The Chado Natural Diversity module: a new generic database schema for large-scale phenotyping and genotyping data

Jung, Sook; Menda, Naama; Redmond, Seth; Buels, Robert; Friesen, Maren; Bendaña, Yuri R.; Sanderson, Lacey-Anne; Lapp, Hilmar; Lee, Taein; MacCallum, Bob; Bett, Kirstin E.; Cain, Scott; Clements, D. L.; Mueller, Lukas A.; Main, Dorrie

doi:10.1093/database/bar051

Cited by 31 publications

(46 citation statements)

References 32 publications

(41 reference statements)

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“…The sol genomics database is clade-oriented, meaning that it contains data for a number of other related species, as well as common model plant species. Furthermore, ontology terms have been developed for the Solanaceae to provide a standardized vocabulary for describing their phenotypes (Cooper et al 2013;Jung et al 2011;Menda et al 2008).…”

Section: Resourcesmentioning

confidence: 99%

Advances in tomato research in the post-genome era

Menda

Strickler

Mueller

2013

Plant Biotechnology

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The tomato (Solanum lycopersicum) draft genome, along with a draft of the wild relative, Solanum pimpinellifolium, were released in 2012, almost a decade after the International Tomato Genome project was initiated. Tomato is an important domesticated crop species, as well as a model organism for many aspects of plant biology such as fleshy fruit development, ripening, disease resistance, plant architecture, and compound leaf development. For these reasons, there has been a substantial effort for producing a high quality reference genome that will serve as an anchor for tomato species, and for closely related Solanaceae plants. The utility of this genome has already been demonstrated by a relatively large number of studies that have been published since the release of the sequence, covering a wide range of topics including gene expression, genetic diversity, phylogeny, comparative genomics, and epigenetics. With the availability of the potato genome, it is now possible to perform detailed comparative genomic analysis of gene families in the Solanaceae, facilitated by conservation and synteny between their genomes. A large number of ongoing efforts will result in the sequencing of hundreds of wild and domesticated tomato accessions from various populations, uncovering the breeding history of tomatoes and introducing new genomic technologies to accelerate breeding processes. In this review, we provide an overview of the origins of tomato and its position in the wider Solanaceae, and demonstrate the impact of the tomato genome sequence on Solanaceae research on the basis of recent literature that has made use of this new resource.

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Section: Resourcesmentioning

confidence: 99%

Advances in tomato research in the post-genome era

Menda

Strickler

Mueller

2013

Plant Biotechnology

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“…However, to our knowledge none of them are capable of reflecting the inheritance patterns of individual mutations through the successive rounds of seed amplifications, line crossing or mutagenesis encountered in a typical research laboratory. Indeed, most programs have been designed for managers of plant transformation or greenhouse facilities that use standardized procedures (Scott et al, 2003; Henry et al, 2008; Kohl and Gremmels, 2010; Hanke et al, 2014), or for plant breeding laboratories facing large sets of phenotyping and genotyping data derived from accession sequencing or QTL mapping (Lee et al, 2005; Jung et al, 2011; Milc et al, 2011; Love et al, 2012). Nevertheless, several software programs have been developed to track plant lines in basic research laboratories, such as PlantDB and Phytotracker (Exner et al, 2008; Nieuwland et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

SeedUSoon: A New Software Program to Improve Seed Stock Management and Plant Line Exchanges between Research Laboratories

et al. 2017

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Plant research is supported by an ever-growing collection of mutant or transgenic lines. In the past, a typical basic research laboratory would focus on only a few plant lines that were carefully isolated from collections of lines containing random mutations. The subsequent technological breakthrough in high-throughput sequencing, combined with novel and highly efficient mutagenesis techniques (including site-directed mutagenesis), has led to a recent exponential growth in plant line collections used by individual researchers. Tracking the generation and genetic properties of these genetic resources is thus becoming increasingly challenging for researchers. Another difficulty for researchers is controlling the use of seeds protected by a Material Transfer Agreement, as often only the original recipient of the seeds is aware of the existence of such documents. This situation can thus lead to difficult legal situations. Simultaneously, various institutions and the general public now demand more information about the use of genetically modified organisms (GMOs). In response, researchers are seeking new database solutions to address the triple challenge of research competition, legal constraints, and institutional/public demands. To help plant biology laboratories organize, describe, store, trace, and distribute their seeds, we have developed the new program SeedUSoon, with simplicity in mind. This software contains data management functions that allow the separate tracking of distinct mutations, even in successive crossings or mutagenesis. SeedUSoon reflects the biotechnological diversity of mutations and transgenes contained in any specific line, and the history of their inheritance. It can facilitate GMO certification procedures by distinguishing mutations on the basis of the presence/absence of a transgene, and by recording the technology used for their generation. Its interface can be customized to match the context and rules of any laboratory. In addition, SeedUSoon includes functions to help the laboratory protect intellectual property, export data, and facilitate seed exchange between laboratories. The SeedUSoon program, which is customizable to match individual practices and preferences, provides a powerful toolkit to plant laboratories searching for innovative approaches in laboratory management.

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“…This concept of ‘stock’ can be generalized to represent strain, line, biological entities or individual, therefore it could represent patients. The Natural Diversity Module [27] allows representation of experiment data related to a stock, therefore this module allows to represent clinical information as experiments. However, this approach could make it harder the process of clinical data integration from different sources and would preclude the generation and the use of the Clinical Databases bridge layers.…”

Section: Methodsmentioning

confidence: 99%

Computational framework to support integration of biomolecular and clinical data within a translational approach

et al. 2013

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BackgroundThe use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information.ResultsWe have implemented an extension of Chado – the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications.ConclusionsOpen-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different “omics” technologies with patient’s clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be ...

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The Chado Natural Diversity module: a new generic database schema for large-scale phenotyping and genotyping data

Cited by 31 publications

References 32 publications

Advances in tomato research in the post-genome era

Advances in tomato research in the post-genome era

SeedUSoon: A New Software Program to Improve Seed Stock Management and Plant Line Exchanges between Research Laboratories

Computational framework to support integration of biomolecular and clinical data within a translational approach

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