BACKGROUND Accurate identification of real somatic variants is a primary part of cancer genome studies and precision oncology. However, artifacts introduced in various steps of sequencing obfuscate confidence in variant calling. Current computational approaches to variant filtering involve intensive interrogation of Binary Alignment Map (BAM) files and require massive computing power, data storage, and manual labor. Recently, mutational signatures associated with sequencing artifacts have been extracted by the Pan-cancer Analysis of Whole Genomes (PCAWG) study. These spectrums can be used to evaluate refinement quality of a given set of somatic mutations. RESULTS Here we introduce a novel variant refinement software, FIREVAT (FInding REliable Variants without ArTifacts), which uses known spectrums of sequencing artifacts extracted from one of the largest publicly available catalogs of human tumor samples. FIREVAT performs a quick and efficient variant refinement that accurately removes artifacts and greatly improves the precision and specificity of somatic calls. We validated FIREVAT refinement performance using orthogonal sequencing datasets totaling 384 tumor samples with respect to ground truth. Our novel method achieved the highest level of performance compared to existing filtering approaches. Application of FIREVAT on additional 308 The Cancer Genome Atlas (TCGA) samples demonstrated that FIREVAT refinement leads to identification of more biologically and clinically relevant mutational signatures as well as enrichment of sequence contexts associated with experimental errors. FIREVAT only requires a Variant Call Format file (VCF) and generates a comprehensive report of the variant refinement processes and outcomes for the user. CONCLUSIONS In summary, FIREVAT facilitates a novel refinement strategy using mutational signatures to distinguish artifactual point mutations called in human cancer samples. We anticipate that FIREVAT results will further contribute to precision oncology efforts that rely on accurate identification of variants, especially in the context of analyzing mutational signatures that bear prognostic and therapeutic significance. FIREVAT is freely available at https://github.com/cgab-ncc/FIREVAT.

中文翻译：

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FIREVAT：使用病因相关的突变特征，在人类癌症样品中找到可靠的变异体，而没有伪影。

背景技术准确鉴定真正的体细胞变体是癌症基因组研究和精确肿瘤学的主要部分。但是，在测序的各个步骤中引入的工件会混淆对变异调用的信心。当前用于变体过滤的计算方法涉及对二进制对准图（BAM）文件的密集询问，并且需要大量的计算能力，数据存储和人工。最近，通过全基因组全癌分析（PCAWG）研究提取了与测序伪影相关的突变特征。这些光谱可用于评估一组给定的体细胞突变的精炼质量。结果在这里，我们介绍了一种新颖的变体细化软件FIREVAT（找到没有ArTifacts的可靠变体），它使用从人类肿瘤样品最大的公开目录之一中提取的测序伪影的已知光谱。FIREVAT执行快速有效的变体细化，可精确消除伪影，并大大提高了体检的准确性和特异性。我们使用正交测序数据集验证了FIREVAT提纯性能，该数据集总共384个肿瘤样本关于地面真实性。与现有的过滤方法相比，我们的新颖方法实现了最高水平的性能。FIREVAT在另外308个样本上的应用癌症基因组图谱（TCGA）样本表明，FIREVAT的提炼可导致鉴定更多与生物学和临床相关的突变特征，并丰富与实验错误相关的序列背景。FIREVAT仅需要一个变量调用格式文件（VCF），并为用户生成有关变量优化过程和结果的综合报告。结论总而言之，FIREVAT促进了一种新的改进策略，该策略使用突变签名来区分人类癌症样品中称为人为的点突变。我们预计FIREVAT的结果将进一步有助于依靠精确识别变异的精确肿瘤学工作，特别是在分析具有预后和治疗意义的突变特征的情况下。FIREVAT可从https://github.com/cgab-ncc/FIREVAT免费获得。FIREVAT使用突变签名来区分人类癌症样本中称为的人为点突变，从而促进了一种新颖的提炼策略。我们预计FIREVAT的结果将进一步有助于依靠精确识别变异的精确肿瘤学工作，特别是在分析具有预后和治疗意义的突变特征的情况下。FIREVAT可从https://github.com/cgab-ncc/FIREVAT免费获得。FIREVAT使用突变签名来区分人类癌症样本中称为的人为点突变，从而促进了一种新颖的提炼策略。我们预计FIREVAT的结果将进一步有助于依靠精确识别变异的精确肿瘤学工作，特别是在分析具有预后和治疗意义的突变特征的情况下。FIREVAT可从https://github.com/cgab-ncc/FIREVAT免费获得。