HVSeeker:一种基于深度学习的宿主和病毒DNA序列识别方法。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Abdullatif Al-Najim, Sven Hauns, Van Dinh Tran, Rolf Backofen, Omer S Alkhnbashi
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引用次数: 0

摘要

背景:噬菌体是地球上最丰富的生物之一,对生态系统和人类社会产生重大影响。从混合宏基因组中鉴定病毒序列,特别是新序列,是分析宿主样本病毒成分的关键第一步。这在许多下游任务中起着关键作用。然而,由于它们的快速进化速度,这是一项具有挑战性的任务。鉴定过程通常包括两个步骤:从宿主中区分病毒序列,并确定它们是否来自新的病毒基因组。传统的宏基因组技术依赖于与已知实体的序列相似性,特别是在处理短基因组或新基因组时,往往存在不足。与此同时,深度学习在包括生物信息学领域在内的各个领域都显示出了它的功效。结果:我们开发了hvseeker——一种基于深度学习的宿主/病毒搜索器方法,用于区分细菌和噬菌体序列。HVSeeker由两个独立的模型组成:一个分析DNA序列,另一个专注于蛋白质。除了HVSeeker的强大架构外,还引入了三种不同的预处理方法来增强学习过程:填充、组件组装和滑动窗口。该方法在长度从200到1500碱基对不等的序列上显示出令人满意的结果。在NCBI和IMGVR数据库上测试,HVSeeker优于文献中的几种方法,如Seeker、Rnn-VirSeeker、DeepVirFinder和PPR-Meta。此外,在基准数据集上,与其他方法相比,HVSeeker表现出了更好的性能,证明了其在未知噬菌体基因组鉴定方面的有效性。结论:这些结果证明了HVSeeker的特殊结构,包括预处理方法和模型设计。HVSeeker提供的进展对于识别病毒基因组和开发新的治疗方法(如噬菌体治疗)具有重要意义。因此,HVSeeker作为原核生物和噬菌体分类的重要工具,通过识别混合宏基因组中的宿主和病毒序列,为分析样品的宿主-病毒成分提供了至关重要的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HVSeeker: a deep-learning-based method for identification of host and viral DNA sequences.

Background: Bacteriophages are among the most abundant organisms on Earth, significantly impacting ecosystems and human society. The identification of viral sequences, especially novel ones, from mixed metagenomes is a critical first step in analyzing the viral components of host samples. This plays a key role in many downstream tasks. However, this is a challenging task due to their rapid evolution rate. The identification process typically involves two steps: distinguishing viral sequences from the host and identifying if they come from novel viral genomes. Traditional metagenomic techniques that rely on sequence similarity with known entities often fall short, especially when dealing with short or novel genomes. Meanwhile, deep learning has demonstrated its efficacy across various domains, including the bioinformatics field.

Results: We have developed HVSeeker-a host/virus seeker method-based on deep learning to distinguish between bacterial and phage sequences. HVSeeker consists of two separate models: one analyzing DNA sequences and the other focusing on proteins. In addition to the robust architecture of HVSeeker, three distinct preprocessing methods were introduced to enhance the learning process: padding, contigs assembly, and sliding window. This method has shown promising results on sequences with various lengths, ranging from 200 to 1,500 base pairs. Tested on both NCBI and IMGVR databases, HVSeeker outperformed several methods from the literature such as Seeker, Rnn-VirSeeker, DeepVirFinder, and PPR-Meta. Moreover, when compared with other methods on benchmark datasets, HVSeeker has shown better performance, establishing its effectiveness in identifying unknown phage genomes.

Conclusions: These results demonstrate the exceptional structure of HVSeeker, which encompasses both the preprocessing methods and the model design. The advancements provided by HVSeeker are significant for identifying viral genomes and developing new therapeutic approaches, such as phage therapy. Therefore, HVSeeker serves as an essential tool in prokaryotic and phage taxonomy, offering a crucial first step toward analyzing the host-viral component of samples by identifying the host and viral sequences in mixed metagenomes.

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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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