FindCSV: a long-read based method for detecting complex structural variations.

IF 2.9 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2024-09-28 DOI:10.1186/s12859-024-05937-w

Yan Zheng, Xuequn Shang

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引用次数: 0

Abstract

Background: Structural variations play a significant role in genetic diseases and evolutionary mechanisms. Extensive research has been conducted over the past decade to detect simple structural variations, leading to the development of well-established detection methods. However, recent studies have highlighted the potentially greater impact of complex structural variations on individuals compared to simple structural variations. Despite this, the field still lacks precise detection methods specifically designed for complex structural variations. Therefore, the development of a highly efficient and accurate detection method is of utmost importance.

Result: In response to this need, we propose a novel method called FindCSV, which leverages deep learning techniques and consensus sequences to enhance the detection of SVs using long-read sequencing data. Compared to current methods, FindCSV performs better in detecting complex and simple structural variations.

Conclusions: FindCSV is a new method to detect complex and simple structural variations with reasonable accuracy in real and simulated data. The source code for the program is available at https://github.com/nwpuzhengyan/FindCSV .

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FindCSV：基于长读取的复杂结构变异检测方法。

背景：结构变异在遗传疾病和进化机制中发挥着重要作用。在过去的十年中，人们对简单结构变异的检测进行了广泛的研究，从而开发出了成熟的检测方法。然而，最近的研究强调，与简单结构变异相比，复杂结构变异对个体的潜在影响更大。尽管如此，该领域仍然缺乏专门针对复杂结构变异的精确检测方法。因此，开发一种高效、准确的检测方法至关重要：针对这一需求，我们提出了一种名为 "FindCSV "的新方法，该方法利用深度学习技术和共识序列来提高利用长读程测序数据检测 SV 的能力。与现有方法相比，FindCSV 在检测复杂和简单结构变异方面表现更好：FindCSV是一种在真实和模拟数据中检测复杂和简单结构变异的新方法，具有合理的准确性。该程序的源代码可在 https://github.com/nwpuzhengyan/FindCSV 上获取。

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

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.