从长读数中检测结构变异并进行基因分型的图聚类算法。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Nicolás Gaitán, Jorge Duitama
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引用次数: 0

摘要

背景:结构变异(SV)是由其长度(大于 50 bp)定义的基因组多态性。SV 的常见类型有缺失、插入、易位、倒位和拷贝数变异。鉴于 SV 在表型变异和进化事件等现象中的作用,SV 的检测和基因分型至关重要。因此,最近开发出了利用长线程测序数据识别 SV 的方法:我们提出了一种准确、高效的算法,用于从长读序测序数据中预测种系SV。该算法首先从读数比对中收集 SV 的证据(特征)。然后,根据长度和基因组位置计算出的坐标欧几里得图对特征进行聚类。聚类是通过 DBSCAN 算法进行的,该算法具有高分辨率划分聚类的优势。聚类被转化为 SV,贝叶斯模型可根据 SV 的支持证据对 SV 进行精确的基因分型。该算法已被集成到下一代测序体验平台的单样本变异检测器中,从而促进了与其他基因组学分析功能的集成。我们进行了多个基准实验,包括模拟和真实数据,代表了不同的基因组图谱、测序技术(PacBio HiFi、ONT)和读取深度:结果表明,在种系 SV 调用和基因分型方面,我们的方法优于最先进的工具,尤其是在低深度和易出错的重复区域。我们相信,这项工作将极大地促进生物信息学策略的发展,从而最大限度地利用长读数测序技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A graph clustering algorithm for detection and genotyping of structural variants from long reads.

Background: Structural variants (SVs) are genomic polymorphisms defined by their length (>50 bp). The usual types of SVs are deletions, insertions, translocations, inversions, and copy number variants. SV detection and genotyping is fundamental given the role of SVs in phenomena such as phenotypic variation and evolutionary events. Thus, methods to identify SVs using long-read sequencing data have been recently developed.

Findings: We present an accurate and efficient algorithm to predict germline SVs from long-read sequencing data. The algorithm starts collecting evidence (signatures) of SVs from read alignments. Then, signatures are clustered based on a Euclidean graph with coordinates calculated from lengths and genomic positions. Clustering is performed by the DBSCAN algorithm, which provides the advantage of delimiting clusters with high resolution. Clusters are transformed into SVs and a Bayesian model allows to precisely genotype SVs based on their supporting evidence. This algorithm is integrated into the single sample variants detector of the Next Generation Sequencing Experience Platform, which facilitates the integration with other functionalities for genomics analysis. We performed multiple benchmark experiments, including simulation and real data, representing different genome profiles, sequencing technologies (PacBio HiFi, ONT), and read depths.

Conclusion: The results show that our approach outperformed state-of-the-art tools on germline SV calling and genotyping, especially at low depths, and in error-prone repetitive regions. We believe this work significantly contributes to the development of bioinformatic strategies to maximize the use of long-read sequencing technologies.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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