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      PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API

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          Abstract

          PANTHER (Protein Analysis Through Evolutionary Relationships, http://www.pantherdb.org) is a resource for the evolutionary and functional classification of protein-coding genes from all domains of life. The evolutionary classification is based on a library of over 15,000 phylogenetic trees, and the functional classifications include Gene Ontology terms and pathways. Here, we analyze the current coverage of genes from genomes in different taxonomic groups, so that users can better understand what to expect when analyzing a gene list using PANTHER tools. We also describe extensive improvements to PANTHER made in the past two years. The PANTHER Protein Class ontology has been completely refactored, and 6101 PANTHER families have been manually assigned to a Protein Class, providing a high level classification of protein families and their genes. Users can access the TreeGrafter tool to add their own protein sequences to the reference phylogenetic trees in PANTHER, to infer evolutionary context as well as fine-grained annotations. We have added human enhancer-gene links that associate non-coding regions with the annotated human genes in PANTHER. We have also expanded the available services for programmatic access to PANTHER tools and data via application programming interfaces (APIs). Other improvements include additional plant genomes and an updated PANTHER GO-slim.

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          Most cited references24

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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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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              The Gene Ontology Resource: 20 years and still GOing strong

              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
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 January 2021
                08 December 2020
                08 December 2020
                : 49
                : D1
                : D394-D403
                Affiliations
                Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033, USA
                Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033, USA
                Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033, USA
                Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033, USA
                Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033, USA
                Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033, USA
                Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 323 442 7975; Email: pdthomas@ 123456usc.edu
                Correspondence may also be addressed to Huaiyu Mi. Email: huaiyumi@ 123456usc.edu
                Author information
                http://orcid.org/0000-0001-5801-1974
                http://orcid.org/0000-0002-9074-3507
                Article
                gkaa1106
                10.1093/nar/gkaa1106
                7778891
                33290554
                d8419bf1-ecb5-45e4-b5ca-47c7a53165d6
                © The Author(s) 2020. 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 ( 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
                : 28 October 2020
                : 19 October 2020
                : 15 September 2020
                Page count
                Pages: 10
                Funding
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: 1458808
                Award ID: 1661543
                Award ID: 1917302
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: U41HG002273
                Funded by: National Cancer Institute, DOI 10.13039/100000054;
                Award ID: P01CA196569
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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