注释
基因组
鉴定(生物学)
流动遗传元素
可扩展性
计算生物学
生物
计算机科学
DNA测序
基因
遗传学
数据库
植物
作者
Antônio Pedro Camargo,Simon Roux,Frederik Schulz,Michal Babinski,Yan Xu,Bin Hu,Patrick Chain,Stephen Nayfach,Nikos C. Kyrpides
标识
DOI:10.1038/s41587-023-01953-y
摘要
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 .
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