Hayes Matthew, Pearson Jeremy S
Xavier University of Louisiana, 1 Drexel Dr, New Orleans, 70125, LA, USA.
Department of Computer Science, Tennessee State University, 3500 John A. Merritt Blvd., Nashville, 37221, Tennessee, USA.
BMC Bioinformatics. 2017 Oct 16;18(Suppl 12):413. doi: 10.1186/s12859-017-1829-z.
Genomic structural variants (SV) play a significant role in the onset and progression of cancer. Genomic deletions can create oncogenic fusion genes or cause the loss of tumor suppressing gene function which can lead to tumorigenesis by downregulating these genes. Detecting these variants has clinical importance in the treatment of diseases. Furthermore, it is also clinically important to detect their breakpoint boundaries at high resolution. We have generalized the framework of a previously-published algorithm that located translocations, and we have applied that framework to develop a method to locate deletions at base pair level using next-generation sequencing data. Our method uses abnormally mapped read pairs, and then subsequently maps split reads to identify precise breakpoints.
On a primary prostate cancer dataset and a simulated dataset, our method predicted the number, type, and breakpoints of biologically validated SVs at high accuracy. It also outperformed two existing algorithms on precise breakpoint prediction, which is clinically important.
Our algorithm, called Pegasus, accurately calls deletion breakpoints. However, the method must be extended to allow for germline variant filtering and heterozygous deletion detection. The source code that implements Pegasus can be downloaded from the following URL: http://github.com/mhayes20/Pegasus .
基因组结构变异（SV）在癌症的发生和发展中起着重要作用。基因组缺失可产生致癌融合基因或导致肿瘤抑制基因功能丧失，进而通过下调这些基因引发肿瘤发生。检测这些变异在疾病治疗中具有临床重要性。此外，高分辨率检测其断点边界在临床上也很重要。我们推广了一种先前发表的用于定位易位的算法框架，并应用该框架开发了一种利用下一代测序数据在碱基对水平定位缺失的方法。我们的方法使用异常映射的读对，随后映射拆分读段以识别精确断点。
在一个原发性前列腺癌数据集和一个模拟数据集上，我们的方法高精度地预测了经生物学验证的SV的数量、类型和断点。在精确断点预测方面，它也优于两种现有算法，这在临床上很重要。
我们的算法名为Pegasus，能够准确地调用缺失断点。然而，该方法必须扩展以允许进行种系变异过滤和杂合缺失检测。实现Pegasus的源代码可从以下网址下载：http://github.com/mhayes20/Pegasus 。
