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      qPrimerDB: a thermodynamics-based gene-specific qPCR primer database for 147 organisms

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          Abstract

          Real-time quantitative polymerase chain reaction (qPCR) is one of the most important methods for analyzing the expression patterns of target genes. However, successful qPCR experiments rely heavily on the use of high-quality primers. Various qPCR primer databases have been developed to address this issue, but these databases target only a few important organisms. Here, we developed the qPrimerDB database, founded on an automatic gene-specific qPCR primer design and thermodynamics-based validation workflow. The qPrimerDB database is the most comprehensive qPCR primer database available to date, with a web front-end providing gene-specific and pre-computed primer pairs across 147 important organisms, including human, mouse, zebrafish, yeast, thale cress, rice and maize. In this database, we provide 3331426 of the best primer pairs for each gene, based on primer pair coverage, as well as 47760359 alternative gene-specific primer pairs, which can be conveniently batch downloaded. The specificity and efficiency was validated for qPCR primer pairs for 66 randomly selected genes, in six different organisms, through qPCR assays and gel electrophoresis. The qPrimerDB database represents a valuable, timesaving resource for gene expression analysis. This resource, which will be routinely updated, is publically accessible at http://biodb.swu.edu.cn/qprimerdb.

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          PLAZA 3.0: an access point for plant comparative genomics

          Comparative sequence analysis has significantly altered our view on the complexity of genome organization and gene functions in different kingdoms. PLAZA 3.0 is designed to make comparative genomics data for plants available through a user-friendly web interface. Structural and functional annotation, gene families, protein domains, phylogenetic trees and detailed information about genome organization can easily be queried and visualized. Compared with the first version released in 2009, which featured nine organisms, the number of integrated genomes is more than four times higher, and now covers 37 plant species. The new species provide a wider phylogenetic range as well as a more in-depth sampling of specific clades, and genomes of additional crop species are present. The functional annotation has been expanded and now comprises data from Gene Ontology, MapMan, UniProtKB/Swiss-Prot, PlnTFDB and PlantTFDB. Furthermore, we improved the algorithms to transfer functional annotation from well-characterized plant genomes to other species. The additional data and new features make PLAZA 3.0 (http://bioinformatics.psb.ugent.be/plaza/) a versatile and comprehensible resource for users wanting to explore genome information to study different aspects of plant biology, both in model and non-model organisms.
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            A Chado case study: an ontology-based modular schema for representing genome-associated biological information.

            A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused. Chado is a relational database schema now being used to manage biological knowledge for a wide variety of organisms, from human to pathogens, especially the classes of information that directly or indirectly can be associated with genome sequences or the primary RNA and protein products encoded by a genome. Biological databases that conform to this schema can interoperate with one another, and with application software from the Generic Model Organism Database (GMOD) toolkit. Chado is distinctive because its design is driven by ontologies. The use of ontologies (or controlled vocabularies) is ubiquitous across the schema, as they are used as a means of typing entities. The Chado schema is partitioned into integrated subschemas (modules), each encapsulating a different biological domain, and each described using representations in appropriate ontologies. To illustrate this methodology, we describe here the Chado modules used for describing genomic sequences. GMOD is a collaboration of several model organism database groups, including FlyBase, to develop a set of open-source software for managing model organism data. The Chado schema is freely distributed under the terms of the Artistic License (http://www.opensource.org/licenses/artistic-license.php) from GMOD (www.gmod.org).
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              Sequence mapping by electronic PCR.

              The highly specific and sensitive PCR provides the basis for sequence-tagged sites (STSs), unique landmarks that have been used widely in the construction of genetic and physical maps of the human genome. Electronic PCR (e-PCR) refers to the process of recovering these unique sites in DNA sequences by searching for subsequences that closely match the PCR primers and have the correct order, orientation, and spacing that they could plausibly prime the amplification of a PCR product of the correct molecular weight. A software tool was developed to provide an efficient implementation of this search strategy and allow the sort of en masse searching that is required for modern genome analysis. Some sample searches were performed to demonstrate a number of factors that can affect the likelihood of obtaining a match. Analysis of one large sequence database record revealed the presence of several microsatellite and gene-based markers and allowed the exact base-pair distances among them to be calculated. This example provides a demonstration of how e-PCR can be used to integrate the growing body of genomic sequence data with existing maps, reveal relationships among markers that existed previously on different maps, and correlate genetic distances with physical distances.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                04 January 2018
                21 August 2017
                21 August 2017
                : 46
                : Database issue , Database issue
                : D1229-D1236
                Affiliations
                College of Agronomy and Biotechnology, Southwest University, Beibei, Chongqing 400715, China
                Academy of Agricultural Sciences, Southwest University, Beibei, Chongqing 400715, China
                State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqing 400715, China
                Shennong Class, Southwest University, Beibei, Chongqing 400715, China
                College of Resources and Environment, Southwest University, Chongqing 400715, China
                Key Laboratory of Molecular Genetics, China National Tobacco Corporation, Guizhou Academy of Tobacco Science, Guiyang 550081, China
                Upland Flue-Cured Tobacco Quality and Ecology Key Laboratory of China Tobacco, Guizhou Academy of Tobacco Science, Guiyang 550081, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 23 68251264; Fax: +86 23 68251264; Email: drlukun@ 123456swu.edu.cn . Correspondence may also be addressed to Kai Zhang. Tel: +86 23 68250744; Fax: +86 23 68250744; Email: kaizhang2013@ 123456gmail.com . Correspondence may also be addressed to Jiana Li. Tel: +86 23 68250642; Fax: +86 23 68250701; Email: ljn1950@ 123456swu.edu.cn

                These authors contributed equally to this paper as first authors.

                Author information
                http://orcid.org/0000-0003-1370-8633
                Article
                gkx725
                10.1093/nar/gkx725
                5753361
                28977518
                63a90805-cf9a-4178-8b8e-85df607f8c3f
                © The Author(s) 2017. 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-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
                : 08 August 2017
                : 27 July 2017
                : 03 July 2017
                Page count
                Pages: 8
                Categories
                Database Issue

                Genetics
                Genetics

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