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      PRGdb 3.0: a comprehensive platform for prediction and analysis of plant disease resistance genes

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          Abstract

          The Plant Resistance Genes database (PRGdb; http://prgdb.org) has been redesigned with a new user interface, new sections, new tools and new data for genetic improvement, allowing easy access not only to the plant science research community but also to breeders who want to improve plant disease resistance. The home page offers an overview of easy-to-read search boxes that streamline data queries and directly show plant species for which data from candidate or cloned genes have been collected. Bulk data files and curated resistance gene annotations are made available for each plant species hosted. The new Gene Model view offers detailed information on each cloned resistance gene structure to highlight shared attributes with other genes. PRGdb 3.0 offers 153 reference resistance genes and 177 072 annotated candidate Pathogen Receptor Genes (PRGs). Compared to the previous release, the number of putative genes has been increased from 106 to 177 K from 76 sequenced Viridiplantae and algae genomes. The DRAGO 2 tool, which automatically annotates and predicts (PRGs) from DNA and amino acid with high accuracy and sensitivity, has been added. BLAST search has been implemented to offer users the opportunity to annotate and compare their own sequences. The improved section on plant diseases displays useful information linked to genes and genomes to connect complementary data and better address specific needs. Through, a revised and enlarged collection of data, the development of new tools and a renewed portal, PRGdb 3.0 engages the plant science community in developing a consensus plan to improve knowledge and strategies to fight diseases that afflict main crops and other plants.

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          Hidden Markov model speed heuristic and iterative HMM search procedure

          L Steven Johnson, Sean R. Eddy, Elon Portugaly (2010)
          Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.
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            The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community.

            Seung Rhee, William D. Beavis, Tanya Berardini … (2003)
            Arabidopsis thaliana is the most widely-studied plant today. The concerted efforts of over 11 000 researchers and 4000 organizations around the world are generating a rich diversity and quantity of information and materials. This information is made available through a comprehensive on-line resource called the Arabidopsis Information Resource (TAIR) (http://arabidopsis.org), which is accessible via commonly used web browsers and can be searched and downloaded in a number of ways. In the last two years, efforts have been focused on increasing data content and diversity, functionally annotating genes and gene products with controlled vocabularies, and improving data retrieval, analysis and visualization tools. New information include sequence polymorphisms including alleles, germplasms and phenotypes, Gene Ontology annotations, gene families, protein information, metabolic pathways, gene expression data from microarray experiments and seed and DNA stocks. New data visualization and analysis tools include SeqViewer, which interactively displays the genome from the whole chromosome down to 10 kb of nucleotide sequence and AraCyc, a metabolic pathway database and map tool that allows overlaying expression data onto the pathway diagrams. Finally, we have recently incorporated seed and DNA stock information from the Arabidopsis Biological Resource Center (ABRC) and implemented a shopping-cart style on-line ordering system.
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              The Sol Genomics Network (SGN)—from genotype to phenotype to breeding

              Noe Fernandez-Pozo, Naama Menda, Jeremy Edwards … (2014)
              The Sol Genomics Network (SGN, http://solgenomics.net) is a web portal with genomic and phenotypic data, and analysis tools for the Solanaceae family and close relatives. SGN hosts whole genome data for an increasing number of Solanaceae family members including tomato, potato, pepper, eggplant, tobacco and Nicotiana benthamiana. The database also stores loci and phenotype data, which researchers can upload and edit with user-friendly web interfaces. Tools such as BLAST, GBrowse and JBrowse for browsing genomes, expression and map data viewers, a locus community annotation system and a QTL analysis tools are available. A new tool was recently implemented to improve Virus-Induced Gene Silencing (VIGS) constructs called the SGN VIGS tool. With the growing genomic and phenotypic data in the database, SGN is now advancing to develop new web-based breeding tools and implement the code and database structure for other species or clade-specific databases.
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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): 04 January 2018
                Publication date (Electronic): 16 November 2017
                Publication date PMC-release: 16 November 2017
                Volume: 46
                Issue: Database issue , Database issue
                Pages: D1197-D1201
                Affiliations
                Sequentia Biotech SL, Calle Comte D’Urgell 240, 08036 Barcelona, Spain
                Dipartimento di Agraria, Università di Napoli ‘Federico II’, Via Università 100, 80055 Portici, Italy
                Author notes
                To whom correspondence should be addressed. Tel: +39 08 12 53 94 31; Fax: +39 08 12 53 94 31; Email: ercolano@ 123456unina.it . Correspondence may also be addressed to Riccardo Aiese Cigliano. Tel: +34 93 010 73 68; Fax: +34 93 010 73 68; Email: raiesecigliano@ 123456sequentiabiotech.com . Correspondence may also be addressed to Walter Sanseverino. Tel: +34 93 010 73 68; Fax: +34 93 010 73 68; Email: wsanseverino@ 123456sequentiabiotech.com

                These authors contributed equally to the paper as first authors.

                Author information
                http://orcid.org/0000-0002-9058-6994
                http://orcid.org/0000-0003-3324-5912
                Article
                Publisher ID: gkx1119
                DOI: 10.1093/nar/gkx1119
                PMC ID: 5753367
                PubMed ID: 29156057
                SO-VID: f5f67e1a-f703-4679-b1b9-96a167dfe1be
                Copyright © © The Author(s) 2017. 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-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date accepted : 25 October 2017
                Date revision received : 19 October 2017
                Date received : 15 September 2017
                Page count
                Pages: 5
                Categories
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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