Diagnostics of viral infections using high-throughput genome sequencing data.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Haochen Ning, Ian Boyes, Ibrahim Numanagić, Michael Rott, Li Xing, Xuekui Zhang
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引用次数: 0

Abstract

Plant viral infections cause significant economic losses, totalling $350 billion USD in 2021. With no treatment for virus-infected plants, accurate and efficient diagnosis is crucial to preventing and controlling these diseases. High-throughput sequencing (HTS) enables cost-efficient identification of known and unknown viruses. However, existing diagnostic pipelines face challenges. First, many methods depend on subjectively chosen parameter values, undermining their robustness across various data sources. Second, artifacts (e.g. false peaks) in the mapped sequence data can lead to incorrect diagnostic results. While some methods require manual or subjective verification to address these artifacts, others overlook them entirely, affecting the overall method performance and leading to imprecise or labour-intensive outcomes. To address these challenges, we introduce IIMI, a new automated analysis pipeline using machine learning to diagnose infections from 1583 plant viruses with HTS data. It adopts a data-driven approach for parameter selection, reducing subjectivity, and automatically filters out regions affected by artifacts, thus improving accuracy. Testing with in-house and published data shows IIMI's superiority over existing methods. Besides a prediction model, IIMI also provides resources on plant virus genomes, including annotations of regions prone to artifacts. The method is available as an R package (iimi) on CRAN and will integrate with the web application www.virtool.ca, enhancing accessibility and user convenience.

利用高通量基因组测序数据诊断病毒感染。
植物病毒感染造成了巨大的经济损失,2021 年的损失总额将达到 3500 亿美元。由于受病毒感染的植物无法得到治疗,准确有效的诊断对预防和控制这些疾病至关重要。高通量测序 (HTS) 可以经济高效地鉴定已知和未知病毒。然而,现有的诊断管道面临着挑战。首先,许多方法依赖于主观选择的参数值,这就削弱了它们在不同数据源中的稳健性。其次,映射序列数据中的伪影(如假峰)会导致不正确的诊断结果。有些方法需要人工或主观验证来处理这些伪影,而有些方法则完全忽略了它们,从而影响了方法的整体性能,导致结果不精确或劳动密集型结果。为了应对这些挑战,我们引入了 IIMI,这是一种利用机器学习诊断 1583 种植物病毒感染的 HTS 数据的新型自动分析管道。它采用数据驱动的方法进行参数选择,减少了主观性,并自动过滤掉受伪影影响的区域,从而提高了准确性。利用内部数据和已发表数据进行的测试表明,IIMI 优于现有方法。除预测模型外,IIMI 还提供了植物病毒基因组资源,包括易受人工影响区域的注释。该方法以 R 软件包(iimi)的形式在 CRAN 上提供,并将与网络应用程序 www.virtool.ca 集成,以提高可访问性和用户便利性。
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
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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