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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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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. |
format | Online Article Text |
id | pubmed-9825741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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