Identification of mobile genetic elements with geNomad

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…

• UD
  U.S. Department of Energy

  Awards: -AC02-05CH11231, AC05-00OR22725, 76RL01830, AC05-76RL01830, 05CH11231, DE-AC05-76RL01830, AC02-05CH11231, DE-AC02, 17-SC-20-SC, DE-AC02-05CH11231, DE-AC05, 89233218CNA000001, 00OR22725, DE-AC02-
• JG
  Joint Genome Institute

  Awards: DE-AC02-05CH11231, AC02-05CH11231
• OO
  Office of Science

  Awards: DE-AC05-00OR22725, 89233218CNA000001, AC02-05CH11231, -AC02-05CH11231, DE-AC02, DE-AC05-76RL01830, 17-SC-20-SC, AC05-00OR22725
• NN
  National Nuclear Security Administration

  Awards: AC02-05CH11231, DE-AC02-05CH11231, 17-SC-20-SC, DE-AC05-00OR22725, DE-AC05-76RL01830, 89233218CNA000001
• BA
  Biological and Environmental Research

  Awards: 05CH11231, DE-AC05-00OR22725, AC05-76RL01830, 00OR22725, 89233218CNA000001, 76RL01830, DE-AC05-76RL01830, DE-AC02-05CH11231, AC02-05CH11231