Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health. Viruses, the most abundant and diverse biological entities in the biosphere, play important roles in public health, ecology, and agriculture ,2. The iWeb-facing databases provide the structured and indexed storage of information, which can be easily accessed through an internet browser. In the context of virus research, virus databases represent central hubs of information connecting virus genomic sequences and associated metadata, such as viral and host taxonomy, host range, transmission modes, genomic structures, and gene annotations. Researchers use these databases to gain insights into viral genetic diversity and evolutionary relationships, and to study new viruses ,12. ViruThe presence of multiple virus databases can be attributed to variations in specialization, data types, and aims. Some databases are created for a specific research purpose or to support a specific virus research area, e.g., virus ecology or epidemiology; alternately, some databases focus on specific viruses or cover a wide range of viruses. Further, databases have different goals, ranging from information dissemination to providing web-based tools for data analysis. Database longevity refers to the ability of a database to remain functional and accessible over a long period of time, even as technology and software evolve . EnsurinNCBI Genbank) as well as additional computed datasets based on these sequences. As such, virus databases contain vital metadata, such as the taxonomic details of hosts and viruses, location data, publications, years, processing information, morphology, and more. These metadata play a crucial role in studying virus outbreaks, tracking infection pathways, and analyzing viral distribution patterns. Another important type of data found in virus databases is gene annotations, providing valuable information about genetic elements, protein sequences, gene functions, and structural features. These annotation data allow researchers to analyze the functions of viral genes and proteins, gaining insights into their impact on virus biology. These datasets facilitate the comparative analysis of viruses and virus communities. On the other hand, incomplete or inaccurate metadata in sequence entries can adversely impact downstream analysis [Many virus databases feature the extra curation of a subset of virus sequences from long-term sequence repositories  FAIRsharing [The Database Commons [ELIXIR bio.tools [NAR database list [In addition to database review articles, several catalogs of databases have been developed that aggregate databases in a clear and well-structured manner. Five such catalogs of databases are (1) Rsharing , (3) The Commons , (4) ELIio.tools , and (5)ase list . These cWhen using a virus database, users have diverse requirements beyond accessing relevant content. The usability of the database is crucial, with easy navigation and efficient information retrieval being key factors. Features like keyword search and phrase suggestions enable efficient entry retrieval. Presenting results in meaningful formats, such as tables, enhances usability, particularly when multiple matching entries are involved. Accessibility is important, providing options like one-click downloads and availability through various channels , considering the need for user login. Establishing links to other databases enables comprehensive and integrated research, while integration with workbenches and the availability of associated tools empower users to work with their data and utilize additional resources for analysis. The easy sharing of URLs promotes collaboration and efficient communication. Trust and transparency are fostered by the availability of source code, enabling users to comprehend underlying processes. To the best of our knowledge, there are currently no existing reviews that attempt to describe the functionality of databases. This lack of information hinders users in making informed decisions about which database is the most suitable for their needs.the FAIR principles website. The FAIR principles can be applied to evaluate three key entities: data , metadata , and infrastructure. Adhering to the FAIR principles is meant to enhance machine-driven discovery and the utilization of data in parallel to facilitating human collaboration and data reuse in scientific research. Evaluating virus databases based on FAIR principles would reveal their potential to advance scientific knowledge and enable data-driven discoveries. As far as our knowledge extends, this is the initial evaluation of virus databases in terms of FAIRness.The FAIR data principles encompass the findability, accessibility, interoperability, and reusability of research data objects . The prire3data.org, (2) FAIRsharing [The Database Commons [ELIXIR bio.tools [NAR database list [In this study, we provide a comprehensive overview of the most up-to-date virus databases, covering five key aspects: (1) content, (2) functionality, (3) FAIRness, (4) comparison of the catalogs of databases, and (5) an analysis of potential errors within the databases. In terms of content, we compare the key features already present in other reviews, such as name, link, last update, short description, and citations. Additionally, we delve deeper into the content of the databases by evaluating the number of sequences and species available, suggesting use cases for each database. Regarding functionality, we compare features such as general usage, presence of a workbench, tool availability, database complexity, download options and ease of use, and the need for user login. Additionally, we analyze whether keyword search functionality is available, whether there are phrase suggestions, the presentation format of the output, the presence of links to other databases, the ability to share URLs, and the availability of source code. Furthermore, we evaluate the FAIRness  of the databases, as it is of paramount importance for their usability and long-term value. In our review, we include an up-to-date assessment of the webpages hosting virus databases, specifically focusing on (1) Rsharing , (3) The Commons , (4) ELIio.tools , and (5)ase list . AdditioHere, we present a comprehensive overview of 24 virus databases that are currently active and contain virus-centric data ; see re3data.org, FAIRsharing, The Database Commons, ELIXIR bio.tools, and NAR database list).We compiled the list of databases for comparisons by including all up-to-date databases mentioned in the previous review papers ,22, alonWe only examined up-to-date databases, meaning databases with a website or data update from 2022 onward. Due to the COVID-19 pandemic, numerous coronavirus-related databases have been developed and established. In total, we identified 49 such coronavirus databases, of which we selected and included 3 important ones in Disease Monitoring Dashboard, (2) RID, (3) Virus-CKB, (4) VirHostNet 3.0, (5) HIV Drug Inter- actions, (6) HEP Drug Interactions, (7) HIV and COVID-19 Registry in Europe, (8) United States Swine Pathogen Database, (9) Global.health, and (10) WestNile.ca.gov.The following non-COVID databases were found in our search of databases and were not included in our The database comparison included the database name, a hyperlink to the database, and an assessment of whether a login was required. Additionally, the functionality was described, considering aspects such as user friendliness, the availability of a workbench, the ability to work with personal data, tool availability, subjective complexity, the type of downloads offered, and the possibility of accessing all the data. Last, a short description of each database was provided along with the corresponding references.In the content section, we listed an estimate of the number of families, genera and species covered in the database. Note that only the species estimate (# spec) is included in Regarding the current manuscript, we used ChatGPT (May 24 Version) in compliance with the journal guidelines to help generate most of the text. The writing process can be simplified as follows: 1. We defined a working outline of the contents of the paper. 2. We used each point in the outline as a prompt to generate a paragraph. 3. The paragraphs were edited for flow and scrutinized to make sure the content was as intended. This process was applied to the entire manuscript text, excluding the first paragraph of the introduction, the FAIR criteria paragraphs, the conclusion and the figure and table captions.Further, we specified the number of nucleotide (#seq-n ) and protein sequences (#seq-p) that were available for browse and bulk download. In more detail, the number of nucleotide sequences referred to the total number of partial and complete nucleotide sequences, which were available as one or more FASTA or FASTQ files. Depending on the database, this number included the genome, gene and/or transcript sequences of viruses. Note that for some databases, plasmids were included in this estimate. The number of protein sequences refers to the number of protein sequences available as one or more FASTA or FASTQ files; this included coding regions, predicted genes (aka open reading frames) and mRNA sequences. Further explanations can be found in the individual database paragraphs starting in In These databases play a crucial role in research, facilitating knowledge transfer and providing foundational information. It is important to note that these sources do not directly contain sequences but provide external links to the sequences, instead serving as repositories of centralized knowledge on viruses.International Committee on Taxonomy of Viruses (ICTV) is the global organization tasked by the International Union of Microbiological Societies (IUMS) with developing, refining and maintaining the virus taxonomy down to the level of species [ICTV (version: MSL38) comprised 264 families, 2818 genera, and 11,273 species. The taxonomy is periodically updated, with revisions released at least once and up to twice a year. New entries are proposed by the scientific community and reviewed by expert subcommittees within the ICTV. The categorization of virus groups is based on various characteristics, such as genetic material, genome organization, replication strategy, and host range. Notably, the ICTV recently embraced the