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      PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools

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          Abstract

          PANTHER (Protein Analysis Through Evolutionary Relationships, http://pantherdb.org) is a resource for the evolutionary and functional classification of genes from organisms across the tree of life. We report the improvements we have made to the resource during the past two years. For evolutionary classifications, we have added more prokaryotic and plant genomes to the phylogenetic gene trees, expanding the representation of gene evolution in these lineages. We have refined many protein family boundaries, and have aligned PANTHER with the MEROPS resource for protease and protease inhibitor families. For functional classifications, we have developed an entirely new PANTHER GO-slim, containing over four times as many Gene Ontology terms as our previous GO-slim, as well as curated associations of genes to these terms. Lastly, we have made substantial improvements to the enrichment analysis tools available on the PANTHER website: users can now analyze over 900 different genomes, using updated statistical tests with false discovery rate corrections for multiple testing. The overrepresentation test is also available as a web service, for easy addition to third-party sites.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          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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            PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification.

            P. Thomas (2003)
            The PANTHER database was designed for high-throughput analysis of protein sequences. One of the key features is a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators have associated the ontology terms with groups of protein sequences rather than individual sequences. Statistical models (Hidden Markov Models, or HMMs) are built from each of these groups. The advantage of this approach is that new sequences can be automatically classified as they become available. To ensure accurate functional classification, HMMs are constructed not only for families, but also for functionally distinct subfamilies. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, are available for each family. The current version of the PANTHER database includes training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs have been used to classify gene products across the entire genomes of human, and Drosophila melanogaster. The ontology terms and protein families and subfamilies, as well as Drosophila gene c;assifications, can be browsed and searched for free. Due to outstanding contractual obligations, access to human gene classifications and to protein family trees and multiple sequence alignments will temporarily require a nominal registration fee. PANTHER is publicly available on the web at http://panther.celera.com.
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              Applications for protein sequence–function evolution data: mRNA/protein expression analysis and coding SNP scoring tools

              Paul Thomas, Anish Kejariwal, Nan Guo … (2006)
              The vast amount of protein sequence data now available, together with accumulating experimental knowledge of protein function, enables modeling of protein sequence and function evolution. The PANTHER database was designed to model evolutionary sequence–function relationships on a large scale. There are a number of applications for these data, and we have implemented web services that address three of them. The first is a protein classification service. Proteins can be classified, using only their amino acid sequences, to evolutionary groups at both the family and subfamily levels. Specific subfamilies, and often families, are further classified when possible according to their functions, including molecular function and the biological processes and pathways they participate in. The second application, then, is an expression data analysis service, where functional classification information can help find biological patterns in the data obtained from genome-wide experiments. The third application is a coding single-nucleotide polymorphism scoring service. In this case, information about evolutionarily related proteins is used to assess the likelihood of a deleterious effect on protein function arising from a single substitution at a specific amino acid position in the protein. All three web services are available at .
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                Author and article information

                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 (Print): 08 January 2019
                Publication date (Electronic): 08 November 2018
                Publication date PMC-release: 08 November 2018
                Volume: 47
                Issue: Database issue , Database issue
                Pages: D419-D426
                Affiliations
                [1 ]Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
                [2 ]School of Life Sciences, Guangzhou University, Guangzhou 510006, China
                Author notes
                To whom correspondence should be addressed. Tel: +1 323 442 7975; Fax: +1 323 442 7995; Email: pdthomas@ 123456usc.edu . Correspondence may also be addressed to Huaiyu Mi. Email: huaiyumi@ 123456usc.edu
                Author information
                http://orcid.org/0000-0002-9074-3507
                Article
                Publisher ID: gky1038
                DOI: 10.1093/nar/gky1038
                PMC ID: 6323939
                PubMed ID: 30407594
                SO-VID: 36f0e3d6-a584-42ba-961d-871166b9e8de
                Copyright © © The Author(s) 2018. 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 : 17 October 2018
                Date revision received : 13 October 2018
                Date received : 15 September 2018
                Page count
                Pages: 8
                Funding
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: 1458808
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: U41HG002273
                Categories
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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