Pindel-TD: a tandem duplication detector based on a pattern growth approach

IF 11.5 2区生物学 Q1 GENETICS & HEREDITY

Genomics, Proteomics & Bioinformatics Pub Date : 2024-01-23 DOI:10.1093/gpbjnl/qzae008

Xiaofei Yang, Gaoyang Zheng, Peng Jia, Songbo Wang, Kai Ye

引用次数: 0

Abstract Tandem duplication (TD) is a major type of structural variation (SV) that plays an important role in novel gene formation and human diseases. However, TDs are often missed or incorrectly classified as insertions by most modern SV detection methods due to the lack of specialized operation on TD-related mutational signals. Herein, we developed a TD detection module for the Pindel tool, referred to as Pindel-TD, based on a TD-specific pattern growth approach. Pindel-TD is capable of detecting TDs with a wide size range at single nucleotide resolution. Using simulated and real read data from HG002, we demonstrated that Pindel-TD outperforms other leading methods in terms of precision, recall, F1-score, and robustness. Furthermore, by applying Pindel-TD to data generated from the K562 cancer cell line, we identified a TD located at the seventh exon of SAGE1, providing an explanation for its high expression. Pindel-TD is available for non-commercial use at https://github.com/xjtu-omics/pindel.

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Pindel-TD：基于模式增长方法的串联重复检测器

摘要串联重复（TD）是结构变异（SV）的一种主要类型，在新基因形成和人类疾病中发挥着重要作用。然而，由于缺乏对 TD 相关突变信号的专门操作，大多数现代 SV 检测方法经常会遗漏 TD 或将其错误地归类为插入。在此，我们为 Pindel 工具开发了一个 TD 检测模块，称为 Pindel-TD，它基于一种 TD 特异性模式生长方法。Pindel-TD 能够以单核苷酸分辨率检测出大范围的 TD。我们使用 HG002 的模拟和真实读数数据证明，Pindel-TD 在精确度、召回率、F1-分数和稳健性方面都优于其他领先方法。此外，通过将 Pindel-TD 应用于 K562 癌细胞系产生的数据，我们发现了位于 SAGE1 第七外显子的 TD，为其高表达提供了解释。Pindel-TD 可在 https://github.com/xjtu-omics/pindel 网站上供非商业使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry

CiteScore

14.30

自引率

4.20%

发文量

844

审稿时长

61 days

期刊介绍： Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.