inclusion of virus groups based solely on sequence information, departing from the traditional reliance on virus morphology. Users can download the entire taxonomy as an Excel sheet or browse through it, available at the visual browser. Links to the genome sequences of exemplars (representatives) and additional representatives of each virus species are provided in the Virus Metadata Resource sheet. In our opinion, the website may profit from a clearer structure and direct download of exemplar and additional virus genome sequences. Helpful are the provided “How-to Videos” as a valuable resource to assist users in effectively navigating the taxonomy.One of the fundamental tasks in virology is establishing a robust taxonomy to facilitate the effective comparison and study of viruses. The  species ,30,59. AViralZone is a powerful and up-to-date online encyclopedic database that provides summarized expert knowledge on various aspects of viruses, including genomic structure, virus replication cycle, host range, virus taxonomy, and molecular biology [ViralZone itself does not contain nucleotide sequences in bulk download form, it does provide links to protein and nucleotide sequences of reference sequences within the fact sheet. It is a structured, user-friendly and well-connected website, where users and especially virologists can quickly find the information for which they are looking.The  biology . Widely VIPERdb, a specialized database dedicated to icosahedral virus capsid structures, offers a wealth of information derived from both structural and computational analyses [Protein Data Bank, and for those with a related publication, the PubMed id is linked. We did not include these protein sequences in VIPERdb allows users to explore virus protein structures based on taxonomic classifications or specific criteria. One limitation of VIPERdb is its focus on icosahedral virus capsid structures. However, there are ongoing efforts to expand its scope, and some helical structures are already included. This comprehensive database encompasses information on 1323 protein structures from 165 viral genera and 75 virus families. With its latest release, VIPERdb introduced a new standalone database on its website, namely the Virus World. It contains a browsable taxonomy and interlinked data (genome type and coat protein sequences) on 211,450 viruses from 1038 viral genera and 158 viral families. Virus World also provides the capsid protein sequences for these viruses in some instances. In our opinion, the search function of VIPERdb could be improved, as users must have prior knowledge of their virus of interest before using it. Further, bulk download and browse options would increase user friendliness. Please note that there was an additional database known as VIPR, which was recently incorporated into the BV-BRC  as described below.The analyses ,33. The Virus-Host DB is a comprehensive and manually curated database that links viruses and hosts using pairs of NCBI taxonomy Ids [NCBI/RefSeq and GenBank, with the accession numbers listed in EBI Genomes. Host information is collected from various sources, including RefSeq, GenBank, UniProt, and ViralZone, and supplemented with additional data obtained from literature surveys. The database offers comprehensive information on 15,179 virus species, encompassing scientific names, lineages, Baltimore groups, RefSeq genome sequences, database links, and details of 3791 host species, enabling users to investigate interactions from both virus and host viewpoints. The database is well-interconnected, making it user friendly and valuable for obtaining an overview of interactions. The database contains a limited amount of information, as it focuses on providing specific linkages rather than comprehensive data.The nomy Ids . It inclGenomic, transcriptomic, and proteomic virus sequences serve as a foundational element for a wide range of virus bioinformatics analyses. For example, phylogenetic analysis typically starts with multiple sequence alignment of a collection of sequences. For host sequences, we refer to additional -omics databases; see below. Sequence databases serve as a critical starting point for examining genetic variations and functional components. When working with all or a significant portion of sequences from a database for further analyses, e.g., in virus ecology, it is important to be aware of the imbalance in virus representation. In other words, the composition of viruses sequences within a database does not reflect the natural occurrence of viruses.Bacterial and Viral Bioinformatics Resource Center (BV-BRC) is a recently merged platform that integrates various NIAID-funded pathogen-related resources, including the Virus Pathogen Resource (ViPR), the Influenza Research Database (IRD) and PATRIC . With diverse computational tools, the BV-BRC empowers researchers to analyze and interpret large genetic datasets originating from NCBI GenBank and Refseq (see below) as well as specific projects. Users have the ability to search, browse, download, and analyze a multitude of data types, including metadata, taxonomy, genomes, features (ORFs), proteins, protein structures, domains and motifs, epitopes, and experimental data. It also offers a private workbench for the secure analysis and storage of private datasets. The BV-BRC encompasses integrated datasets from mainly pathogenic bacteria, archaea, viruses, and eukaryotes, allowing users to search, browse, download, and analyze various data types, such as metadata, taxonomy, genomes, features, proteins, and experimental data. Tools and services are categorized into genomics, phylogenomics, protein analysis, metagenomics, transcriptomics, and utilities. The BV-BRC provides access to 295,306,161 virus gene and genome, partial and complete sequences including 9,763,946 genomes/segments, representing 106 virus families, 1946 genera, and 24,824 species. Additionally, there are 480,376,932 virus protein sequences. The BV-BRC categorizes plasmids under the virus category, resulting in the inclusion of plasmid sequences within the overall sequence count. Note that the number of species is higher than that of the official ICTV number because the BV-BRC includes unclassified taxa. Despite the complexity of the platform, efforts have been made to maintain user friendliness and visual accessibility. Workshops and training opportunities are provided regularly to enhance user proficiency in utilizing the database effectively.The NCBI Virus interface is a consolidation of various NCBI resources: NCBI Viral Genomes (a former version of NCBI Virus), NCBI Nucleotide (selected for taxonomic classification to viruses), including Refseq, Genbank, Virus Variation Resource, and the old resource NCBI Retroviruses [International Nucleotide Sequence Database Collaboration (INSDC). The INSDC collaboration is composed of three organizations: the National Center for Biotechnology Information (NCBI) GenBank, the EMBL-EBI European Nucleotide Archive (ENA) and the DNA DataBank of Japan (DDBJ). These three repositories contain the same data, ensuring data consistency across platforms. The sequences from these repositories are frequently utilized as a starting point by other databases, which then apply diverse analyses or visualizations to further explore the data.The recently established oviruses . Virus gNCBI Virus is regularly updated, with the core component being the GenBank and Refseq sequences and well-curated metadata, and additional features being new analysis or visualization functionalities. As of June 2023, 11,345,662 virus nucleotide and 52,734,161 virus protein sequences were accessible through NCBI Virus. These sequences are linked to the NCBI Nucleotide database, providing extensive metadata, such as organism, host, taxonomy, publication, and organization , as well as the corresponding nucleotide or protein sequences. The number of species in the NCBI Virus database, which is 52,414, surpasses the official ICTV count due to the inclusion of unclassified taxa. However, it is important to note that within the broader NCBI GenBank database, there are instances of sequence errors that can potentially contribute to false-positive results in analyses (see NCBI offers the Reference Sequence (RefSeq) collection as a comprehensive, integrated, and well-annotated dataset containing extensive curation and additional data types, including 19,975 nucleotide sequences and 710,847 protein sequences of viruses. The RefSeq are considered high-quality sequences and are extensively utilized by the scientific community.NCBI Virus is user-friendly, and the results are filterable by a wide range of curated metadata, such as taxonomy, length, completeness, host, submitter, genome molecule type, and date. Users can perform a sequence blast or keyword search, with example searches, such as “all viruses” or “bacteriophages”. Based on our experience, datasets containing up to 100,000 sequences can be readily downloaded, offering users a range of options to select from. Additionally, users can conveniently create their own custom FASTA headers. Several tools are available to perform alignments or phylogenetic analyses with selected sequences. Over the years, NCBI Virus resources have evolved, offering enhanced functionality. Compared to the older NCBI Viral Genomes database [NCBI Virus is more organized, functional, and visually appealing.The Virus Variation Resource (VVR) covers seven virus species . Only the sub-database for influenza virus provides extra functionality, such as an annotation tool. NCBI SARS-CoV-2 Resources is another specific virus database for COVID-19 only. In summary, NCBI Virus serves as the go-to resource when working with NCBI virus-related data, offering a visually appealing and user-friendly interface for virus sequence data and metadata.The search interface of database , NCBI ViReference Viral Database (RVDB) comprises a comprehensive collection of nucleotide sequences, encompassing viral, virus-related, and virus-like sequences  [RVDB provides a curated and a non-redundant subset of GenBank genome sequences, facilitating downstream bioinformatics analyses. The database provides two versions: an unclustered and a clustered version based on sequence similarity. We took the number of sequences available in the unclustered version as an estimate of the number