MGnify: the microbiome sequence data analysis resource in 2023.

The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.

VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models.

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 characterisation 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 characterisation 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.

MGnify: the microbiome analysis resource in 2020.

MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations.

Aquacobalamin Accelerates Orange II Destruction by Peroxymonosulfate via the Transient Formation of Secocorrinoid: A Mechanistic Study.

Besides its use in medicine, vitamin B12 (cobalamin) and its derivatives have found in numerous applications as catalysts. However, studies related to the activation of oxidants via cobalamin are scant. In this work, we showed how the addition of aquacobalamin (H2OCbl) accelerates the destruction of azo-dye Orange II by peroxymonosulfate (HSO5-) in aqueous solutions. In neutral and weakly alkaline media, the process is initiated by the modification of the corrin macrocycle with HSO5-, which requires the preliminary deprotonation of the aqua-ligand in H2OCbl to give hydroxocobalamin, producing 5,6-dioxo-5,6-secocobalamin or its isomer (14,15-dioxo-14,15-secocobalamin). In acidic solutions, where the concentration of hydroxocobalamin is negligible, the formation of dioxo-seco-species is not observed, and the reaction between H2OCbl and HSO5- results in slow chromophore bleaching. Using terephthalic acid, we demonstrated the formation of hydroxyl radicals in the mixture of H2OCbl with HSO5-, whereas the generation of sulfate radicals was proved by comparing the effects of ethanol and nitrobenzene on Orange II destruction using the H2OCbl/HSO5- system. The reaction mechanism includes the binding of HSO5- to the Co(III) ion of dioxo-secocobalamin, which results in its deprotonation and the labilization of the O-O bond, leading to the formation of sulfate and hydroxyl radicals which further react with Orange II.

svist4get: a simple visualization tool for genomic tracks from sequencing experiments.

BACKGROUND: High-throughput sequencing often provides a foundation for experimental analyses in the life sciences. For many such methods, an intermediate layer of bioinformatics data analysis is the genomic signal track constructed by short read mapping to a particular genome assembly. There are many software tools to visualize genomic tracks in a web browser or with a stand-alone graphical user interface. However, there are only few command-line applications suitable for automated usage or production of publication-ready visualizations. RESULTS: Here we present svist4get, a command-line tool for customizable generation of publication-quality figures based on data from genomic signal tracks. Similarly to generic genome browser software, svist4get visualizes signal tracks at a given genomic location and is able to aggregate data from several tracks on a single plot along with the transcriptome annotation. The resulting plots can be saved as the vector or high-resolution bitmap images. We demonstrate practical use cases of svist4get for Ribo-Seq and RNA-Seq data. CONCLUSIONS: svist4get is implemented in Python 3 and runs on Linux. The command-line interface of svist4get allows for easy integration into bioinformatics pipelines in a console environment. Extra customization is possible through configuration files and Python API. For convenience, svist4get is provided as pypi package. The source code is available at https://bitbucket.org/artegorov/svist4get/.

Top-down and bottom-up control of phytoplankton communities by zebra mussels Dreissena polymorpha (Pallas, 1771).

Zebra mussels (ZM), Dreissena polymorpha (Pallas, 1771), are one of the most aggressive invasive species. ZM have a strong bidirectional impact on phytoplankton because of their high nutrient excretion rates and high grazing pressure. Furthermore, the interactions between excretion and selective grazing are related to the trophic status of a waterbody and could cause unpredictable changes in phytoplankton. We performed three types of experiments: (i) bottom-up where we measured ZM excretion rates; (ii) top-down where we analyzed the effects of ZM on phytoplankton taxonomic structure via grazing in different trophic conditions; (iii) mesocosm experiment where we estimated the combined top-down and bottom-up effects of ZM on phytoplankton assemblages under different trophic conditions. Our first experiment confirmed high excretion rates of dissolved nutrients (PO43- and NH4+) and dissolved organic carbon (DOC) by ZM. The other experiments revealed selective grazing by ZM, where diatoms were mostly selectively rejected, while green algae were preferred. In the top-down experiment, ZM decreased the total biomass of phytoplankton, while in mesocosm experiments where top-down and bottom-up controls acted simultaneously, we observed increased phytoplankton biomass mainly through increases in filamentous green algae. Our experiments show that ZM can influence phytoplankton through a combination of bottom-up and top-down effects that vary with trophic state.

MGnify Genomes: A Resource for Biome-specific Microbial Genome Catalogues.

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/.

A unified catalog of 204,938 reference genomes from the human gut microbiome.

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.