106 research outputs found
The Plant Ontology: A common reference ontology for plants
The Plant Ontology (PO) (http://www.plantontology.org) (Jaiswal et al., 2005; Avraham et al., 2008) was designed to facilitate cross-database querying and to foster consistent
use of plant-specific terminology in annotation. As new data are generated from the ever-expanding list of plant genome projects, the need for a consistent, cross-taxon vocabulary has grown. To meet this need, the PO is being expanded to represent all plants. This is the first ontology designed to encompass anatomical structures as well as growth and developmental stages across such a broad taxonomic range. While other ontologies such as the Gene Ontology (GO) (The Gene Ontology Consortium, 2010) or Cell Type Ontology (CL) (Bard et al., 2005) cover all living organisms,
they are confined to structures at the cellular level and below. The diversity of growth forms and life histories within plants presents a challenge, but also provides unique opportunities to study developmental and evolutionary homology across organisms
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Genes affecting novel seed constituents in Limnanthes alba Benth: transcriptome analysis of developing embryos and a new genetic map of meadowfoam
The seed oil of meadowfoam, a new crop in the Limnanthaceae family, is highly enriched in very long chain fatty acids that are desaturated at the Δ5 position. The unusual oil is desirable for cosmetics and innovative industrial applications and the seed meal remaining after oil extraction contains glucolimnanthin, a methoxylated benzylglucosinolate whose degradation products are herbicidal and anti-microbial. Here we describe EST analysis of the developing seed transcriptome that identified major genes involved in biosynthesis and assembly of the seed oil and in glucosinolate metabolic pathways. mRNAs encoding acyl-CoA Δ5 desaturase were notably abundant. The library was searched for simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). Fifty-four new SSR markers and eight candidate gene markers were developed and combined with previously developed SSRs to construct a new genetic map for Limnanthes alba. Mapped genes in the lipid biosynthetic pathway encode 3-ketoacyl-CoA synthase (KCS), Δ5 desaturase (Δ5DS), lysophosphatidylacyl-acyl transferase (LPAT), and acyl-CoA diacylglycerol acyl transferase (DGAT). Mapped genes in glucosinolate biosynthetic and degradation pathways encode CYP79A, myrosinase (TGG), and epithiospecifier modifier protein (ESM). The resources developed in this study will further the domestication and improvement of meadowfoam as an oilseed crop.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by PeerJ. The published article can be found at: https://peerj.com/.Keywords: Desaturase, KCS, Meadowfoam, Glucolimnanthin, Limnanthes, LPA
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SlabaughMaryCropSoilSciGenesAffectingNovelSupp3TableS2.pdf
The seed oil of meadowfoam, a new crop in the Limnanthaceae family, is highly enriched in very long chain fatty acids that are desaturated at the Δ5 position. The unusual oil is desirable for cosmetics and innovative industrial applications and the seed meal remaining after oil extraction contains glucolimnanthin, a methoxylated benzylglucosinolate whose degradation products are herbicidal and anti-microbial. Here we describe EST analysis of the developing seed transcriptome that identified major genes involved in biosynthesis and assembly of the seed oil and in glucosinolate metabolic pathways. mRNAs encoding acyl-CoA Δ5 desaturase were notably abundant. The library was searched for simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). Fifty-four new SSR markers and eight candidate gene markers were developed and combined with previously developed SSRs to construct a new genetic map for Limnanthes alba. Mapped genes in the lipid biosynthetic pathway encode 3-ketoacyl-CoA synthase (KCS), Δ5 desaturase (Δ5DS), lysophosphatidylacyl-acyl transferase (LPAT), and acyl-CoA diacylglycerol acyl transferase (DGAT). Mapped genes in glucosinolate biosynthetic and degradation pathways encode CYP79A, myrosinase (TGG), and epithiospecifier modifier protein (ESM). The resources developed in this study will further the domestication and improvement of meadowfoam as an oilseed crop.Keywords: LPAT, Desaturase, KCS, Limnanthes, Meadowfoam, GlucolimnanthinKeywords: LPAT, Desaturase, KCS, Limnanthes, Meadowfoam, Glucolimnanthi
Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the AgBioData Consortium
Over the last several decades, there has been rapid growth in the number and
scope of agricultural genetics, genomics and breeding (GGB) databases and
resources. The AgBioData Consortium (https://www.agbiodata.org/) currently
represents 44 databases and resources covering model or crop plant and animal
GGB data, ontologies, pathways, genetic variation and breeding platforms
(referred to as 'databases' throughout). One of the goals of the Consortium is
to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data
management and the integration of datasets which requires data sharing, along
with structured vocabularies and/or ontologies. Two AgBioData working groups,
focused on Data Sharing and Ontologies, conducted a survey to assess the status