of genomes , while we took the number of sequences available in the clustered version as an estimate of the number of species . Further, old versions of the database are available. Researchers can conveniently download all RVDB sequences in one file, although it should be noted that the large file size (approximately 20 GB) may result in longer download times.The viruses) . The RVDBLAST tool for performing sequence searches against different versions of the RVDB, further enhancing its usability.The database is designed with simplicity in mind, offering user-friendly functionality. Additionally, the database provides a Virus Orthologous Groups Database (VOGDB) is an automatically updated database that provides virus protein family sequences derived from RefSeq virus genomes. This provides a comprehensive representation of viral protein families as virus orthologous groups (VOG) for comparative virus (meta-)genomics. The VOGDB release vog218 contains 38,109 VOGs derived from 610,152 protein sequences (self-reported at https://vogdb.org/reports/release_stats (accessed on 7 August 2023)) from 11,133 species (estimate extrapolated from the number of species in the corresponding NCBI Refseq release 218), allowing for multiple assignments of the same sequence to different VOGs. While VOGDB currently supports searching for VOGs and provides taxonomic information, the direct downloading of a single VOG is not available. Instead, users can access fileshare platforms, or choose from 11 different (compressed) file formats for their downloads, which may be slightly disorganized. Surprisingly, there have been no publications published by the creators of VOGDB to date.The Virxicon is a centralized knowledge base, gathering information about viruses and their associated sequences [Virxicon is a database that maintains the ICTV virus taxonomy, incorporating virus genome sequences from the NCBI Viral Genomes database and GenBank, and annotating them based on the Baltimore classification system. The database comprises a total of 599,538 genome sequences . In their research paper, the authors compare Virxicon with other databases, such as ViralZone, NCBI Virus, and ViPR (now BV-BRC), aiming to combine the strengths of these databases. Virxicon facilitates the bulk download of virus genome sequences with searchable, well-curated metadata, namely Baltimore class, molecular types, and topological resources. However, it is our impression that the database does not provide unique functionality compared to other virus databases. NCBI Virus and BV-BRC provide a larger number of sequences and more extensive functionality related to sequences, including search and tools, while ViralZone serves as a more comprehensive lexicon, including virus sequence download and curated simple metadata. The Virxicon website offers an intuitive and user-friendly interface, providing search and easy access to information.The equences . VirxicoZOVER, a comprehensive database of zoonotic and vector-borne viruses, aims to integrate virological, ecological, and epidemiological information to enhance understanding of animal-associated viruses and their significant impact on human and animal health [ZOVER is a valuable resource, offering a curated subset of NCBI GenBank data, manually collected from the published literature, focused on four specific hosts: bats, rodents, mosquitoes, and ticks. ZOVER was merged from the Database of Bat-associated Viruses and Database of Rodent-associated Viruses. The ZOVER database includes 64,289 gene and genome sequences from 135 genera, combining both protein and nucleotide sequences, making it challenging to differentiate them individually. ZOVER presents data in a well-organized, visualized and user-friendly manner, providing a comprehensive and visually appealing platform for accessing information. ZOVER offers a valuable tool for researchers in the field, as it provides curated and easily accessible data, enhances data visualization, and offers a user-friendly interface for efficient exploration and analysis. Users can easily navigate the database using taxonomy-based searches or various search options, including sequence based, text based or region based.The l health ,41,42. ZThe emergence of databases dedicated to -omics data and analyses represents a remarkable advancement in the field of virology. These specialized resources go beyond traditional databases, providing a next-level platform for researchers to delve into the vast realm of -omics datasets and unlock hidden viral treasures. By focusing on -omics data, which encompass various -‘omics’ disciplines, such as genomics, metagenomics, transcriptomics, and proteomics, these databases offer a comprehensive view of the viral world at a molecular level. These databases serve as central hubs for storing, organizing, and analyzing -omics datasets, enabling researchers to explore uncharted territories and uncover previously unknown virus species.Integrated Microbial Genomes/Virus (IMG/VR)The  provides its own geNomad analysis workflow, with which IMG/VR systematically identifies viral sequences from user-contributed and publicly available datasets, providing researchers with a comprehensive collection of uncultivated virus genomes [JGI database IMG/M, RefSeq database, and three specific virus databases, while enhancing the annotation process with genome quality estimation, up-to-date taxonomic classification, and microbial host taxonomy prediction. IMG/VR offers users a comprehensive platform with abundant information, analysis tools, and links to sub-databases, including detailed meta-information and statistics for each virus. However, for us, its navigation and accessibility pose challenges, necessitating substantial time investment for users to become acquainted with its features. The database contains an impressive collection of 15,677,623 vGenomes , organized into 9,169,185 viral operational taxonomic units  across various viral families and genera. Despite its importance in the study of uncultivated viruses (UViGs), users should be mindful of the complexity of IMG/VR and its lack of user friendliness, requiring a login for full functionality and demanding considerable effort to effectively explore and utilize all available features.The online platform  genomes ,44 with Multi-omics Portal of Virus Infection (MVIP) collects and analyzes virus infection-related high-throughput sequencing data, integrating comprehensive meta-information [MVIP provides external resource links and allows user submissions for broader analyses and database enhancement. MVIP offers valuable information and analysis for specific biosamples, driving advancements in the understanding of virus infection, and provides users with the opportunity to suggest biosamples for integration. Currently, MVIP boasts a dataset comprising approximately 6586 sequencing samples derived from 77 distinct viruses, such as SARS-CoV-2, SARS-CoV, dengue virus (DENV), Zika virus (ZIKV), and Influenza A Virus (IAV), across 33 host species, including Homo sapiens and Mus musculus. MVIP is a visually appealing database that provides comprehensive -omics data analysis capabilities, serving as both a resource for analyzing existing data and a knowledge base for researchers conducting their own sequencing projects, offering insights into the availability of suitable datasets for specific research questions, despite occasional short loading delays.The ormation . It enabViral Host Range Database (VHRdb) is a unique resource that consolidates experimental data on the range of hosts that a virus can infect [VHRdb is an online platform that centralizes experimental data on viral host ranges, allowing users to browse, upload, analyze, and visualize the results. Currently, it contains 17,170 interactions between 776 viruses and 2041 hosts from 20 datasets. Among the 776 viruses in the VHRdb, 303 are linked to the NCBI, representing 279 species from 25 families. The comprehensive overview of virus–host interactions is presented in a visually appealing table, categorizing the relationships into “No infection”, “Intermediate”, and “Infection”. The VHRdb provides extensive and helpful documentation, including quick-start guides, to assist users in navigating and utilizing the database effectively. However, a limitation of the VHRdb is its relatively limited representation of viruses, which are not evenly distributed across various viral families, relying heavily on available studies that may not provide comprehensive coverage. To mitigate this, users can upload their own data for public access or private use. Despite these limitations, the existing studies are visually appealing, allowing for straightforward interpretation and analysis.The n infect . DespiteNCBI VVR database, which specifically addresses seven different viruses as mentioned earlier. For coronaviruses, we rely on the following databases: (1) GISAID, (2) COVID-19 Data Portal, and (3) Stanford Coronavirus Antiviral & Resistance Database (COVDB) [LANL HIV Database, (2) EuResist, (3) HIV Drug Resistance DB, and (4) PSD [EuResit by the time of submission, the investigation could not be conducted as extensively as with other databases.In addition to comprehensive databases, there are numerous virus-specific databases available. If one is working on a specific virus, it is worthwhile to explore specialized databases dedicated to that particular species or group. Here is a brief list of potential databases that primarily focus on a single virus species. One exception is the  (COVDB) ,49,50,51 (4) PSD ,54,55,58HBVdb [PapillomaVirus Episteme (PaVE) database [For Hepatitis B virus, the dedicated database is HBVdb . And fordatabase .Global Core Biodata Resources, which seek to identify invaluable, long-term resources for the life sciences.In addition to virus-specific databases, there exist numerous databases that are also important for virus research in the broader fields of biology and genomics. We would suggest also for interested users to keep an eye on initiatives, such as the UniProt [Rfam database [InterPro database serves as an extensively employed resource, offering multiple sequence alignments and hidden Markov models for protein families [NCBI houses additional non-virus-specific databases, such as the Gene Expression Omnibus, which serves as an international public repository for high-throughput functional genomic datasets, or Sequence Read Archive(SRA), a valuable resource that provides access to biological sequence data, fostering reproducibility and enabling new discoveries through dataset comparisons within the research community [NCBI datasets browser (currently in beta version), which provides easy searchable access to different NCBI databases and NCBI Taxonomy via fact sheets. