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Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data

SUMMARY: Whole-exome and targeted sequencing are widely utilized both in translational cancer genomics and in the setting of precision medicine. The benchmarking of computational methods and tools that are in continuous development is fundamental for the correct interpretation of somatic genomic pro...

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Autores principales: Scandino, Riccardo, Calabrese, Federico, Romanel, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825741/
https://www.ncbi.nlm.nih.gov/pubmed/36484701
http://dx.doi.org/10.1093/bioinformatics/btac792
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author Scandino, Riccardo
Calabrese, Federico
Romanel, Alessandro
author_facet Scandino, Riccardo
Calabrese, Federico
Romanel, Alessandro
author_sort Scandino, Riccardo
collection PubMed
description SUMMARY: Whole-exome and targeted sequencing are widely utilized both in translational cancer genomics and in the setting of precision medicine. The benchmarking of computational methods and tools that are in continuous development is fundamental for the correct interpretation of somatic genomic profiling results. To this aim we developed synggen, a tool for the fast generation of large-scale realistic and heterogeneous cancer whole-exome and targeted sequencing synthetic datasets, which enables the incorporation of phased germline single nucleotide polymorphisms and complex allele-specific somatic genomic events. Synggen performances and effectiveness in generating synthetic cancer data are shown across different scenarios and considering different platforms with distinct characteristics. AVAILABILITY AND IMPLEMENTATION: synggen is freely available at https://bitbucket.org/CibioBCG/synggen/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-98257412023-01-10 Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data Scandino, Riccardo Calabrese, Federico Romanel, Alessandro Bioinformatics Applications Note SUMMARY: Whole-exome and targeted sequencing are widely utilized both in translational cancer genomics and in the setting of precision medicine. The benchmarking of computational methods and tools that are in continuous development is fundamental for the correct interpretation of somatic genomic profiling results. To this aim we developed synggen, a tool for the fast generation of large-scale realistic and heterogeneous cancer whole-exome and targeted sequencing synthetic datasets, which enables the incorporation of phased germline single nucleotide polymorphisms and complex allele-specific somatic genomic events. Synggen performances and effectiveness in generating synthetic cancer data are shown across different scenarios and considering different platforms with distinct characteristics. AVAILABILITY AND IMPLEMENTATION: synggen is freely available at https://bitbucket.org/CibioBCG/synggen/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-12-09 /pmc/articles/PMC9825741/ /pubmed/36484701 http://dx.doi.org/10.1093/bioinformatics/btac792 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Scandino, Riccardo
Calabrese, Federico
Romanel, Alessandro
Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data
title Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data
title_full Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data
title_fullStr Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data
title_full_unstemmed Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data
title_short Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data
title_sort synggen: fast and data-driven generation of synthetic heterogeneous ngs cancer data
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825741/
https://www.ncbi.nlm.nih.gov/pubmed/36484701
http://dx.doi.org/10.1093/bioinformatics/btac792
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