美国劳伦斯伯克利国家实验室Nikos C. Kyrpides、Antonio Pedro Camargo课题组的研究利用geNomad鉴定了可移动遗传元件。这一研究成果发表在2023年9月21日出版的国际学术期刊《自然-生物技术》上。

研究人员研发了geNomad，这是一个分类和注释框架，它结合基因信息和深度神经网络信息来识别质粒和病毒序列。geNomad使用了超过200,000个标记蛋白谱的数据集对病毒基因组的功能基因进行注释和分类分配。使用条件随模型，geNomad还可以高精度地检测整合到宿主基因组中的前病毒。在基准测试中，geNomad对各种质粒和病毒实现了高精准分类（Matthews相关系数分别为77.8和95.3），大大优于其他工具。

利用geNomad快速和可扩展性，研究人员处理了超过2.7万亿个碱基对的测序数据，从而发现了数百万种包含在IMG/VR和IMG/PR数据库中的病毒和质粒。geNomad可通过https://portal.nersc.gov/genomad访问。

据介绍，识别和表征测序数据中的可移动遗传元件对于了解其多样性、生态学、生物技术应用以及对公共卫生的影响都至关重要。

附：英文原文

Title: Identification of mobile genetic elements with geNomad

Author: Camargo, Antonio Pedro, Roux, Simon, Schulz, Frederik, Babinski, Michal, Xu, Yan, Hu, Bin, Chain, Patrick S. G., Nayfach, Stephen, Kyrpides, Nikos C.

Issue&Volume: 2023-09-21

Abstract: Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad’s speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad.

DOI: 10.1038/s41587-023-01953-y

Source: https://www.nature.com/articles/s41587-023-01953-y