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive biological database that represents molecular networks and pathways, facilitates analysis of genomic data, and integrates drug labels and disease databases, making it one of the most widely used resources in the field [miRBase is the central repository for microRNA (miRNA) [UniProt  providesdatabase , widely families . The NCBommunity . Of notehe field ,65,66. T (miRNA) . It enabEuropean Virus Bioinformatics Center (EVBC) website, on which a total of 275 entries are linked, sorted by software type , virus family, or functionality [iVirus.us, which provides a platform to access 27 tools and 21 datasets [Additionally, there exist virus-related online platforms that link together pre-existing tools, databases, and datasets. These websites serve as valuable resources for researchers and practitioners seeking to leverage existing resources and foster collaboration within the scientific community. By linking together disparate resources, these platforms contribute to the dissemination and accessibility of scientific information, promoting the efficient utilization of available resources for further research and innovation. One example is the ionality ,69. Anotdatasets ,71.FAIR principles checklist. Where a virus database had a table featuring one virus per row, the entries were evaluated as research objects  and the EuResist database (to which we did not have access by the time of submission). The FAIR scores are based on presence/absence for each of the checklist criteria as was done previously in the context of data deposition of nuclear magnetic resonance data [Many virus databases aim to support the (re)use of virus data and enable processing using machine-learning methods. Both goals can be facilitated by adopting the FAIR principles. We therefore included an evaluation of FAIR properties in our database overview, using nce data . The scoBV-BRC and IMG/VR featured this, the smaller databases such as HBVdb often used an external id, e.g., the NCBI Accession ID or TaxIDs. This might be due to the differing aims of the virus databases, as some are focused on data reuse and machine-readability, while others may have simpler goals, such as cross linking available knowledge. Accessibility for the databases was generally positive owing to web-accessible links and straightforward download options  or for pathogenic virus metadata (see the Genomic Standards Consortium (GSC)), this is not yet the case for metadata for all viruses. This is currently a target for various groups, such as the GSC (which is responsible for the minimum information about sequencing standards that are used by the INSDC repositories), the Gene Ontology consortium, the Genomes Online Database (GOLD) which complements the IMG databases of the Joint Genome Institute JGI, and other efforts such as those of Bernasconi et al. with the viral conceptual model [Choose A License). Overall, this FAIR evaluation was a first for virus databases and highlighted several areas for improvement.In general, the FAIR scores of the content of the active databases reviewed here  FAIRsharing [The Database Commons [ELIXIR bio.tools [NAR database list [To assist users in selecting appropriate databases, scholarly journals and other entities have established catalogs that employ various criteria for indexing databases based on different criteria to improve their findability and accessibility. Here, we describe five catalogs of databases: (1) Rsharing , (3) The Commons , (4) ELIio.tools , and (5)ase list  . However, only 62 of these resources are virus-specific, and only 41 are up-to-date. These 41 resources can be further classified into the 13 that are listed in our curated HIV Drug Interactions, (2) HEP Drug Interactions, (3) Global.health, and (4) HIV and COVID-19 Registry in Europe. These databases were excluded due to their restricted focus, such as focusing only on drug interactions of a particular virus or containing primarily epidemiological data, which did not align with our definition of a comprehensive virus database. Additionally, one of the databases resembled more of a network than a traditional database.The online platform Database Commons is a curated catalog of biological databases that organize databases based on data type, species, and subject matter. It provides detailed metadata for each database, including name, URL, description, hosting institution, and contact information. Within the Database Commons, there are currently 5902 entries listed. Among them, 355 databases fall under the “Data Object” virus category. Of these, 146 are virus-specific, and 36 are considered up to date. These 36 databases can be further categorized as the 17 listed in our curated Disease Monitoring Dashboard, RID, and Virus-CKB). The additional databases were excluded due to their specific nature, such as being more tool-oriented or containing limited data with only two tables rather than meeting the criteria of a comprehensive virus database.The ELIXIR, a ELIXIR) taxonomy errors, (2) naming and labeling errors, (3) missing information, (4) sequences errors, (5) wrong orientation, and (6) chimeric sequences.Despite diligent curation, databases like the  methods . As userNCBI Taxonomy [ICTV entries, supplemented by a number of additional taxa; see ICTV defines only approximately one-fifth of the species mentioned in NCBI. In rare cases, inconsistencies may occur, but they are typically resolved in subsequent updates. The higher number of species in the NCBI Virus database compared to the official ICTV count is primarily attributed to the inclusion of unclassified and unverified taxa. As outlined above, the ICTV serves as the authoritative source for virus taxonomy. Within the ICTV, new taxa are evaluated by an expert committee, and the taxonomic classification extends only up to the rank of species. More detailed subdivisions, such as subspecies or lineages, are not determined by the ICTV but are entered by the submitter to NCBI. TaxIDs are commonly used for taxonomic groups in databases like NCBI and UniProt but not in ICTV. The NCBI Virus database houses around 53,000 virus species, with nearly 1.5% having more than one TaxID assigned ; see NCBI for certain viruses result from the ongoing refinement of virus classification, including species mergers and new TaxID assignments. An additional challenge arises in cases where assigning two TaxIDs to a specific sequence is needed as seen in studies involving integration sites, where a sequence represents both the virus and the host [The Taxonomy  incorporthe host . ImplemeEU255989.1), even though the UTR sequences are missing. The widespread occurrence of this error is exemplified by the Hepatitis C virus in the BV-BRC, where a search for complete genomes yielded 3676 results. Upon closer examination, 69 specific sequences did not meet the expected length of 8500 bp, indicating that these are incomplete genomes.Within the realm of virus labeling, an ongoing systematic error can be observed, where users label sequences as “complete genomes” despite the partial or complete absence of untranslated region (UTR) sequences , for exablastn . This can cause difficulties in forward analysis. A more user-friendly alternative is implemented in NCBI Virus, where the user can build the name of the sequence from predefined properties independently .The naming of fasta headers can also contribute to confusion. Sequences can contain cryptic names , species variations , and sequence information .To maximize the utility of genomic sequences for various purposes, it is essential to collect metadata on the properties of the pathogen and make them available in organized, clear, and consistent formats. Several studies have focused on identifying the necessary minimum of metadata ,80. In tBV-BRC are missing more than half of the described metadata above. Astonishingly, for only 68.12% of the sequences, lineage information is provided  of the 9,763,946 genomes/segments described in the Submitting Data to ENA and NCBI GenBank Submissions, the NCBI Submission Help Page and the NCBI Virus Submission Help Page) [To counteract missing or incorrect metadata, several guides and tools are available to help explain how to submit data to the INSDC repositories (see lp Page) ,82,83. Rlp Page) ,85,86,87lp Page) ,85,86,87lp Page) .NCBI and other databases can contain errors; see Likewise, nucleotide or protein sequences in 1PFI_C-Chain C, PF1 VIRUS STRUCTURE, which is composed of a solitary N nucleotide.In addition to the challenge of differentiating sequencing errors from the impact of rapid viral evolution, in some cases, sequences lack even any meaningful sequence information. A striking illustration of this is observed in the sequence of Furthermore, errors in (reference) sequences can have a cascading effect on subsequent sequences, e.g., during reference-based assemblies, potentially propagating inaccuracies throughout computational downstream analyses. These errors, combined with methodological challenges in genome assemblies, further emphasize the need for scrutiny and validation of genomic data to ensure the reliability of research findings ,87,88. ANCBI default for user-uploaded virus sequences is the positive sense strand, which is counter intuitive, for example, for viruses in Baltimore class 5 (negative single-stranded viruses).Viruses have diverse genomes and are classified into different Baltimore classes based on their genome. The KU059750.1, leads to misinterpretation and further complications. This issue is exacerbated by the fact that many tools do not verify the correct orientation, resulting in nonsensical output that can misguide subsequent analyses. This issue extends to the annotation of functional units, including proteins, which are also provided in reverse order. To identify incorrectly uploaded sequences in terms of orientation, the ORF density was used, which refers