用geNomad识别移动遗传元素的方法
Antonio Pedro Camargo1, Simon Roux2, Frederik Schulz2
1DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. antoniop.camargo@lbl.gov.
Nature biotechnology
|September 21, 2023
概括
geNomad是一个新的框架,可以在测序数据中识别和注释移动遗传元素,如塑体和病毒. 它的性能明显优于现有的工具,使得发现数百万种新元素成为可能.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 病毒学 病毒学
背景情况:
- 像塑体和病毒这样的移动遗传元素 (MGE) 对进化,生态和公共卫生至关重要.
- 从测序数据中准确识别和描述MGEs至关重要,但具有挑战性.
- 现有的工具往往缺乏大规模基因组分析所需的精度和可扩展性.
研究的目的:
- 介绍geNomad,一个用于分类和注释等离子体和病毒的新型计算框架.
- 加强在宿主基因组内检测MGEs,包括集成的前病毒.
- 为分析大规模测序数据集提供可扩展和高性能解决方案.
主要方法:
- geNomad将基因内容分析与用于MGE识别的深度神经网络集成在一起.
- 它使用了超过20万个标记蛋白质配置文件的综合数据集,用于功能注释和分类学赋值.
- 条件随机场模型用于精确检测集成的前病毒.
主要成果:
- 与其他工具相比,geNomad在分类等离子体 (MCC 77.8%) 和病毒 (MCC 95.3%) 中表现优越.
- 该框架成功处理了超过2.7万亿个基数对的测序数据.
- 这种分析导致发现了数以百万计的新型病毒和等离子体.
结论:
- geNomad提供了一种高度准确,快速和可扩展的方法,用于在大型测序数据集中识别和注释MGE.
- 发现的病毒和等离子体序列通过IMG/VR和IMG/PR数据库公开提供.
- geNomad在研究MGE及其影响方面取得了重大进展.
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