Institute of Medical Informatics, University of Münster, Münster, Germany.
Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Bioinformatics. 2018 Dec 15;34(24):4205-4212. doi: 10.1093/bioinformatics/bty518.
The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation.
We developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8's performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach.
Appreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411).
Supplementary data are available at Bioinformatics online.
下一代测序在研究中的应用，尤其是在临床常规中，需要有效的变异调用结果。然而，对几种常用工具的评估指出，没有一种工具能够满足这一要求。假阳性和假阴性调用需要额外的实验和广泛的人工工作。不同工具的智能组合和输出过滤可以显著改善当前的情况。
我们开发了 appreci8，这是一种通过组合和过滤 8 种开源变异调用工具的输出，基于新的伪影和多态性评分，用于调用单核苷酸变异和短插入缺失的自动变异调用管道。Appreci8 是在两个来自骨髓增生异常综合征患者的数据集上进行训练的，涵盖了 165 个 Illumina 样本。随后，在五个独立的数据集上测试了 appreci8 的性能，涵盖了 513 个样本。考虑了测序平台、目标区域和疾病实体的变化。所有的调用都通过在同一平台、不同平台或基于专家的审查上重新测序进行了验证。Appreci8 的敏感性在 0.93 到 1.00 之间，而阳性预测值在 0.65 到 1.00 之间。在所有情况下，Appreci8 的表现都优于任何评估的替代方法。
Appreci8 可在 https://hub.docker.com/r/wwuimi/appreci8/ 免费获得。使用 appreci8 分析的 678 名患者的测序数据（BAM 文件）已被存入 NCBI 序列读取档案（生物项目 ID：388411；https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411）。
补充数据可在《生物信息学》在线获取。
