■串联复制(TD)是人类基因组中常见且重要的结构变异类型。TDs已被证明在许多疾病中起着至关重要的作用,包括癌症.然而,由于读段的不均匀分布和下一代测序(NGS)数据的固有复杂性,很难准确检测TD.
■本文提出了一种称为DTDHM(基于混合方法的串联重复检测)的方法,它利用NGS数据来检测单个样品中的TD。DTDHM构建了一个集成读取深度(RD)的管道,拆分读取(SR),和配对端映射(PEM)信号。为解决正态和异常样本分布不均的问题,DTDHM使用K最近邻(KNN)算法进行多特征分类预测。然后,提取并分析合格的分裂读段和不一致读段,实现变异位点的准确定位。本文将DTDHM与其他三种方法在450个模拟数据集和五个真实数据集上进行了比较。
■在450个模拟数据样本中,DTDHM始终保持最高的F1得分。DTDHM的平均F1分数,SVIM,TARDIS,TIDDIT为80.0%,56.2%,43.4%,和67.1%,分别。DTDHM的F1评分变化范围小,其检测效果最稳定,是次优方法的1.2倍。DTDHM的大多数边界偏差在20bp左右波动,其边界偏差检测能力优于TARDIS和TIDDIT。在真实数据实验中,使用五个真实测序样品(NA19238、NA19239、NA19240、HG00266和NA12891)来测试DTDHM。结果表明,DTDHM在四种方法中具有最高的重叠密度得分(ODS)和F1得分。
■与其他三种方法相比,DTDHM在灵敏度方面取得了优异的结果,精度,F1分数,和边界偏差。这些结果表明,DTDHM可以用作从NGS数据中检测TD的可靠工具,特别是在低覆盖深度和肿瘤纯度样品的情况下。
UNASSIGNED: Tandem duplication (TD) is a common and important type of structural variation in the human genome. TDs have been shown to play an essential role in many diseases, including cancer. However, it is difficult to accurately detect TDs due to the uneven distribution of reads and the inherent complexity of next-generation sequencing (NGS) data.
UNASSIGNED: This article proposes a method called DTDHM (detection of tandem duplications based on hybrid methods), which utilizes NGS data to detect TDs in a single sample. DTDHM builds a pipeline that integrates read depth (RD), split read (SR), and paired-end mapping (PEM) signals. To solve the problem of uneven distribution of normal and abnormal samples, DTDHM uses the K-nearest neighbor (KNN) algorithm for multi-feature classification prediction. Then, the qualified split reads and discordant reads are extracted and analyzed to achieve accurate localization of variation sites. This article compares DTDHM with three other methods on 450 simulated datasets and five real datasets.
UNASSIGNED: In 450 simulated data samples, DTDHM consistently maintained the highest F1-score. The average F1-score of DTDHM, SVIM, TARDIS, and TIDDIT were 80.0%, 56.2%, 43.4%, and 67.1%, respectively. The F1-score of DTDHM had a small variation range and its detection effect was the most stable and 1.2 times that of the suboptimal method. Most of the boundary biases of DTDHM fluctuated around 20 bp, and its boundary deviation detection ability was better than TARDIS and TIDDIT. In real data experiments, five real sequencing samples (NA19238, NA19239, NA19240, HG00266, and NA12891) were used to test DTDHM. The results showed that DTDHM had the highest overlap density score (ODS) and F1-score of the four methods.
UNASSIGNED: Compared with the other three methods, DTDHM achieved excellent results in terms of sensitivity, precision, F1-score, and boundary bias. These results indicate that DTDHM can be used as a reliable tool for detecting TDs from NGS data, especially in the case of low coverage depth and tumor purity samples.