PDF(Pubmed)
Abstract：
Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.
摘要：
结构变异体(SV)是一种重要类型的遗传变异体，可以显着影响表型。因此,SVs的鉴定是现代基因组分析的重要组成部分。在这篇文章中,我们介绍Kled,一个超快速和敏感的SV调用长读测序数据给出了一个特殊设计的方法与一个新的签名合并算法，自定义细化策略和高性能程序结构。评估结果表明，与针对不同平台和测序深度的模拟和真实长读数数据的几种最新方法相比，kled可以实现最佳SV调用。此外，kled擅长快速SV调用，可以有效地利用多个中央处理器(CPU)内核，同时保持低内存使用率。kled的源代码可以从https://github.com/CoREse/kled获得。
