长读宏基因组测序否定了黏液孢子菌胞嘧啶甲基化的推测损失。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Antonio Starcevic, Rayline T A Figueredo, Juliana Naldoni, Lincoln L Corrêa, Beth Okamura, Edson A Adriano, Paul F Long
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引用次数: 0

摘要

Oxford-Nanopore PromethION测序是一种无pcr的方法,保留表观遗传标记,并提供DNA甲基化的直接定量信息。利用这种长读测序技术,我们成功地组装了5个黏液动物基因组,没有明显的宿主DNA污染,在质量和完整性上都超过了以前的研究。基因组组装揭示了黏液动物基因组中的DNA甲基化模式,特别是在基因体内富含gc的区域。这一发现不仅驳斥了黏液动物缺乏DNA甲基化能力的观点,而且为研究黏液动物的基因调控提供了新的视角。高质量的基因组组合为黏液动物的未来研究奠定了坚实的基础,包括控制这些具有商业意义的鱼类病原体的新策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Long-read metagenomic sequencing negates inferred loss of cytosine methylation in Myxosporea (Cnidaria: Myxozoa).

Oxford-Nanopore PromethION sequencing is a PCR-free method that retains epigenetic markers and provides direct quantitative information about DNA methylation. Using this long-read sequencing technology, we successfully assembled 5 myxozoan genomes free from discernible host DNA contamination, surpassing previous studies in both quality and completeness. Genome assembly revealed DNA methylation patterns within myxozoan genomes, particularly in GC-rich regions within gene bodies. The findings not only refute the notion of myxozoans lacking DNA methylation capability but also offer a new perspective on gene regulation in these parasites. The high-quality genome assemblies lay a solid foundation for future research on myxozoans, including new strategies to control these commercially significant fish pathogens.

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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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