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Background Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner. Findings Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case. Conclusions metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data. [ABSTRACT FROM AUTHOR]

The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterization of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterization of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them. Author summary: Viruses are the most abundant biological entities on our planet. Some are relevant pathogens for public health or agriculture. Still, many also play ecological roles that are critical for maintaining ecosystems. Most viruses are yet to be cultured, so their identification and characterisation depend solely on the analysis of DNA or RNA obtained from the environment. Unlike cellular organisms, viruses also lack a universal genetic marker that allows taxonomic profiling of an environmental viral community. We have manually curated a set of specific viral protein models that serve as taxonomic markers for a comprehensive range of viral taxa. Using these protein models, we developed VIRify, a computational pipeline for the detection, annotation, and taxonomic classification of viral sequences obtained from environmental DNA or RNA. Our new pipeline was efficient in detecting and classifying sequences of viruses targeting bacteria or eukaryotic organisms in mock microbial communities samples from the world's oceans and a previously assembled collection of human gut viruses. VIRify is user-friendly, requires minimal interaction with the command line, and was developed with portability in mind. VIRify can enhance the exploration of viral diversity in nature and support the detection of pathogenic viruses with pandemic potential. [ABSTRACT FROM AUTHOR]

[Display omitted] • MGnify Genomes is a free-to-use resource for biome-specific microbial genomes. • Each genome catalogue is non-redundant and includes extensive functional annotations. • Users can search their own sequences against the resource to assess their novelty. • MGnify Genomes can be accessed via the website, the FTP server and the API. • The resource provides insight into previously uncultured species and novel proteins. An increasingly common output arising from the analysis of shotgun metagenomic datasets is the generation of metagenome-assembled genomes (MAGs), with tens of thousands of MAGs now described in the literature. However, the discovery and comparison of these MAG collections is hampered by the lack of uniformity in their generation, annotation and storage. To address this, we have developed MGnify Genomes, a growing collection of biome-specific non-redundant microbial genome catalogues generated using MAGs and publicly available isolate genomes. Genomes within a biome-specific catalogue are organised into species clusters. For species that contain multiple conspecific genomes, the highest quality genome is selected as the representative, always prioritising an isolate genome over a MAG. The species representative sequences and annotations can be visualised on the MGnify website and the full catalogue and associated analysis outputs can be downloaded from MGnify servers. A suite of online search tools is provided allowing users to compare their own sequences, ranging from a gene to sets of genomes, against the catalogues. Seven biomes are available currently, comprising over 300,000 genomes that represent 11,048 non-redundant species, and include 36 taxonomic classes not currently represented by cultured genomes. MGnify Genomes is available at https://www.ebi.ac.uk/metagenomics/browse/genomes/. [ABSTRACT FROM AUTHOR]

Comprehensive, high-quality reference genomes are required for functional characterization and taxonomic assignment of the human gut microbiota. We present the Unified Human Gastrointestinal Genome (UHGG) collection, comprising 204,938 nonredundant genomes from 4,644 gut prokaryotes. These genomes encode >170 million protein sequences, which we collated in the Unified Human Gastrointestinal Protein (UHGP) catalog. The UHGP more than doubles the number of gut proteins in comparison to those present in the Integrated Gene Catalog. More than 70% of the UHGG species lack cultured representatives, and 40% of the UHGP lack functional annotations. Intraspecies genomic variation analyses revealed a large reservoir of accessory genes and single-nucleotide variants, many of which are specific to individual human populations. The UHGG and UHGP collections will enable studies linking genotypes to phenotypes in the human gut microbiome. More than 200,000 gut prokaryotic reference genomes and the proteins they encode are collated, providing comprehensive resources for microbiome researchers. [ABSTRACT FROM AUTHOR]

Abstract: Maize (Zea mays L.) plants can establish beneficial associations with various nitrogen fixing and plant growth promoting bacteria (PGPB). Twenty-two putative endophytic bacteria isolated from maize plants were identified and characterized by the presence of nifH, IAA production, siderophores and phosphate solubilizing capacity. In addition, inoculation experiments to evaluate plant growth promotion were conducted under laboratory conditions. High diversity of diazotrophic bacteria associated to maize plants was found, including the genera Rhanella, Pantoea, Rhizobium, Pseudomona, Herbaspirillum, Enterobacter, Brevundimonas and Burkholderia. All strains produce IAA in vitro but only P. fluorescens (EMA68) produces siderophores. Phosphate solubilization capability was detected in eighteen strains and Rhanella spp. (EMA83) showed the highest potential. There were significant differences between controls and inoculated plants. Positive effects across maize cultivars were observed with 10 isolates from 22, whose inoculation resulted in an increase on shoot biomass over uninoculated controls without N. H. frisingense, EMA117 was the only strain that showed positive effect on both maize cultivars. It was observed a significant interaction between inoculation treatment×maize cultivars in shoot and root dry weight (P <0.05). Only one strain, Pseudomona flourescen, increased significantly the growth of the radicle compared to that of the controls without inoculation. In conclusion, maize inoculation experiments showed significant increases in shoot dry weight under controlled conditions but it is not clearly related to IAA production, P solubilization capacity or to more N efficiency assimilation. There is a potential PGPB in maize cultivars, but the results indicate the need for a better understanding of plant–bacteria interactions and to investigate plant colonization by endophytic bacteria. [Copyright &y& Elsevier]