Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice

Shrestha, Rosemary; Matteis, Luca; Skofic, Milko; Portugal, Arllet; McLaren, Graham; Hyman, Glenn; Arnaud, Elizabeth

doi:10.3389/fphys.2012.00326

Cited by 108 publications

(109 citation statements)

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“…This is reflected in MIAPPE in the section “Observed Variables”. Following the approach of the Crop Ontology platform [32, 33], we propose to describe the observed variables by three basic attributes: trait name, method, and scale. In this section, in addition to phenotypic variables (any plant characteristics that are measured in a phenotyping experiment), we also consider environmental variables, i.e.…”

Section: Resultsmentioning

confidence: 99%

Measures for interoperability of phenotypic data: minimum information requirements and formatting

et al. 2016

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BackgroundPlant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse.ResultsIn this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called “Minimum Information About a Plant Phenotyping Experiment”, which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented.ConclusionsAcceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-016-0144-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Measures for interoperability of phenotypic data: minimum information requirements and formatting

et al. 2016

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“…For example, the Plant Ontology (PO) community is continuously improving its structured term lists to include anatomical entities that reflect the organizing principle of the plant body, thereby enabling interspecific comparisons of gene expression, phenotypes and gene functions [52]. At the other end of the research spectrum, ecologists, agronomists and breeders are also codifying trait descriptions with the implementation of dedicated ontologies to integrate field observations and measurements across experimental sites and for different species, including crops [53,54]. As our understanding of the functional modules that govern plant growth and development improves, information formatted through studies such as this one, focusing on mutant phenotypes in one plant species, could eventually assist trait development efforts in another.…”

Section: Discussionmentioning

confidence: 99%

The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining

Szakonyi

Landeghem

Baerenfaller

et al. 2015

Current Plant Biology

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Please cite this article in press as: D. Szakonyi, et al., The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining, Curr. Plant Biol. (2015), http://dx. a b s t r a c tThe information that connects genotypes and phenotypes is essentially embedded in research articles written in natural language. To facilitate access to this knowledge, we constructed a framework for the curation of the scientific literature studying the molecular mechanisms that control leaf growth and development in Arabidopsis thaliana (Arabidopsis). Standard structured statements, called relations, were designed to capture diverse data types, including phenotypes and gene expression linked to genotype description, growth conditions, genetic and molecular interactions, and details about molecular entities. Relations were then annotated from the literature, defining the relevant terms according to standard biomedical ontologies. This curation process was supported by a dedicated graphical user interface, called Leaf Knowtator. A total of 283 primary research articles were curated by a community of annotators, yielding 9947 relations monitored for consistency and over 12,500 references to Arabidopsis genes. This information was converted into a relational database (KnownLeaf) and merged with other public Arabidopsis resources relative to transcriptional networks, protein-protein interaction, gene co-expression, and additional molecular annotations. Within KnownLeaf, leaf phenotype data can be searched together with molecular data originating either from this curation initiative or from external public resources. Finally, we built a network (LeafNet) with a portion of the KnownLeaf database content to graphically represent the leaf phenotype relations in a molecular context, offering an intuitive starting point for knowledge mining. Literature curation efforts such as ours provide high quality structured information accessible to computational analysis, and thereby to a wide range of applications.

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“…In particular, the Solanaceae Phenotype Ontology (SPTO) [17, 18], Plant Ontology (PO) [19, 20], Phenotypic Quality Ontology (PATO) [21, 22] and Trait Ontology (TO) [23] were used to identify plant-specific phenotypic information whereas Gene Ontology (GO) [24] and Sequence Ontology (SO) [25] were used to identify genotypic information. Further, small chemical compounds were annotated using the Chemical Entities of Biological Interest database/ontology (ChEBI) [26, 27].…”

Section: Methodsmentioning

confidence: 99%

QTLTableMiner++: semantic mining of QTL tables in scientific articles

et al. 2018

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BackgroundA quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature.QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file.ResultsThe performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall.ConclusionQTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2165-7) contains supplementary material, which is available to authorized users.

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Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice

Cited by 108 publications

References 7 publications

Measures for interoperability of phenotypic data: minimum information requirements and formatting

Measures for interoperability of phenotypic data: minimum information requirements and formatting

The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining

QTLTableMiner++: semantic mining of QTL tables in scientific articles

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