GoldPolish-target: targeted long-read genome assembly polishing.

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Emily Zhang, Lauren Coombe, Johnathan Wong, René L Warren, Inanç Birol
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引用次数: 0

Abstract

Background: Advanced long-read sequencing technologies, such as those from Oxford Nanopore Technologies and Pacific Biosciences, are finding a wide use in de novo genome sequencing projects. However, long reads typically have higher error rates relative to short reads. If left unaddressed, subsequent genome assemblies may exhibit high base error rates that compromise the reliability of downstream analysis. Several specialized error correction tools for genome assemblies have since emerged, employing a range of algorithms and strategies to improve base quality. However, despite these efforts, many genome assembly workflows still produce regions with elevated error rates, such as gaps filled with unpolished or ambiguous bases. To address this, we introduce GoldPolish-Target, a modular targeted sequence polishing pipeline. Coupled with GoldPolish, a linear-time genome assembly algorithm, GoldPolish-Target isolates and polishes user-specified assembly loci, offering a resource-efficient means for polishing targeted regions of draft genomes.

Results: Experiments using Drosophila melanogaster and Homo sapiens datasets demonstrate that GoldPolish-Target can reduce insertion/deletion (indel) and mismatch errors by up to 49.2% and 55.4% respectively, achieving base accuracy values upwards of 99.9% (Phred score Q > 30). This polishing accuracy is comparable to the current state-of-the-art, Medaka, while exhibiting up to 27-fold shorter run times and consuming 95% less memory, on average.

Conclusion: GoldPolish-Target, in contrast to most other polishing tools, offers the ability to target specific regions of a genome assembly for polishing, providing a computationally light-weight and highly scalable solution for base error correction.

GoldPolish-target:靶向长读基因组组装抛光。
背景:先进的长读测序技术,如牛津纳米孔技术公司和太平洋生物科学公司的技术,在从头基因组测序项目中得到了广泛的应用。然而,相对于短读取,长读取通常具有更高的错误率。如果不进行定位,随后的基因组组装可能会出现高碱基错误率,从而损害下游分析的可靠性。一些专门的错误纠正工具的基因组组装已经出现,采用一系列的算法和策略,以提高碱基质量。然而,尽管有这些努力,许多基因组组装工作流程仍然产生错误率较高的区域,例如填充未修饰或模糊碱基的间隙。为了解决这个问题,我们引入了GoldPolish-Target,一个模块化的目标序列抛光管道。结合线性时间基因组组装算法GoldPolish, GoldPolish- target分离和抛光用户指定的组装位点,提供了一种资源高效的方法来抛光基因组草图的目标区域。结果:使用果蝇和智人数据集进行的实验表明,GoldPolish-Target可将插入/删除(indel)和错配误差分别降低49.2%和55.4%,碱基准确率高达99.9% (Phred评分Q bbb30)。这种抛光精度可与当前最先进的Medaka相媲美,同时平均运行时间缩短27倍,消耗的内存减少95%。结论:与大多数其他抛光工具相比,GoldPolish-Target提供了针对基因组组装的特定区域进行抛光的能力,为碱基误差校正提供了计算轻量级和高度可扩展的解决方案。
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
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.
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