Peter MacCallum Cancer Centre, Sarcoma Genetics and Genomics Laboratory, St. Andrew's Place, East Melbourne, Victoria, Australia ; Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia.
Peter MacCallum Cancer Centre, Cancer Genetics Laboratory, St. Andrew's Place, East Melbourne, Victoria, Australia.
Genome Med. 2013 Sep 30;5(9):90. doi: 10.1186/gm494. eCollection 2013.
Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In cases of partial consensus, read depth and mapping quality data, along with additional prediction methods, aided in removing inaccurate predictions. Our consensus approach is fast, flexible and provides a high-confidence list of putative somatic mutations.
从大规模平行测序数据中区分真实的体细胞突变和伪像极具挑战性。为了开发最佳体细胞突变检测方法，并确定影响体细胞突变预测准确性的因素，我们通过 Sanger 测序对三种体细胞突变检测算法（MuTect、JointSNVMix2 和 SomaticSniper）的预测结果进行了验证。完全一致的预测验证率>98%，但部分一致的预测也有一些得到了验证。在部分一致的情况下，通过增加读深度和映射质量数据以及额外的预测方法，可以帮助去除不准确的预测。我们的一致方法快速、灵活，并提供了一组高可信度的疑似体细胞突变列表。
