Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical.

Identifying and characterizing mobile genetic elements (MGEs) 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 large dataset of marker proteins 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 that included diverse MGE and chromosome sequences, geNomad significantly outperformed other tools in all evaluated clades of plasmids and viruses. Leveraging geNomad's speed and scalability, we were able to process public metagenomes and metatranscriptomes, leading to the discovery of millions of new viruses and plasmids that are available through the IMG/VR and IMG/PR databases. We anticipate that geNomad will enable further advancements in MGE research, and it is available at https://portal.nersc.gov/genomad.

The National Microbiome Data Collaborative (NMDC) Data Portal (https://data.microbiomedata.org) supports microbiome multi-omics data exploration and access through an integrated, distributed data framework aligned with the FAIR (Findable, Accessible, Interoperable and Reusable) data principles ( 1). The NMDC Data Portal currently hosts 10.2 terabytes of multi-omics microbiome data, spanning five data types (metagenomes, metatranscriptomes, metaproteomes, metabolomes, and natural organic matter characterizations), generated at two Department of Energy User Facilities, the Joint Genome Institute (JGI) at Lawrence Berkeley National Laboratory (LBNL) and the Environmental Molecular Systems Laboratory (EMSL) at Pacific Northwest National Laboratory (PNNL). A flexible data schema (https://github.com/microbiomedata/nmdc-schema) leveraging community-driven standards underpins how data is managed and integrated. Annotated multi-omic data products are produced by the NMDC workflows and linked through common biosamples to enable search capabilities based on environmental context, instrumentation, and functional attributes. As a pilot system, the NMDC Data Portal offers download capabilities and several search components, including interactive geographic visualization of samples; environmental classification distribution visualized through an interactive Sankey diagram; time-series slider to select longitudinal samples of interest; and an upset plot displaying the number of multi-omics data generated from the same biosample within a study.

Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high-quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina versus ONT technologies on workflow congruence and differences. https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2. Supplementary data are available at Bioinformatics online.