, Jeffrey M
Morrison, Carl D
Hu, Qiang
Cheng, Yubo
Murakami, Mitsuko
Odunsi, Adekunle O
Johnson, Candace S
Wei, Lei
Liu, Song
Wang, Jianmin
SCNVSim: somatic copy number variation and structure variation simulator
title SCNVSim: somatic copy number variation and structure variation simulator
title_full SCNVSim: somatic copy number variation and structure variation simulator
title_fullStr SCNVSim: somatic copy number variation and structure variation simulator
title_full_unstemmed SCNVSim: somatic copy number variation and structure variation simulator
title_short SCNVSim: somatic copy number variation and structure variation simulator
title_sort scnvsim: somatic copy number variation and structure variation simulator
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349766/
https://www.ncbi.nlm.nih.gov/pubmed/25886838
http://dx.doi.org/10.1186/s12859-015-0502-7
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