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Two notable trends currently impacting biology curation are 1) the use of wikis to input, store, and disseminate research data and 2) the development of semantic technologies to facilitate higher-order data description and exploration. These separate developments, when brought together, have the potential to deliver on one promise of the "semantic web": structured, self-described data used to further scientific research and analysis. The Semantic MediaWiki  extension, when used indoi:10.1145/2166896.2166921 dblp:conf/swat4ls/PreeceEJ11 fatcat:wh27lbknsjfn3c4mx6wseg7gle
more »... with Semantic Forms , provides an avenue to create a semantically-driven, communitypowered research platform on the web.
Plant stress traits are important breeding targets for all crop species. Massive amounts of research dollars are spent generating data to combat plant diseases and environmental stress. Often this data is used to achieve a single goal, and then left in a repository to never be used again. As a scientific community, we should be striving to make all publicly funded data reusable, and interoperable. This goal is achievable only through careful annotation using universal data and metadatadblp:conf/icbo/MeierLECPJP18 fatcat:7mfulpxtkrc7fhznlwwu3hevoe
more »... . One such standard is the use of a standardized vocabulary, or ontology. This paper presents a semi-automated method to define and label plant stresses using a combination of web scraping and ontology design patterns. Standardizing the definitions and linking plant stress with established hierarchies leverages previous work of developed knowledge bases such as taxonomic classifications and other ontologies.
doi:10.3732/ajb.1200222 pmid:22847540 pmcid:PMC3492881 fatcat:2gbocanruvbd3f25dyzjqch3wm
The Planteome project is a centralized online plant informatics portal which provides semantic integration of widely diverse datasets with the goal of plant improvement. Traditional plant breeding methods for crop improvement may be combined with next-generation analysis methods and automated scoring of traits and phenotypes to develop improved varieties. The Planteome project (www.planteome.org) develops and hosts a suite of reference ontologies for plants associated with a growing corpus ofdblp:conf/icbo/CooperMEPXKQZTJ16 fatcat:nurcyvvylbaalod6qjjjtglf7m
more »... nomics data. Data annotations linking phenotypes and germplasm to genomics resources are achieved by data transformation and mapping species-specific controlled vocabularies to the reference ontologies. Analysis and annotation tools are being developed to facilitate studies of plant traits, phenotypes, diseases, gene function and expression and genetic diversity data across a wide range of plant species. The project database and the online resources provide researchers tools to search and browse and access remotely via APIs for semantic integration in annotation tools and data repositories providing resources for plant biology, breeding, genomics and genetics.
Large quantities of digital images are now generated for biological collections, including those developed in projects premised on the high-throughput screening of genome-phenome experiments. These images often carry annotations on taxonomy and observable features, such as anatomical structures and phenotype variations often recorded in response to the environmental factors under which the organisms were sampled. At present, most of these annotations are described in free text, may involvedoi:10.1186/2041-1480-5-50 pmid:25584184 pmcid:PMC4290088 fatcat:77xwkdwl7ragxbvkuj2xywyfja
more »... ed use of non-standard vocabularies, and rarely specify precise coordinates of features on the image plane such that a computer vision algorithm could identify, extract and annotate them. Therefore, researchers and curators need a tool that can identify and demarcate features in an image plane and allow their annotation with semantically contextual ontology terms. Such a tool would generate data useful for inter and intra-specific comparison and encourage the integration of curation standards. In the future, quality annotated image segments may provide training data sets for developing machine learning applications for automated image annotation. Results: We developed a novel image segmentation and annotation software application, "Annotation of Image Segments with Ontologies" (AISO). The tool enables researchers and curators to delineate portions of an image into multiple highlighted segments and annotate them with an ontology-based controlled vocabulary. AISO is a freely available Java-based desktop application and runs on multiple platforms. It can be downloaded at http://www. plantontology.org/software/AISO. Conclusions: AISO enables curators and researchers to annotate digital images with ontology terms in a manner which ensures the future computational value of the annotated images. We foresee uses for such data-encoded image annotations in biological data mining, machine learning, predictive annotation, semantic inference, and comparative analyses.
Next-generation sequencing and 'omics' platforms are used extensively in plant biology research to unravel new genomes and study their interactions with abiotic and biotic agents in the growth environment. Despite the availability of a large and growing number of genomic data sets, there are only limited resources providing highly-curated and up-to-date metabolic and regulatory networks for plant pathways.doi:10.1186/1939-8433-6-14 pmid:24280312 pmcid:PMC4883732 fatcat:kei23v3oc5bvfpkw2orkzua4im
High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a cellphone or digital camera captures a video of the surface of the ear. Videos are thendoi:10.1101/2020.07.12.199000 fatcat:fnqms2pvqbfytjv4xjhxpq54by
more »... ally flattened into two-dimensional ear projections. Segregating GFP and anthocyanin kernel phenotype are clearly distinguishable in ear projections, and can be manually annotated using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390,000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme (VPE). We show that by using this system, the quantification of transmission data and other ear phenotypes can be accelerated and scaled to generate large datasets for robust analyses.
Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic,doi:10.1093/nar/gkw932 pmid:27799469 pmcid:PMC5210633 fatcat:uxqy23gd4newjholwhkbbtmoju
more »... and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX.
., 2007; Preece, Becerra, Robinson, & Dandy, 2018) . ... ., 1985; Preece et al., 2017; Vorst & Bermond, 2001; . ...doi:10.1016/j.paid.2018.05.011 fatcat:5wobbtvutfbohnmuha6biumvme
Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabularydoi:10.1093/nar/gkaa979 pmid:33170273 fatcat:atundz637bfalivwtye3vb3twe
more »... tion (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes—over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene–gene interactions. Gramene integrates ontology-based protein structure–function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.
Brachypodium sylvaticum (Huds.) P. Beauv. (slender falsebrome; Poaceae), with an estimated genome size of 470 Mb and 17 chromosomes ( Foote et al., 2004 ) , is a perennial bunchgrass native to Europe, Asia, and North Africa and is closely related to the bioenergy feedstock model grass B. distachyon (L.) P. Beauv. ( Wolny et al., 2011 ) , which has a sequenced genome of 272 Mb and fi ve chromosomes. In its native range, B. sylvaticum occurs in habitats ranging from forest understory to opendoi:10.3732/apps.1200011 pmid:25202520 pmcid:PMC4105277 fatcat:iiirvqvddngpdelpohaxfxpvnm
more »... ws and tolerates conditions from full shade to full sun ( Holten, 1980 ; Long, 1989 ; Aarrestad, 2000 ; Kirby and Thomas, 2000 ) . In the United States, B. sylvaticum is invasive and listed as a noxious weed covering the
We acknowledge technical support provided by Guanming Wu and Justin Elser. We thank all 2017 and 2018 jamboree participants. ...doi:10.1093/database/bay146 pmid:30649295 pmcid:PMC6334007 fatcat:k76ydfk3nnf5fjcsuub47tsnr4
The Planteome project (http://www.planteome.org) provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemicaldoi:10.1093/nar/gkx1152 pmid:29186578 pmcid:PMC5753347 fatcat:7hbnerl4uvf47c6ikf2m3sm6se
more »... ntities of Biological Interest, Phenotype and Attribute Ontology, and others. The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. We provide integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site (https://github.com/Planteome) for sharing, tracking revisions and new requests. The annotated data are freely accessible from the ontology browser (http: //browser.planteome.org/amigo) and our data repository.
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