利用 Meta-NanoSim 鉴定和模拟元基因组纳米孔测序数据。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Chen Yang, Theodora Lo, Ka Ming Nip, Saber Hafezqorani, René L Warren, Inanc Birol
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引用次数: 0

摘要

背景:纳米孔测序对元基因组研究至关重要,因为它的千碱基长读数有助于解析微生物之间的基因组结构差异。然而,测序平台特有的挑战,包括高碱基调用错误率、读取长度不均匀和嵌合伪影的存在,使得有必要专门设计分析算法。使用具有被评估测序平台真实特征的模拟数据集,是在受控环境下评估生物信息学工具性能与基本事实的一种经济有效的方法:在这里,我们介绍了 Meta-NanoSim,这是一种快速、多功能的工具,可以描述和模拟纳米孔元基因组读数的独特属性。它通过一种基础量化算法改进了最先进的微生物丰度估算方法。Meta-NanoSim 可以模拟由线性基因组和环状基因组组成的复杂微生物群落,并能直接从在线服务器获取参考基因组。模拟数据集在读取长度、误差曲线和丰度水平方面与实验数据高度一致。我们证明了 Meta-NanoSim 模拟数据可以促进元基因组算法的开发,并通过元基因组组装基准任务指导实验设计:Meta-NanoSim表征模块研究了读数特征,包括嵌合信息和丰度水平,而模拟模块则模拟了具有不同丰度特征的大型复杂多样本微生物群落。所有训练有素的模型和软件都可以在 GitHub 上免费访问:https://github.com/bcgsc/NanoSim.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim.

Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim.

Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim.

Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim.

Background: Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, sequencing platform-specific challenges, including high base-call error rate, nonuniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical algorithms. The use of simulated datasets with characteristics that are true to the sequencing platform under evaluation is a cost-effective way to assess the performance of bioinformatics tools with the ground truth in a controlled environment.

Results: Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. It improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. Meta-NanoSim can simulate complex microbial communities composed of both linear and circular genomes and can stream reference genomes from online servers directly. Simulated datasets showed high congruence with experimental data in terms of read length, error profiles, and abundance levels. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenome assembly benchmarking task.

Conclusions: The Meta-NanoSim characterization module investigates read features, including chimeric information and abundance levels, while the simulation module simulates large and complex multisample microbial communities with different abundance profiles. All trained models and the software are freely accessible at GitHub: https://github.com/bcgsc/NanoSim.

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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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