Literature DB >> 17237037

A new versatile database created for geneticists and breeders to link molecular and phenotypic data in perennial crops: the AppleBreed DataBase.

A Antofie1, M Lateur, R Oger, A Patocchi, C E Durel, W E Van de Weg.   

Abstract

OBJECTIVE: AppleBreed DataBase (DB) aims to store genotypic and phenotypic data from multiple pedigree verified plant populations (crosses, breeding selections and commercial cultivars) so that they are easily accessible for geneticists and breeders. It will help in elucidating the genetics of economically important traits, in identifying molecular markers associated with agronomic traits, in allele mining and in choosing the best parental cultivars for breeding. It also provides high traceability of data over generations, years and localities. AppleBreed DB could serve as a generic database design for other perennial crops with long economic lifespans, long juvenile periods and clonal propagation.
RESULTS: AppleBreed DB is organized as a relational database. The core element is the GENOTYPE entity, which has two sub-classes at the physical level: TREE and DNA-SAMPLE. This approach facilitates all links between plant material, phenotypic and molecular data. The entities TREE, DNA-SAMPLE, PHENOTYPE and MOLECULAR DATA allow multi-annual observations to be stored as individual samples of individual trees, even if the nature of these observations differs greatly (e.g. molecular data on parts of the apple genome, physico-chemical measurements of fruit quality traits, and evaluation of disease resistance). AppleBreed DB also includes synonyms for cultivars and pedigrees. Finally, it can be loaded and explored through the web, and comes with tools to present basic statistical overviews and with validation procedures for phenotypic and marker data to certify data quality. AppleBreed DB was developed initially as a tool for scientists involved in apple genetics within the framework of the European project, 'High-quality Disease Resistance in Apples for Sustainable Agriculture' (HiDRAS), but it is also applicable to many other perennial crops.

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Year:  2007        PMID: 17237037     DOI: 10.1093/bioinformatics/btm013

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

1.  Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple.

Authors:  M C A M Bink; J Jansen; M Madduri; R E Voorrips; C-E Durel; A B Kouassi; F Laurens; F Mathis; C Gessler; D Gobbin; F Rezzonico; A Patocchi; M Kellerhals; A Boudichevskaia; F Dunemann; A Peil; A Nowicka; B Lata; M Stankiewicz-Kosyl; K Jeziorek; E Pitera; A Soska; K Tomala; K M Evans; F Fernández-Fernández; W Guerra; M Korbin; S Keller; M Lewandowski; W Plocharski; K Rutkowski; E Zurawicz; F Costa; S Sansavini; S Tartarini; M Komjanc; D Mott; A Antofie; M Lateur; A Rondia; L Gianfranceschi; W E van de Weg
Journal:  Theor Appl Genet       Date:  2014-02-25       Impact factor: 5.699

2.  A genotypic and phenotypic information source for marker-assisted selection of cereals: the CEREALAB database.

Authors:  Justyna Milc; Antonio Sala; Sonia Bergamaschi; Nicola Pecchioni
Journal:  Database (Oxford)       Date:  2011-01-18       Impact factor: 3.451

3.  Damming the genomic data flood using a comprehensive analysis and storage data structure.

Authors:  Marc Bouffard; Michael S Phillips; Andrew M K Brown; Sharon Marsh; Jean-Claude Tardif; Tibor van Rooij
Journal:  Database (Oxford)       Date:  2010-12-15       Impact factor: 3.451

  3 in total

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