to the number of open reading frames (ORFs) found on a particular strand. Out of the 3676 Hepatitis C virus genomes labeled as complete in the BV-BRC, a total of 205 sequences exhibited discrepancies in ORF density, indicating potential errors in their orientation. This highlights a systematic problem in the database.The uploading of sequences in the wrong orientation as seen in examples like the Orthomarburgvirus marburgense isolate sequence Another type of error is the presence of chimeric sequences, where a portion of the sequence is derived from the virus, while another part originates from a non-viral source . It is important to note that such chimeric sequences can arise due to biological factors or process errors, such as assembly mistakes. In extreme cases, this leads to thousands of hits being found in the host genome with a viral sequence, although the originating sequence part is not viral.KY766069. A comparative alignment between this sequence and the two other RefSeq Zika virus sequences clearly demonstrates that the 3′ end of KY766069 does not originate from the Zika virus as illustrated in One example highlighting this issue is observed in the last 280 nucleotides of a specific Zika virus sequence blastn search  exhibits a fragment of the AluSx repetitive element in its 3′ end, resulting in over 200,000 false positive hits in a e <10−4)  on the hNCBI Nucleotide database to ensure reliable and accurate downstream analyses. Errors can arise from various factors, including taxonomy inconsistencies, naming and labeling errors, missing information, sequence errors, wrong orientation, and chimeric sequences. These errors can undermine the validity of downstream results, and they highlight the need for the critical scrutiny and validation of data. Addressing these errors is essential to maintain the integrity and reliability of virus databases for scientific research.In summary, it is crucial to acknowledge the presence of errors in databases like the Here, we provided a comprehensive assessment of active virus databases, defining these as any database last updated in 2022 or later which contains virus-related research data. Our list of 24 databases was compiled through an exhaustive search of active virus databases from two previous reviews of virus databases as well as five catalogs. For the first time, our review includes a thorough evaluation of database usability and contents—including the number of species and sequences contained therein—as well as a FAIRness comparison. We hope this overview will help guide prospective users of these databases. We refrain from suggesting a particular database for use because this highly depends on an individual researcher’s needs. For the knowledge databases that cover a broad spectrum of viruses, we provide a detailed overview and offer suggestions for their potential applications.The content of the virus databases vary widely depending on the scope, number of data types and tools, and number of virus species. Here, we presented in detail 4 knowledge databases, 7 sequences databases, 3 -omics databases and 10 databases focusing on specific viruses. The databases feature a range of -omics datasets integrated and in combination with sampling, host, collection, environmental and other metadata. In terms of virus species represented, several are focused only on one or a few species , while on the other end of the range, the large databases feature upwards of 1 million species. The virus databases also vary in terms of usability, with larger databases sometimes presenting a more complex user experience, in which it takes longer to orientate oneself, although some smaller databases could also improve their usability. In general, the databases we listed here are good in terms of findability and accessibility, but we found that community-wide metadata standard development and explicit listing of formal data usage licenses would improve interoperability and reusability. Future database reviews could include an evaluation of the accessibility of metadata standard definitions  and connection to other databases .NAR database list) and to improve domain-specific metadata .We found that researchers might encounter challenges when using existing catalogs of databases for virus-related databases due to limited virus-specific content and searchable metadata. To address this, efforts are ongoing to curate virus-specific database lists (as in the virus subsection of the NCBI Virus).In part due to FAIRification, we expect that these database catalogs will only improve. We suggest that these catalogs can serve as a useful starting point for any researcher, which can complement curated collections, review articles and word of mouth within specific disciplines specifically for searching a particular database, for example, a particular virus or a certain data type. While finding the right database for one’s research needs may be challenging, using these existing resources is crucial for modern virus research, especially due to the high volume of multi-dimensional data available on viruses. We suggest that the landscape of virus databases be regularly evaluated, and for core virus databases to be included in at least university-level virus-related courses when relevant (e.g., Recent sequencing advancements and the growing interest in viruses within the field of virus bioinformatics have led to rapid changes in the virus database landscape. We illustrated this rapid development by referencing the reviews of Sharma et al. 2015 and Mcleod and Upton 2017 of virus databases and tools ,22, of wINSDC repositories and other institutions to curate existing metadata. We underline the need for clear data submission guidelines and the inclusion of curated sequences together with the regular updating and removal of outdated or redundant sequences. Database content errors can arise from various factors, including taxonomy inconsistencies, naming and labeling errors, missing information, sequence errors, wrong orientation, and chimeric sequences, which can be attributed to either user input or inherent database discrepancies. These errors can undermine the validity of downstream results and highlight the need for the critical scrutiny and validation of data. Addressing these errors is essential to maintain the integrity and reliability of virus databases for scientific research.Ensuring longevity for virus sequence databases includes FAIR principle-like criteria in addition to regular maintenance and updates, the creation of backups and archives, collaboration, funding, and trust, and usage by the community ,18. For In conclusion, the databases listed here represent the current knowledge of viruses, and the current review will help aid future users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gain new insights into the biology, evolution, and transmission of viruses, and develop new strategies to manage virus outbreaks and preserve global health.


Mental disorders are highly prevalent in Germany, and associated with decreased quality of life for those affected as well as high economic burden for the society. The purpose of this study was to determine the excess costs of mental disorders and to examine how these differ with respect to disease severity.We analyzed mean 6-month costs using the baseline data from the RECOVER trial in Hamburg, Germany, which evaluates an innovative stepped-care model for mental disorders. Four severity levels were classified based on the current level of mental illness, loss of functioning, and psychiatric diagnosis. In this work, direct costs  and indirect costs  were estimated using interview-based data on health care utilization and productivity losses. Excess costs were determined by matching a comparison group of the German general population without mental disorders. Group differences in sociodemographic covariates and somatic comorbidities were balanced using entropy balancing. Excess costs by severity levels were estimated using generalized linear models (GLM) with gamma distribution and log-link function.n = 816 and the comparison group included n = 3226 individuals. Mean total 6-month excess costs amounted to 19,075€, with higher indirect excess costs  than direct excess costs (5744€) in total excess costs. The excess costs increased with increasing disease severity, ranging from 6,123€ with mild disease severity (level 1) to 31,883€ with severe mental illness (level 4). Indirect excess costs ranged from 5612€ in level 1 to 21,399€ in level 4, and were statistically significant for all disease severity levels. In contrast, direct excess costs were only statistically significant for the levels 2 to 4, and ranged from 511€ in level 1 to 10,485€ in level 4. The main cost drivers were hospital stays (level 2–4), sick leave and unemployment , and early retirement (level 3–4).Overall, the RECOVER group included Mental disorders are associated with high costs that increase with the level of disease severity, which was also shown for individual ICD-10 diagnosis groups. Due to their influence on costs, indirect costs and disease severity levels should be considered in future cost-of-illness studies of mental disorders.clinicaltrials.gov, trial registration number NCT03459664.The online version contains supplementary material available at 10.1007/s00127-022-02298-8. Mental disorders are associated with a high disease burden, including reduced quality of life and social functioning, as well as increased costs for society , 2. In GBy international standards, the German health care system generally provides universal access, and a high quality of care . Yet, thCost-of-illness studies of mental disorders provide information about the economic impact on society, which can be valuable for health policy decisions. So far, few studies have analyzed the costs of mental disorders as a group in Germany, most recently, the study by Gustavsson and colleagues . This stn = 889), which was collected from 2018 to 2019 by clinically trained interviewers. We matched a control group using a dataset representative of the German general population in age, sex, residence state, and education level (n = 5005) [Data on individuals with mental disorders were obtained from the randomized-controlled trial (RCT) RECOVER, conducted at the University Medical Center Hamburg-Eppendorf in Northern