Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.
Department of Clinical Medicine, Faculty of Health, Aarhus University, Aarhus, Denmark.
Genome Biol. 2023 Apr 30;24(1):99. doi: 10.1186/s13059-023-02920-1.
Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.
利用下一代测序（NGS）血浆 DNA 数据进行循环肿瘤 DNA 检测有望用于癌症的识别和特征分析。然而，血液中的肿瘤信号通常较低，且难以与误差区分。我们提出了 DREAMS（测序误差的深度读取水平建模），用于估计各个读取位置的错误率。使用 DREAMS，我们开发了用于变体调用（DREAMS-vc）和癌症检测（DREAMS-cc）的统计方法。在评估中，我们从 85 名结直肠癌患者中生成了匹配的肿瘤和血浆 DNA 的深度靶向 NGS 数据。DREAMS 方法在变体调用和癌症检测方面的性能优于最先进的方法。
