Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 36484701.
- Also identified by DOI 10.1093/bioinformatics/btac792 and PMC identifier 9825741.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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. synggen is freely available at https://bitbucket.org/CibioBCG/synggen/. Supplementary data are available at Bioinformatics online.
Medical subject headings
- High-Throughput Nucleotide Sequencing
- Neoplasms