Germany. In the RECOVER care model, several innovative and evidence-based care components such as case management, assertive community treatment, and e-therapy were combined in a cross-sectoral, stepped care approach and compared to regular care with respect to patient-relevant outcomes, costs, and effectiveness . For our = 5005) . The dat = 5005) .Many study participants with mental disorders were directly recruited at the University Medical Center Hamburg-Eppendorf, while other recruitment channels were external partners such as primary care physicians or psychotherapists, or the website of the RECOVER study. Since home treatment was a part of the RECOVER intervention, the study participants were recruited from the catchment area of the study center (≤ 8 km distance). Furthermore, study participants were eligible for inclusion if they were ≥ 16 years old and insured in one of the participating statutory health insurance funds, which together account for a population share of approximately 38%.n = 38), health care utilization and productivity losses (n = 35) from the analysis, resulting in a final sample of n = 816 individuals with mental disorders. Among these were n = 56 individuals with mild disease severity (level 1), n = 306 individuals with moderate disease severity (level 2), n = 294 individuals with moderate to severe disease severity (level 3), and n = 160 individuals with severe mental illness (level 4).We excluded participants with missing values with respect to sociodemographic and clinical covariates (n = 985 participants), with statutory health insurance (exclusion of n = 685 participants), and without missing values (exclusion of n = 109 participants), resulting in a final sample of n = 3226 individuals without mental disorders.As for the control group, we further restricted the data to individuals without self-reported medical diagnosis of any mental disorder   and diagIn this study, four severity levels were distinguished as classified for the stepped-care treatment in the RECOVER study, which was based on the psychiatric diagnosis, the current level of mental illness , and thWe analyzed 6-month direct and indirect costs on the basis of self-reported resource utilization, assessed for the 6 months before the interview using modified versions of the FIMA questionnaire  and FIMPsy questionnaire  , 46. In Direct costs included outpatient, inpatient, and social/informal care services, which were valued with published unit costs for Germany , 48. OutIndirect costs included productivity losses due to sick leave, unemployment, and early retirement and were estimated with the human capital approach. The human capital approach assesses productivity losses to society by taking the entire period of lost working force due to morbidity or death into account, and evaluates indirect costs with income losses . AnotherThe basic approach was to multiply the productivity losses by average daily wages for full-time and part-time employees. In particular, the average monthly gross income for full-time and part-time work was supplemented by the employer's social insurance contributions, and calculated on a daily basis after deduction of vacation days and public holidays, resulting in 265.53 € per day . We estiBefore the analysis of excess costs, differences in sociodemographic covariates and somatic comorbidities between the group of individuals with mental disorders and the control group were balanced to account for potential confounding. Balancing of the groups was performed with entropy balancing (EB) using the corresponding software package in Stata . EB is ap value < 0.05. The excess costs were further estimated for each severity level using the previously calculated EB weights to balance the respective severity level group and the control group. In addition, subgroup analyses were performed for the most frequent primary diagnosis groups according to the ICD-10, as these were represented differently in the severity levels. Since the subdivision according to main diagnosis led to small subgroups in some cases, we only analyzed subgroups with at least 25 observations. For severity level 1, no subgroup analyses were performed. In severity levels 2 and 3, the diagnosis groups neurotic, stress-related and somatoform disorders (ICD-10 F4), mood disorders (ICD-10 F3), and disorders of adult personality and behavior (ICD-10 F6) were analyzed, and in level 4, the diagnosis groups disorders of adult personality and behavior (ICD-10 F6) and schizophrenia, schizotypal and delusional disorders (ICD-10 F2). For better comparability across subgroups, the control groups were adjusted using the same EB weights as in the primary analysis. All analyses were performed in Stata version 16.We estimated costs using generalized linear models (GLM) with a gamma distribution and log-link function to account for the distribution of cost data. Each cost category was analyzed in a GLM, with the respective cost category as dependent variable and the group assignment  as independent variable. The groups were adjusted for observable factors by including the EB weights in the GLM. For each group, the weighted mean costs were calculated. The excess costs were estimated as average marginal effects of the groups, and considered to be statistically significant at a In a first sensitivity analysis, we conducted an unweighted analysis of excess costs to test the influence of the group balancing on costs. In this study, unemployment was considered when calculating indirect costs, which is quite uncommon in cost-of-illness studies. Therefore, we excluded the indirect costs of unemployment in a second sensitivity analysis to test their influence on the total and indirect excess costs.Among the individuals with mental disorders, 46.8% were diagnosed with mood disorders (ICD-10 F3), in 90.3% of the cases major depression, followed by 22.4% diagnosed with neurotic, stress-related and somatoform disorders (ICD-10 F4), 21.6% diagnosed with disorders of adult personality and behavior (ICD-10 F6), 8.1% diagnosed with schizophrenia, schizotypal and delusional disorders (ICD-10 F2), 0.5% diagnosed with behavioral syndromes associated with physiological disturbances and physical factors (ICD-10 F5), 0.5% diagnosed with behavioral and emotional disorders with onset usually occurring in childhood and adolescence (ICD-10 F9), and 0.1% diagnosed with disorders of psychological development (ICD-10 F8). After the adjustment of the control group, the predefined covariates in the groups were almost equally distributed in mean and variance  or neurotic, stress-related and somatoform disorders (ICD-10 F4), and about half of the individuals diagnosed with disorders of adult personality and behavior (ICD-10 F6) (42.6%) or schizophrenia, schizotypal and delusional disorders (ICD-10 F2) (50%). Table S4 provides details on the share of unemployment by severity levels as well as information on the quantity of health care services used.In total, the average 6-month excess costs of mental disorders amounted to 19,075 €  incurred the lowest total excess costs (7513 €) compared to mood disorders (ICD-10 F3)  or disorders of adult personality and behavior (ICD-10 F6) . In contrast, the total excess costs of these diagnosis groups were similarly high in severity level 3 and ranged from 24,195 € to 25,073 €. In level 4, the total excess costs were higher for the diagnosis group schizophrenia, schizotypal and delusional disorders (ICD-10 F2)  than for disorders of adult personality and behavior (ICD-10 F6) . For all subgroups, indirect excess costs were statistically significant and with a share of at least 69% the main contributor to total excess costs. The only subgroup without statistically significant direct excess costs was the diagnosis group disorders of adult personality and behavior (ICD-10 F6) in severity level 2.The unadjusted excess costs in sensitivity analysis 1 (see Table S7) were in general slightly lower compared to the main analysis except for indirect excess costs, resulting from higher unemployment excess costs. In contrast to the main analysis, the unadjusted excess costs of rehabilitation and of social/informal care were not statistically significant, and the excess costs of other outpatient healthcare providers were negatively associated (− 17 €). In sensitivity analysis 2 without the costs of unemployment, the estimates were lower, but the direction and significance of the excess costs have not changed (see Table S7).Overall, mental disorders showed high excess costs, which increased with the levels of disease severity. Direct costs were 8.7-times higher and indirect costs were 8.4-times higher than for individuals without mental disorders. The study sample was mainly composed of individuals diagnosed with mood disorders (ICD-10 F3), neurotic, stress-related and somatoform disorders (ICD-10 F4), disorders of adult personality and behavior (ICD-10 F6), and schizophrenia, schizotypal and delusional disorders (ICD-F2), although the recruitment of study participants was open to different mental disorders. However, these diagnosis groups also appear in public statistics among the most frequent diagnoses in specialist departments for psychiatry and psychotherapy in Germany . AnotherTo our knowledge, no study has yet evaluated the excess costs of mental disorders in Germany. The excess costs of mental disorders in our study are quite high  compared to estimates on the 12-month excess costs of mental disorders in Austria, which reported 1.4-times higher direct and 2.6-times higher indirect costs . In our In this study, the share of indirect costs in total costs was equal to 70%, and thus higher than the share of direct costs. By contrast, population-based estimates for Germany in 2010 reported a share of 41% of indirect costs in total costs . This gaIn this study, data from individuals with mental disorders were drawn from a RCT in the city of Hamburg in Northern Germany. A particular strength of this study was that the diagnoses were obtained in clinically structured interviews by experienced clinicians. This ensured higher data quality than with self-completed questionnaires, medical records or claims data from health insurance funds. The self-reported utilization of health care services and productivity losses were also assessed interview-based for a period of 6 