and future needs of the members in those areas. A total of 33 researchers
responded to the survey, representing 37 databases. Results suggest that data
sharing practices by AgBioData databases are in a healthy state, but it is not
clear whether this is true for all metadata and data types across all
databases; and that ontology use has not substantially changed since a similar
survey was conducted in 2017. We recommend 1) providing training for database
personnel in specific data sharing techniques, as well as in ontology use; 2)
further study on what metadata is shared, and how well it is shared among
databases; 3) promoting an understanding of data sharing and ontologies in the
stakeholder community; 4) improving data sharing and ontologies for specific
phenotypic data types and formats; and 5) lowering specific barriers to data
sharing and ontology use, by identifying sustainability solutions, and the
identification, promotion, or development of data standards. Combined, these
improvements are likely to help AgBioData databases increase development
efforts towards improved ontology use, and data sharing via programmatic means.Comment: 17 pages, 8 figure
Finding Our Way through Phenotypes
Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility
Hydrogenation of Organic Matter as a Terminal Electron Sink Sustains High CO2:CH4 Production Ratios During Anaerobic Decomposition
Once inorganic electron acceptors are depleted, organic matter in anoxic environments decomposes by hydrolysis, fermentation, and methanogenesis, requiring syntrophic interactions between microorganisms to achieve energetic favorability. In this classic anaerobic food chain, methanogenesis represents the terminal electron accepting (TEA) process, ultimately producing equimolar CO2 and CH4 for each molecule of organic matter degraded. However, CO2:CH4 production in Sphagnum-derived, mineral-poor, cellulosic peat often substantially exceeds this 1:1 ratio, even in the absence of measureable inorganic TEAs. Since the oxidation state of C in both cellulose-derived organic matter and acetate is 0, and CO2 has an oxidation state of +4, if CH4 (oxidation state -4) is not produced in equal ratio, then some other compound(s) must balance CO2 production by receiving 4 electrons. Here we present evidence for ubiquitous hydrogenation of diverse unsaturated compounds that appear to serve as organic TEAs in peat, thereby providing the necessary electron balance to sustain CO2:CH4 \u3e1. While organic electron acceptors have previously been proposed to drive microbial respiration of organic matter through the reversible reduction of quinone moieties, the hydrogenation mechanism that we propose, by contrast, reduces C-C double bonds in organic matter thereby serving as 1) a terminal electron sink, 2) a mechanism for degrading complex unsaturated organic molecules, 3) a potential mechanism to regenerate electron-accepting quinones, and, in some cases, 4) a means to alleviate the toxicity of unsaturated aromatic acids. This mechanism for CO2 generation without concomitant CH4 production has the potential to regulate the global warming potential of peatlands by elevating CO2:CH4 production ratios
Finding Our Way through Phenotypes
Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility
Overview of the interactive task in BioCreative V
Fully automated text mining (TM) systems promote efficient literature searching, retrieval, and review but are not sufficient to produce ready-to-consume curated documents. These systems are not meant to replace biocurators, but instead to assist them in one or more literature curation steps. To do so, the user interface is an important aspect that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a track designed for exploring user-system interactions, promoting development of useful TM tools, and providing a communication channel between the biocuration and the TM communities. In BioCreative V, the IAT track followed a format similar to previous interactive tracks, where the utility and usability of TM tools, as well as the generation of use cases, have been the focal points. The proposed curation tasks are user-centric and formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators participated. Two levels of user participation were offered to broaden curator involvement and obtain more feedback on usability aspects. The full level participation involved training on the system, curation of a set of documents with and without TM assistance, tracking of time-on-task, and completion of a user survey. The partial level participation was designed to focus on usability aspects of the interface and not the performance per se. In this case, biocurators navigated the system by performing pre-designed tasks and then were asked whether they were able to achieve the task and the level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested
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Emerging semantics to link phenotype and environment
Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by PeerJ. The published article can be found at: https://peerj.com
The Gene Ontology knowledgebase in 2023
The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project
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