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      OrthoDB in 2020: evolutionary and functional annotations of orthologs

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          Abstract

          OrthoDB provides evolutionary and functional annotations of orthologs, inferred for a vast number of available organisms. OrthoDB is leading in the coverage and genomic diversity sampling of Eukaryotes, Prokaryotes and Viruses, and the sampling of Bacteria is further set to increase three-fold. The user interface has been enhanced in response to the massive growth in data. OrthoDB provides three views on the data: (i) a list of orthologous groups related to a user query, which are now arranged to visualize their hierarchical relations, (ii) a detailed view of an orthologous group, now featuring a Sankey diagram to facilitate navigation between the levels of orthology, from more finely-resolved to more general groups of orthologs, as well as an arrangement of orthologs into an interactive organism taxonomy structure, and (iii) we added a gene-centric view, showing the gene functional annotations and the pair-wise orthologs in example species. The OrthoDB standalone software for delineation of orthologs, Orthologer, is freely available. Online BUSCO assessments and mapping to OrthoDB of user-uploaded data enable interactive exploration of related annotations and generation of comparative charts. OrthoDB strives to predict orthologs from the broadest coverage of species, as well as to extensively collate available functional annotations, and to compute evolutionary annotations such as evolutionary rate and phyletic profile. OrthoDB data can be assessed via SPARQL RDF, REST API, downloaded or browsed online from https://orthodb.org.

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          UniProt: a worldwide hub of protein knowledge

          (2018)
          Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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            Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

            Nuala A. O'Leary, Mathew Wright, J. Rodney Brister … (2015)
            The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55 000 organisms (>4800 viruses, >40 000 prokaryotes and >10 000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.
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              The Gene Ontology Resource: 20 years and still GOing strong

              Jurg Bahler (2019)
              Abstract The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.
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                Author and article information

                Contributors
                Evgeny M Zdobnov:
                ORCID: http://orcid.org/0000-0002-5178-1498
                Dmitry Kuznetsov:
                ORCID: http://orcid.org/0000-0002-9972-947X
                Fredrik Tegenfeldt
                Mosè Manni:
                ORCID: http://orcid.org/0000-0002-4146-6523
                Matthew Berkeley
                Evgenia V Kriventseva:
                ORCID: http://orcid.org/0000-0003-4835-7562
                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 08 January 2021
                Publication date (Electronic): 16 November 2020
                Publication date PMC-release: 16 November 2020
                Volume: 49
                Issue: D1
                Pages: D389-D393
                Affiliations
                Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland, and Swiss Institute of Bioinformatics , rue Michel-Servet 1, 1211 Geneva, Switzerland
                Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland, and Swiss Institute of Bioinformatics , rue Michel-Servet 1, 1211 Geneva, Switzerland
                Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland, and Swiss Institute of Bioinformatics , rue Michel-Servet 1, 1211 Geneva, Switzerland
                Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland, and Swiss Institute of Bioinformatics , rue Michel-Servet 1, 1211 Geneva, Switzerland
                Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland, and Swiss Institute of Bioinformatics , rue Michel-Servet 1, 1211 Geneva, Switzerland
                Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland, and Swiss Institute of Bioinformatics , rue Michel-Servet 1, 1211 Geneva, Switzerland
                Author notes
                To whom correspondence should be addressed. Tel: +41 22 379 54 32; Email: evgenia.kriventseva@ 123456unige.ch
                Correspondence may also be addressed to Evgeny Zdobnov. Tel: +41 22 379 59 73; Email: evgeny.zdobnov@ 123456unige.ch
                Author information
                http://orcid.org/0000-0002-5178-1498
                http://orcid.org/0000-0002-9972-947X
                http://orcid.org/0000-0002-4146-6523
                http://orcid.org/0000-0003-4835-7562
                Article
                Publisher ID: gkaa1009
                DOI: 10.1093/nar/gkaa1009
                PMC ID: 7779051
                PubMed ID: 33196836
                SO-VID: 24df2c6e-7be0-4fc1-95be-d269abbfa0a9
                Copyright © © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

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

                History
                Date accepted : 29 October 2020
                Date revision received : 12 October 2020
                Date received : 15 September 2020
                Related
                10.1093/nar/gkaa1018
                Page count
                Pages: 5
                Funding
                Funded by: Swiss National Science Foundation, DOI 10.13039/501100001711;
                Award ID: 310030_189062
                Funded by: Swiss Institute of Bioinformatics SERI;
                Categories
                Subject: AcademicSubjects/SCI00010
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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