Genome Competence Center, Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany.
Viruses. 2024 Sep 11;16(9):1444. doi: 10.3390/v16091444.
The identification of genomic variants has become a routine task in the age of genome sequencing. In particular, small genomic variants of a single or few nucleotides are routinely investigated for their impact on an organism's phenotype. Hence, the precise and robust detection of the variants' exact genomic locations and changes in nucleotide composition is vital in many biological applications. Although a plethora of methods exist for the many key steps of variant detection, thoroughly testing the detection process and evaluating its results is still a cumbersome procedure. In this work, we present a collection of easy-to-apply and highly modifiable workflows to facilitate the generation of synthetic test data, as well as to evaluate the accordance of a user-provided set of variants with the test data. The workflows are implemented in Nextflow and are open-source and freely available on Github under the GPL-3.0 license.
在基因组测序时代，鉴定基因组变异已成为一项常规任务。特别是，人们通常会研究单个或少数核苷酸的小基因组变异，以了解它们对生物体表型的影响。因此，精确而稳健地检测变异的确切基因组位置和核苷酸组成的变化在许多生物学应用中至关重要。尽管存在许多用于变异检测的关键步骤的方法，但彻底测试检测过程并评估其结果仍然是一个繁琐的过程。在这项工作中，我们提出了一系列易于应用且高度可修改的工作流程，以方便生成合成测试数据，并评估用户提供的一组变体与测试数据的一致性。这些工作流程是在 Nextflow 中实现的，并且是开源的，并在 Github 上以 GPL-3.0 许可证免费提供。