months. For mental disorders in particular, it was shown that more cost categories were captured with survey data than with other data sources . HoweverAnother advantage of this study is that the individuals were insured in different major statutory health insurance funds, as differences between insurers were documented with regard to sociodemographic characteristics and morbidity . HoweverAnother strength of this study was that the use of a comparison group allowed the calculation of excess costs, and thus a precise estimation of disease-related costs. We have balanced the control group using modern approaches. Yet, the data for the control group stem from another study setting and earlier survey period. The different interview modes might also have caused differences in the datasets. For example, it was shown that telephone interviews have a higher risk of social desirability bias than face-to-face interviews . MoreoveThe excess costs of mental disorders in our study were high and increased by levels of disease severity. Based on the study setting, our findings showed that acute phases of illness substantially increased the costs associated with mental disorders compared to population-based estimates. Future research is needed to investigate, whether these findings are also reflected in other diagnosis groups that are underrepresented in this study, as well as in larger study samples in Germany. Cost-of-illness studies serving as the basis for policy interventions and decisions for mental disorders should take indirect costs and the levels of disease severity into account.Supplementary file1 (DOCX 39 KB)Below is the link to the electronic supplementary material.


Herein, we report the design and assemblyof an artificial [Fe4S4]-containing Fischer–Tropschaserelying on the biotin–streptavidin technology. For this purpose,we synthesized a bis-biotinylated [Fe4S4] cofactorwith marked aqueous stability and incorporated it in streptavidin.The effect of the second coordination sphere provided by the proteinenvironment was scrutinized by cyclic voltammetry, highlighting theaccessibility of the doubly reduced [Fe4S4]cluster. The Fischer–Tropschase activity was improved by chemo-geneticmeans for the reduction of CO2 to hydrocarbons with upto 14 turnovers.Iron–sulfurclusters have been reported to catalyzevariousredox transformations, including the multielectron reduction of CO However, the catalytic functionof [Fe4S4] is increasingly recognized.3 While electron transport chains rely on the redox couples [Fe4S4]3+/2+ and [Fe4S4]2+/1+, the oxidation state [Fe4S4]0 is particularly intriguing for catalytic purposes,as such highly reduced species are capable of activating very inertmoieties. For instance, it was reported recently that a radical S-adenosylmethionine (SAM) enzyme of Methanocaldococcusjannaschii catalyzes the coupling of two lipid chains.4 Thereby, an [Fe4S4]0 cluster activates two sp3-carbon centers, ultimatelyleading to the formation of a C–C bond. Further, Suess andco-workers have investigated the electronic configuration of a synthetic[Fe4S4]0 cluster supported by N-heterocycliccarbene ligands.5 The binding of a CO ligandto the FeS core induces an intramolecular valence disproportionation,and the CO-bound Fe site adopts a low-valent Fe1+ oxidationstate. Thereby, the C–O bond exhibits remarkable activation,as evidenced by spectroscopy. However, elucidating the fascinatingproperties of [Fe4S4]0 clusters ischallenged by their pronounced reactivity. Although all-thiolate ligated[Fe4S4]0 clusters have been observedelectrochemically since the 1970s, isolating such clusters has notbeen realized until recently.7Iron–sulfur metallocofactorsare ubiquitous in nature andinvolved in some of the earth’s most fundamental biologicalprocesses. Among them, the cuboidal [Fe4S4]0 cluster, relieson a 3:1 site-differentiated [Fe4S4] cluster,in which the unique iron atom is coordinated to a labile ligand  before being replaced by the substrate/COligand. The 3:1 site-differentiated pattern is found throughout severalclasses of catalytically active FeS proteins, including isoprenoidsynthesis proteins (IspG and IspH), aconitase, (R)-2-hydroxyacyl-CoA dehydratase, and the superfamily of SAM enzymes.11 It has been postulated that these unsaturated forms may be essentialfor reactivity.12The aforementionedlipid-modifying SAM enzyme, as well as Suess’CO-bound [Fe4S4]-containing metalloproteinsto catalyze the reduction of CO and CO2 to hydrocarbons.16 Strikingly, they showed that the reaction was also catalyzed bya synthetic [Fe4S4(SCH2CH2OH)4] cluster in the presence of strong reducing agents,in either aqueous or organic solvents.17 These results contrast with most catalytic systems, which rarelylead to the formation of multiple C–C bonds upon reductionof CO2.20The remarkablecatalytic properties of FeS clusters are furtherhighlighted by the work of the Ribbe and Hu groups. They reportedon the propensity of [Fe4S4] cluster into a protein environment mayenable us to engineer and evolve an artificial metalloenzyme (ArM)for the reduction of CO2 to alka/enes . Anchoring a metallocofactor into a protein scaffoldprovides a well-defined second coordination sphere around the cofactor,thus offering straightforward means of optimizing the catalytic performanceby chemical and genetic methods.21 A scaffoldof particular interest is streptavidin (Sav), thanks to its exceptionallyhigh affinity for biotin.23 In the past 20 years,Sav has proven to be a privileged host for incorporating various biotinylatedcofactors. The resulting ArMs were chemo-genetically optimized tocatalyze various reactions including metathesis, C–H activation,hydroamination, hydrogenation, and hydrogen production.31 Other versatile host proteins that have been used for the generationof ArMs include carbonic anhydrase, hemoproteins, prolyl oligopeptidase,helical bundles, the lactococcal multiresistance regulator, and de novo designed metallopeptides.39 [Fe4S4] clusters have been incorporated intothioredoxin, cytochrome c peroxidase, and several de novo structures.46 Herein, we report on our efforts to optimize the catalytic performanceof an [Fe4S4]-containing FTase based on thebiotin–streptavidin technology.Inspired by these results, we speculated thatembedding a biotinylated[Fe22.14S4] core, we selecteda 3,5-bis(mercaptomethyl)benzene scaffold, which has been shownby Holm and co-workers to bind to two adjacent Fe centers in [Fe4S4] tightly.47 We speculated thatan incoming substrate might displace one of the thiols of the bidentateligand. However, as the thiol remains in proximity of the Fe centerduring and after substrate turnover, the decomposition of the clusterby aquation may be minimized.With the aim of minimizingthe decomposition of the [Fe4S4] cluster with two biotinylated 3,5-bis(mercaptomethyl)benzeneligands—and thus four thiolate donors—to firmly anchorthe cofactor within the biotin-binding vestibule. Relying on QM-MMcalculations, we selected glycine as a spacer to enforce the coordinationof the cofactor to two adjacent biotin-binding sites and thus minimizethe cross-linking of Sav to afford oligomers. The modeled structureof [(Biot-gly)2Fe4S4]·Sav WT is displayed in Supporting Information.The homotetrameric structure ofSav can be described as a dimer-of-dimers, with each dimer consistingof two biotin-binding sites facing each other. Capitalizing on thisfeature, we hypothesized that it might be possible to coordinate the2– to afford the correspondingbis-biotinylated cluster [(Biot-gly)2FeII2FeIII2S4]2– and four equivalents of t-BuSH, which were removed in vacuo.48 The cluster was characterized by UV–vis, HRMS, andparamagnetic 1H NMR, to confirm the purity of the oxygen-sensitivebis-biotinylated cluster [(Biot-gly)2Fe4S4]; see the Supporting Information for experimental details.We set out to synthesizea biotinylated ligand bearing a glycine spacer between the biotinanchor and the 3,5-bis(mercaptomethyl)aniline, 2.34Fe4S4] clusters display limited stabilitytoward water.50 As the thiolate ligands are proneto substitution reactions with coordinating solvents, the additionof excess ligand stabilizes the FeS cores under basic conditions.54 Further, the stability can be improved by using large hydrophilicligands or adding surfactants.56 However, in the absence of excessligand or surfactant, the reported [(RS)4Fe4S4] clusters are only stable up to a water content ofaround 40%. We hypothesized that the chelating nature of the dithiolateligand (Biot-gly) might minimize aquation, thus increasingthe stability of [(Biot-gly)2Fe4S4] in water. Inspired by a publicationby Holm and co-workers, we examined the aqueous stability of [(Biot-gly)2Fe4S4] spectrophotometrically.50 For comparison,the same experiment was conducted with (NMe4)2[(HOCH2CH2S)4Fe4S4] (6), which previously had been reportedto be stable in partially aqueous solutions.50 Accordingly, the two clusters were dissolved in mixtures of DMSOand borate buffer , and the spectral changes were monitoredover time, 4Fe4S4] 6 in DMSO remained unchangedfor >18 h. However, in the presence of 40% borate buffer, the spectrumalready exhibits changes after 1 h. After 18 h, a significantly elevatedbaseline and decreased features at 400 nm are observed, indicativeof cluster decomposition.47 At 99% aqueouscontent, the spectrum is nearly featureless after 15 min. Gratifyingly, [(Biot-gly)2Fe4S4] proved remarkably stable even in 99% borate buffer: no notable spectralchange was apparent for >18 h. To the best of our knowledge, [(Biot-gly)2Fe4S4] represents the first synthetic [Fe4S4] clusterthat is stable in an aqueous solution in the absence of excess ligand.49Typically, [(RS)2.4[(Biot-gly)2Fe4S4] within Sav.Both circular dichroism spectroscopy (CD) and native mass spectroscopy(HRMS) unambiguously highlight the formation of discrete [(Biot-gly)2Fe4S4]2·Sav intramolecular assemblies , rather than the formation of cross-linked, oligomeric [(Biot-gly)2Fe4S4]n·Savm assemblies. As can be appreciated, anchoring of two [(Biot-gly)2Fe4S4] clusters withinhomotetrameric Sav WT is unambiguously confirmed by the presence ofa peak at 68273.8 m/z in nativeHRMS experiments ([(Biot-gly)2Fe4S4]2·Sav WTcalculated peak: 68273.6 m/z), m/z was detected,thus supporting the hypothesis that the topology of [(Biot-gly)2Fe4S4] favors theincorporation of its two biotins in