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.

The bacterial and archaeal genomes that have been sequenced to date were chosen for sequencing based mainly on their physiology, which is fine but has resulted in a distinct phylogenetic bias. An alternative approach has been taken in the Genomic Encyclopedia of Bacteria and Archaea (GEBA) project, which advocates choosing genomes based on the organism's phylogenetic position, with the aim filling in the gaps in sequencing along on bacterial and archaeal branches of the tree of life. The value of this approach has been demonstrated by a pilot study of the genome sequences of 56 culturable species selected to maximize phylogenetic coverage. Analysis of the sequences provides insights into phylogenetics, protein function and genome annotation. There are now nearly 1,000 completed bacterial and archaeal genomes available, but as most of them were chosen for sequencing on the basis of their physiology, the data are limited by a highly biased phylogenetic distribution. To explore the value added by choosing microbial genomes for sequencing on the basis of their evolutionary relationships, the genomes of 56 species of Bacteria and Archaea selected to maximize phylogenetic coverage are now sequenced and analysed. Sequencing of bacterial and archaeal genomes has revolutionized our understanding of the many roles played by microorganisms1. There are now nearly 1,000 completed bacterial and archaeal genomes available2, most of which were chosen for sequencing on the basis of their physiology. As a result, the perspective provided by the currently available genomes is limited by a highly biased phylogenetic distribution3,4,5. To explore the value added by choosing microbial genomes for sequencing on the basis of their evolutionary relationships, we have sequenced and analysed the genomes of 56 culturable species of Bacteria and Archaea selected to maximize phylogenetic coverage. Analysis of these genomes demonstrated pronounced benefits (compared to an equivalent set of genomes randomly selected from the existing database) in diverse areas including the reconstruction of phylogenetic history, the discovery of new protein families and biological properties, and the prediction of functions for known genes from other organisms. Our results strongly support the need for systematic ‘phylogenomic’ efforts to compile a phylogeny-driven ‘Genomic Encyclopedia of Bacteria and Archaea’ in order to derive maximum knowledge from existing microbial genome data as well as from genome sequences to come.

Here we present a standard developed by the Genomic Standards Consortium (GSC) for reporting marker gene sequences—the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The 'environmental packages' apply to any genome sequence of known origin and can be used in combination with MIMARKS and other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we present the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.

The Deepwater Horizon oil spill in the Gulf of Mexico resulted in a deep-sea hydrocarbon plume that caused a shift in the indigenous microbial community composition with unknown ecological consequences. Early in the spill history, a bloom of uncultured, thus uncharacterized, members of the Oceanospirillales was previously detected, but their role in oil disposition was unknown. Here our aim was to determine the functional role of the Oceanospirillales and other active members of the indigenous microbial community using deep sequencing of community DNA and RNA, as well as single-cell genomics. Shotgun metagenomic and metatranscriptomic sequencing revealed that genes for motility, chemotaxis and aliphatic hydrocarbon degradation were significantly enriched and expressed in the hydrocarbon plume samples compared with uncontaminated seawater collected from plume depth. In contrast, although genes coding for degradation of more recalcitrant compounds, such as benzene, toluene, ethylbenzene, total xylenes and polycyclic aromatic hydrocarbons, were identified in the metagenomes, they were expressed at low levels, or not at all based on analysis of the metatranscriptomes. Isolation and sequencing of two Oceanospirillales single cells revealed that both cells possessed genes coding for n-alkane and cycloalkane degradation. Specifically, the near-complete pathway for cyclohexane oxidation in the Oceanospirillales single cells was elucidated and supported by both metagenome and metatranscriptome data. The draft genome also included genes for chemotaxis, motility and nutrient acquisition strategies that were also identified in the metagenomes and metatranscriptomes. These data point towards a rapid response of members of the Oceanospirillales to aliphatic hydrocarbons in the deep sea.

The human gastrointestinal tract contains a complex community of microbes, fulfilling important health-promoting functions. However, this vast complexity of species hampers the assignment of responsible organisms to these functions. Recently, Akkermansia muciniphila, a new species from the deeply branched phylum Verrucomicrobia, was isolated from the human intestinal tract based on its capacity to efficiently use mucus as a carbon and nitrogen source. This anaerobic resident is associated with the protective mucus lining of the intestines. In order to uncover the functional potential of A. muciniphila, its genome was sequenced and annotated. It was found to contain numerous candidate mucinase-encoding genes, but lacking genes encoding canonical mucus-binding domains. Numerous phage-associated sequences found throughout the genome indicate that viruses have played an important part in the evolution of this species. Furthermore, we mined 37 GI tract metagenomes for the presence, and genetic diversity of Akkermansia sequences. Out of 37, eleven contained 16S ribosomal RNA gene sequences that are >95% identical to that of A. muciniphila. In addition, these libraries were found to contain large amounts of Akkermansia DNA based on average nucleotide identity scores, which indicated in one subject co-colonization by different Akkermansia phylotypes. An additional 12 libraries also contained Akkermansia sequences, making a total of ∼16 Mbp of new Akkermansia pangenomic DNA. The relative abundance of Akkermansia DNA varied between <0.01% to nearly 4% of the assembled metagenomic reads. Finally, by testing a large collection of full length 16S sequences, we find at least eight different representative species in the genus Akkermansia. These large repositories allow us to further mine for genetic heterogeneity and species diversity in the genus Akkermansia, providing novel insight towards the functionality of this abundant inhabitant of the human intestinal tract.

Sequencing-based analyses of microbiomes have traditionally focused on addressing the question of community membership and profiling taxonomic abundance through amplicon sequencing of 16 rRNA genes. More recently, shotgun metagenomics, which involves the random sequencing of all genomic content of a microbiome, has dominated this arena due to advancements in sequencing technology throughput and capability to profile genes as well as microbiome membership. While these methods have revealed a great number of insights into a wide variety of microbiomes, both of these approaches only describe the presence of organisms or genes, and not whether they are active members of the microbiome. To obtain deeper insights into how a microbial community responds over time to their changing environmental conditions, microbiome scientists are beginning to employ large-scale metatranscriptomics approaches. Here, we present a comprehensive review on computational metatranscriptomics approaches to study microbial community transcriptomes. We review the major advancements in this burgeoning field, compare strengths and weaknesses to other microbiome analysis methods, list available tools and workflows, and describe use cases and limitations of this method. We envision that this field will continue to grow exponentially, as will the scope of projects (e.g. longitudinal studies of community transcriptional responses to perturbations over time) and the resulting data. This review will provide a list of options for computational analysis of these data and will highlight areas in need of development.