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      eggNOG 6.0: enabling comparative genomics across 12 535 organisms

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          Abstract

          The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.

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          The Protein Data Bank.

          The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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            The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

            Abstract 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/.
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              UniProt: the universal protein knowledgebase in 2021

              (2020)
              Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                06 January 2023
                18 November 2022
                18 November 2022
                : 51
                : D1
                : D389-D394
                Affiliations
                Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC) , Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
                Department of Molecular Life Sciences, University of Zurich , 8057 Zurich, Switzerland
                SIB Swiss Institute of Bioinformatics , 1015 Lausanne, Switzerland
                Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC) , Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
                Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC) , Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
                Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC) , Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
                Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM) , Madrid 28040, Spain
                Department of Medical Microbiology, Amsterdam University Medical Centers , Amsterdam, The Netherlands
                Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen , 2200 Copenhagen N, Denmark
                University of Vienna, Centre for Microbiology and Environmental Systems Science , Djerassiplatz 11030, Vienna, Austria
                Biobyte solutions GmbH , Bothestr. 142, 69117 Heidelberg, Germany
                Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen , 2200 Copenhagen N, Denmark
                Structural and Computational Biology Unit, European Molecular Biology Laboratory , 69117 Heidelberg, Germany
                Yonsei Frontier Lab (YFL), Yonsei University , 03722 Seoul, South Korea
                Department of Bioinformatics, Biocenter, University of Würzburg , 97074 Würzburg, Germany
                Department of Molecular Life Sciences, University of Zurich , 8057 Zurich, Switzerland
                SIB Swiss Institute of Bioinformatics , 1015 Lausanne, Switzerland
                Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC) , Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
                Author notes
                To whom correspondence should be addressed. Tel: +34 910679202; Email: j.huerta@ 123456csic.es
                Correspondence may also be addressed to Christian von Mering. Tel: +41 446353147; Email: mering@ 123456imls.uzh.ch
                Correspondence may also be addressed to Peer Bork. Tel: +49 62213878526; Email: bork@ 123456embl.de
                Author information
                https://orcid.org/0000-0002-9844-7999
                https://orcid.org/0000-0002-4052-5069
                https://orcid.org/0000-0001-7292-8981
                https://orcid.org/0000-0001-5263-533X
                https://orcid.org/0000-0003-1553-8295
                https://orcid.org/0000-0001-6831-4557
                https://orcid.org/0000-0002-0592-7791
                https://orcid.org/0000-0003-3560-4288
                https://orcid.org/0000-0001-7885-715X
                https://orcid.org/0000-0002-2627-833X
                https://orcid.org/0000-0001-7734-9102
                https://orcid.org/0000-0003-4195-5025
                Article
                gkac1022
                10.1093/nar/gkac1022
                9825578
                36399505
                00193733-3480-44d3-ab40-543184dc1868
                © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 October 2022
                : 17 October 2022
                : 15 September 2022
                Page count
                Pages: 6
                Funding
                Funded by: National Programme for Fostering Excellence in Scientific and Technical Research;
                Award ID: PGC2018-098073-A-I00 MCIU/AEI/FEDER
                Funded by: Chan Zuckerberg Initiative, DOI 10.13039/100014989;
                Award ID: 2020-218584
                Funded by: Silicon Valley Community Foundation, DOI 10.13039/100000923;
                Funded by: Severo Ochoa Centres of Excellence Programme from the State Research Agency (AEI) of Spain;
                Award ID: SEV-2016–0672
                Award ID: 2017–2021
                Funded by: Research Technical Support Staff Aid;
                Award ID: PTA2019-017593-I/AEI/10.13039/501100011033
                Funded by: Novo Nordisk Foundation, DOI 10.13039/501100009708;
                Award ID: NNF14CC0001
                Funded by: Swiss Institute of Bioinformatics, DOI 10.13039/501100021373;
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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