adjacent biotin-binding sitesover the formation of cross-linked Sav aggregates [(Biot-gly)2Fe4S4]n·Savm. Next, a CDtitration was carried out whereby a Sav S112A K121A sample (Sav AAhereafter) was treated with increasing amounts of [(Biot-gly)2Fe4S4]. The gradualappearance of an induced CD signal in the absorbance window of [(Biot-gly)2Fe4S4] confirms that the achiral metal assembly experiences a well-definedchiral environment, thus highlighting its incorporation within Sav.Monitoring the increase in ellipticity at 367 nm reveals a linearincrease up to 2.3 equivalents of [(Biot-gly)2Fe4S4] vs tetrameric Sav, thus confirmingthe incorporation of two equivalents of the bis-biotinylated cofactor [(Biot-gly)2Fe4S4] vs homotetrameric Sav, [(Biot-gly)2Fe4S4]2·Sav AA was examined in a solution containing 1% DMSOand 99% borate buffer ; the spectrum exhibited onlymarginal changes over the course of 18 h, suggesting that the [Fe4S4] core remains intact, Next,we set out to investigate the incorporation of 2.5[(Biot-gly)2Fe4S4] and [(Biot-gly)2Fe4S4]·Sav were investigated bycyclic voltammetry (CV) in an aqueous borate buffer with KPF6 (100 mM) as a supporting electrolyte. The freecofactor [(Biot-gly)2Fe4S4] presents a reversible reduction wave with a half-wave potential(E1/2) of −343 mV ), 4S4]2+ core.It is followed by a second irreversible reduction at −1014mV presumably corresponding to the [Fe4S4]1+/0 couple, 57 The anodic scan after thesecond reduction reveals a nontrivial behavior: (i) a high currentdensity at −649 mV is observed, and (ii) the anodic peak correspondingto the [Fe4S4]1+/2+ process splitsinto two peaks, indicative of some structural changes in the [Fe4S4] core. However, this does not affect the basicintegrity of the cluster as derived from two consecutive CV scans, Figure S10. We hypothesize that a thiolate liganddissociates from the [Fe4S4] core during thesecond reduction.58 The redox processesof [(Biot-gly)2Fe4S4]·Sav are diffusion-controlled. To circumvent this limitation,the FTase was adsorbed on an l-cysteine-modified gold electrodeto scrutinize its redox behavior.59 Thecorresponding cyclic voltammograms reveal that the [Fe4S4]2+/1+ reduction potential of [(Biot-gly)2Fe4S4]·Sav shiftsby +38 mV to −305 mV upon incorporation into Sav AA, 4S4]1+/0 redox couple is observed: the potential shiftsby +500 mV to −514 mV, The electrochemicalproperties of 4S4] clusters. The mostcommonly discussed factors include (i) the nature of the solvent andaccessibility of the cluster to the solvent, (ii) hydrogen bonds anddipoles from backbone amides in proximity of the cluster, (iii) electrostaticeffects, and (iv) the identity of the ligands.63 These factors dictate by-and-largewhich redox couple is operative in FeS proteins. A particular caseis the Fe protein of nitrogenase, which is among the rare proteinsthat stabilize the [Fe4S4]0 state.66 Its ability to stabilize the highly reduced [Fe4S4]0 cluster has been hypothesized to arise from(i) the large number of H-bonds from the protein backbone to the sulfidegroups of the [Fe4S4] core and the thiolateligands, (ii) high solvent accessibility of the [Fe4S4] center, and (iii) dipoles arising from the amide backbone.68 In contrast, the stabilities of synthetic [Fe4S4]0 clusters in an aqueous environment have not been thoroughlyinvestigated. An exception is the cluster [(COOHCH2CH2S)4Fe4S4] (7), whose redox potentials were determined in the presence of excessligand, 57Both synthetic and biological clusters have beenstudied to identifythe first- and second-coordination sphere factors that affect theredox potentials of [Fe[(Biot-gly)2Fe4S4] into Sav AA,marked differencesin the redox behavior are clearly apparent, 4S4]0 species and shift the potentials of the[Fe4S4]2+/1+ and [Fe4S4]1+/0 redox events closer together. The anodicpeak splitting, which was observed during the [Fe4S4]1+/2+ process for the free cofactor after thesecond reduction, disappeared for [(Biot-gly)2Fe4S4]·Sav AA. If operative,the ligand dissociation from the [Fe4S4] coreis potentially minimized by the preorganization of the protein-confinedligands (Biot-gly), which may favor the rapid thiolaterecoordination in the event of ligand  dissociation.Upon embedding the cofactor 4S4] core. To investigatethe effect of these residues on the redox potential, cyclic voltammogramsof four single mutants of [(Biot-gly)2Fe4S4]·Sav were recorded, 4S4]2+/1+ redox couple appears to be affectedby the S112A mutation (+68 mV), whereas the mutation K121A only hada marginal effect. On the other hand, the potential for the [Fe4S4]1+/0 couple ranges from −637to −514 mV, whereby the mutations in both positions S112A andK121A resulted in substantial positive shifts. Thus, it appears thatincreased hydrophobicity around the thiolate ligands of the [Fe4S4]2+ core contributes to stabilizingboth reduction processes, whereby the effect is more pronounced forthe [Fe4S4]1+/0 redox process.As highlighted in [(Biot-gly)2Fe4S4], we measured its redox potentials inthe presence of bovine serum albumin (BSA), [(Biot-gly)2Fe4S4] and the surface of Sav AA was examined by saturating the proteinwith biotin prior to the addition of the cofactor  to minimize the assembly of the ArM. The [Fe4S4]1+/0 couple was affected in both cases andshifted to more positive potentials. However, the measured potentialsare still well below that of [(Biot-gly)2Fe4S4]·Sav. This suggests that incorporationof the cofactor in the biotin-binding vestibule significantly contributesto the stabilization of the [Fe4S4]0 core in [(Biot-gly)2Fe4S4]·Sav.To test how nonspecific hydrophobic interactions influence theredox behavior of 2.62 (1 atm) and Eu(II)-DTPA(E0 = −1.3 V vs Ag/AgCl at pH 8)69 as reductant, [(Biot-gly)2Fe4S4]·Sav AA catalyzes theproduction of short alkanes and alkenes (C1–C4), which were detected by GC-FID and GC-MS. Compared to thefree cofactor, the ArM displays improved turnover numbers (TONs), 3, Na2S, and (Biot-gly) instead of the assembled [(Biot-gly)2Fe4S4] led to minimalbackground activity. This strongly suggests that the FTase [(Biot-gly)2Fe4S4]·Sav AAis the catalyst precursor for the reduction of CO2 to alka/enes.Nonspecific hydrophobic interactions between [(Biot-gly)2Fe4S4] and BSA orBiot·Sav led to slightly increased FTase activity compared tothe free cofactor. However, to achieve maximal TONs, embedding thecofactor in the biotin-binding pocket is essential.In the presence of CO2, some residualFTase activityis detected, presumably due to the reduction of the cosolvent DMF, 13CO3HNa as a CO2 source, the detectionof 13C-alka/enes by GC-MS unambiguously confirms that dissolvedCO2  is indeedthe major C-source of the alka/enes , Figure S20.In the absence of CO2 reduction inwater is the competing production of dihydrogen in the presence ofstrong reducing agents.18 The hydrogenevolution during CO2 reduction with [(Biot-gly)2Fe4S4] and [(Biot-gly)2Fe4S4]·Sav AAwas monitored over time by GC-TCD, 2 and alka/enes follows similar trends: the correspondingfirst-order kinetic fit reveals no catalytic onset, neither for H2 nor for CnHm production. The consumption of the reducing agent leads toleveling-off of CnHm and H2 production after 2 h ([(Biot-gly)2Fe4S4]) and 48 h ([(Biot-gly)2Fe4S4]·Sav AA). However, the addition of Eu(II)-DTPA restores theFTase activity, Figure S21. This suggeststhat the nature of the active catalyst is by-and-large maintainedbeyond the indicated times. Further, [(Biot-gly)2Fe4S4]·Sav AA reactsmuch more slowly than the free [(Biot-gly)2Fe4S4] cofactor, despite overallhigher TON. As observed in the cyclic voltammogram, the free cofactorexhibited structural changes on the CV time scale. If such an event is essential for substratebinding, this might explain the increased CO2 fixationrate . In the presence of Biot·Sav(1 equiv Sav AA and excess biotin added), the FTase activity is verysimilar to that of the free cofactor. This supports the hypothesisthat the catalytically active species is indeed embedded in Sav duringcatalysis.A generalchallenge when performing CO2.7Biot-β-ala) hereafter) instead of the glycinespacer in (Biot-gly). , [2Fe4S4], and [2Fe4S4]·Sav isoforms were preparedas described in Figure S1 for stability assessments, S5 for CD titration, S8 for nativeHRMS, and S12, S17 for cyclic voltammograms). We evaluated the FTaseactivity of both [(Biot-gly)2Fe4S4] and [2Fe4S4] in the presenceof various Sav mutants, [2Fe4S4]·Sav K121D. This highlightsthe critical influence of second coordination sphere interactionsbetween the biotinylated cofactor and close-lying residues. Further,this observation supports the hypothesis that the active catalystis indeed embedded within Sav. Indeed, in the event of [Fe4S4] core decomposition or dissociation from its biotinylatedligand, identical catalytic activities would be expected for all FTases.However, more research is needed to understand the effects of theamino acid residues in the binding pocket on the catalytic activity.Next,we turned to the chemo-genetic optimization of the FTase activity.For chemical optimization purposes, we synthesized and characterizeda 3,5-bis(mercaptomethyl)benzene-bearing ligand with a β-alanine-spacer the coordination ofthe [Fe4S4] cluster to two 3,5-bis(mercaptomethyl)benzeneligands endows the corresponding cofactor with significantly improvedaqueous stability. (ii) The design of a ligand bearing an amino acidspacer between the biotin anchor and the bis-thiolate moiety ensuresthe formation of discrete [(Biot-gly)2Fe4S4]·Sav FTases, rather than cross-linkedSav aggregates. This was confirmed by CD spectroscopy and native MS.(iii) Incorporating [(Biot-gly)2Fe4S4] within various Sav mutants significantlyaffected the redox properties of the corresponding ArM compared tothe free cofactor [(Biot-gly)2Fe4S4]. (iv) The chemo-genetic optimizationof the catalytic performance of the FTase strongly supports the hypothesisthat the metalloprotein is critically involved in the catalytic transformationrather than merely acting as a source of FeS nanoparticles, whichcatalyze the CO2 reduction. Current efforts aim to optimizethe FTase activity further by evaluating a large library of Sav isoforms,including chimeric streptavidin with a shielded active site.Capitalizing on the biotin–streptavidintechnology, we havedesigned, engineered, and genetically improved an artificial Fischer–Tropschaseto convert CO