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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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Detalles Bibliográficos
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
Descripción
Sumario: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.