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Updated: Aug 18, 2025

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data
Riccardo Scandino1, Federico Calabrese1, Alessandro Romanel1
1Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy.
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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