A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. The current version introduces four new dataset types, together with numerous new features. Annotation options have been expanded and new control options added for many display elements. An interactive spreadsheet-like editor has been implemented, providing dataset creation and editing directly in the web interface. Font support has been rewritten with full support for UTF-8 character encoding throughout the user interface. Google Web Fonts are now fully supported in the tree text labels. iTOL v4 is the first tool which supports direct visualization of Qiime 2 trees and associated annotations. The user account system has been streamlined and expanded with new navigation options, and currently handles >700 000 trees from more than 40 000 individual users. Full batch access has been implemented allowing programmatic upload and export of trees and annotations.

The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 5 introduces a completely new tree display engine, together with numerous new features. For example, a new dataset type has been added (MEME motifs), while annotation options have been expanded for several existing ones. Node metadata display options have been extended and now also support non-numerical categorical values, as well as multiple values per node. Direct manual annotation is now available, providing a set of basic drawing and labeling tools, allowing users to draw shapes, labels and other features by hand directly onto the trees. Support for tree and dataset scales has been extended, providing fine control over line and label styles. Unrooted tree displays can now use the equal-daylight algorithm, proving a much greater display clarity. The user account system has been streamlined and expanded with new navigation options and currently handles >1 million trees from >70 000 individual users.

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

AbstracteggNOG is a public database of orthology relationships, gene evolutionary histories and functional annotations. Here, we present version 5.0, featuring a major update of the underlying genome sets, which have been expanded to 4445 representative bacteria and 168 archaea derived from 25 038 genomes, as well as 477 eukaryotic organisms and 2502 viral proteomes that were selected for diversity and filtered by genome quality. In total, 4.4M orthologous groups (OGs) distributed across 379 taxonomic levels were computed together with their associated sequence alignments, phylogenies, HMM models and functional descriptors. Precomputed evolutionary analysis provides fine-grained resolution of duplication/speciation events within each OG. Our benchmarks show that, despite doubling the amount of genomes, the quality of orthology assignments and functional annotations (80% coverage) has persisted without significant changes across this update. Finally, we improved eggNOG online services for fast functional annotation and orthology prediction of custom genomics or metagenomics datasets. All precomputed data are publicly available for downloading or via API queries at http://eggnog.embl.de

Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DR hi CD11c hi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DR lo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe COVID-19.

Orthology assignment is ideally suited for functional inference. However, because predicting orthology is computationally intensive at large scale, and most pipelines are relatively inaccessible (e.g., new assignments only available through database updates), less precise homology-based functional transfer is still the default for (meta-)genome annotation. We, therefore, developed eggNOG-mapper, a tool for functional annotation of large sets of sequences based on fast orthology assignments using precomputed clusters and phylogenies from the eggNOG database. To validate our method, we benchmarked Gene Ontology (GO) predictions against two widely used homology-based approaches: BLAST and InterProScan. Orthology filters applied to BLAST results reduced the rate of false positive assignments by 11%, and increased the ratio of experimentally validated terms recovered over all terms assigned per protein by 15%. Compared with InterProScan, eggNOG-mapper achieved similar proteome coverage and precision while predicting, on average, 41 more terms per protein and increasing the rate of experimentally validated terms recovered over total term assignments per protein by 35%. EggNOG-mapper predictions scored within the top-5 methods in the three GO categories using the CAFA2 NK-partial benchmark. Finally, we evaluated eggNOG-mapper for functional annotation of metagenomics data, yielding better performance than interProScan. eggNOG-mapper runs ∼15× faster than BLAST and at least 2.5× faster than InterProScan. The tool is available standalone and as an online service at http://eggnog-mapper.embl.de.

We present two standards developed by the Genomic Standards Consortium (GSC) for reporting bacterial and archaeal genome sequences. Both are extensions of the Minimum Information about Any (x) Sequence (MIxS). The standards are the Minimum Information about a Single Amplified Genome (MISAG) and the Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity.