Trait Ontology Research Articles

Mapping bread wheat trait ontologies for semantic interoperability

Background The Wheat Crop ontology was created to annotate phenotypic experimental data (i.e. field and greenhouse measurements standardized and integrated in databases). The Wheat Trait and Phenotype ontology was created to annotate information on wheat traits from the literature (i.e. text found in the abstract, results and discussion of scholarly articles). To enable seamless data retrieval on wheat traits from these complementary sources, the classes in the two ontologies have been aligned. Methods All pairs of ontology classes were examined and categorized in nine groups based on the nature of their relationships (e.g. equivalence, subsumption). General principles emerged from this process which were formalized into rules. The Simple Standard for Sharing Ontological Mappings (SSSOM) representation was chosen to represent the mappings in RDF (Resource Description Framework), including their metadata such as creators, reviewers, and justification (including rules). Results The mapping dataset is publicly available. It covers 77% of the ontology classes. Most labels of the aligned classes differed significantly and required domain expertise for decisions, especially for traits related to biotic stress. Consequently, most mappings are close mappings rather than exact equivalents. Conclusions We present the end-to-end manual process used to select and represent mappings in SSSOM within the specific domain of wheat traits. We derive general lessons from the complex alignment process that extend beyond the specific case of these two ontologies and more generally apply to alignments of specialized ontologies for information retrieval purposes. This work demonstrates the relevance of SSSOM for representing these mappings.

Ontologies related to livestock for the Global Burden of Animal Diseases programme: a review.

The Global Burden of Animal Diseases (GBADs) programme aims to assess the impact of animal health on agricultural animals, livestock production systems and associated communities worldwide. As part of the objectives of GBADs'Animal Health Ontology theme, the programme reviewed conceptual frameworks, ontologies and classification systems in biomedical science. The focus was on data requirements in animal health and the connections between animal health and human and environmental health. In May 2023, the team conducted searches of recognised repositories of biomedical ontologies, including BioPortal, Open Biological and Biomedical Ontology Foundry, and Ontology Lookup Service, to identify animal and livestock ontologies and those containing relevant concepts. Sixteen ontologies were found, covering topics such as surveillance, anatomy and genetics. Notable examples include the Animal Trait Ontology for Livestock, the Animal Health Surveillance Ontology, the National Center for Biotechnology Information Taxonomy and the Uberon Multi-Species Anatomy Ontology. However, some ontologies lacked class definitions for a significant portion of their classes. The review highlights the need for domain evidence to support proposed models, critical appraisal of external ontologies before reuse, and external expert reviews along with statistical tests of agreements. The findings from this review informed the structural framework, concepts and rationales of the animal health ontology for GBADs. This animal health ontology aims to increase the interoperability and transparency of GBADs data, thereby enabling estimates of the impacts of animal diseases on agriculture, livestock production systems and associated communities globally.

A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Discovering New QTNs and Candidate Genes Associated with Rice-Grain-Related Traits within a Collection of Northeast Core Set and Rice Landraces.

Grain-related traits are pivotal in rice cultivation, influencing yield and consumer preference. The complex inheritance of these traits, involving multiple alleles contributing to their expression, poses challenges in breeding. To address these challenges, a multi-locus genome-wide association study (ML-GWAS) utilizing 35,286 high-quality single-nucleotide polymorphisms (SNPs) was conducted. Our study utilized an association panel comprising 483 rice genotypes sourced from a northeast core set and a landraces set collected from various regions in India. Forty quantitative trait nucleotides (QTNs) were identified, associated with four grain-related traits: grain length (GL), grain width (GW), grain aroma (Aro), and length-width ratio (LWR). Notably, 16 QTNs were simultaneously identified using two ML-GWAS methods, distributed across multiple chromosomes. Nearly 258 genes were found near the 16 significant QTNs. Gene annotation study revealed that sixty of these genes exhibited elevated expression levels in specific tissues and were implicated in pathways influencing grain quality. Gene ontology (GO), trait ontology (TO), and enrichment analysis pinpointed 60 candidate genes (CGs) enriched in relevant GO terms. Among them, LOC_Os05g06470, LOC_Os06g06080, LOC_Os08g43470, and LOC_Os03g53110 were confirmed as key contributors to GL, GW, Aro, and LWR. Insights from QTNs and CGs illuminate rice trait regulation and genetic connections, offering potential targets for future studies.

Advancing utilization of diverse global carrot (Daucus carota L.) germplasm with flowering habit trait ontology.

Biennial vegetable crops are challenging to breed due to long breeding cycle times. At the same time, it is important to preserve a strong biennial growth habit, avoiding premature flowering that renders the crop unmarketable. Gene banks carry important genetic variation which may be essential to improve crop resilience, but these collections are underutilized due to lack of characterization for key traits like bolting tendency for biennial vegetable crops. Due to concerns about introducing undesirable traits such as premature flowering into elite germplasm, many accessions may not be considered for other key traits that benefit growers, leaving crops more vulnerable to pests, diseases, and abiotic stresses. In this study, we develop a method for characterizing flowering to identify accessions that are predominantly biennial, which could be incorporated into biennial breeding programs without substantially increasing the risk of annual growth habits. This should increase the use of these accessions if they are also sources of other important traits such as disease resistance. We developed the CarrotOmics flowering habit trait ontology and evaluated flowering habit in the largest (N=695), and most diverse collection of cultivated carrots studied to date. Over 80% of accessions were collected from the Eurasian supercontinent, which includes the primary and secondary centers of carrot diversity. We successfully identified untapped genetic diversity in biennial carrot germplasm (n=197 with 0% plants flowering) and predominantly-biennial germplasm (n=357 with <15% plants flowering). High broad-sense heritability for flowering habit (0.81 < H2< 0.93) indicates a strong genetic component of this trait, suggesting that these carrot accessions should be consistently biennial. Breeders can select biennial plants and eliminate annual plants from a predominantly biennial population. The establishment of the predominantly biennial subcategory nearly doubles the availability of germplasm with commercial potential and accounts for 54% of the germplasm collection we evaluated. This subcollection is a useful source of genetic diversity for breeders. This method could also be applied to other biennial vegetable genetic resources and to introduce higher levels of genetic diversity into commercial cultivars, to reduce crop genetic vulnerability. We encourage breeders and researchers of biennial crops to optimize this strategy for their particular crop.

Ukrainian Plant Trait Database: UkrTrait v. 1.0.

Considering the growing demand for plant trait data and taking into account the lack of trait data from Eastern Europe, especially from its steppic region, we launched a new Ukrainian Plant Trait Database (UkrTrait v. 1.0) aiming at collecting all the available plant trait data from Ukraine.To facilitate further use of this database, we linked the trait terminology to the TRY Plant Trait Database, Thesaurus of Plant Characteristics (TOP) and Plant Trait Ontology (TO). For taxa names, we provide the crosswalks between the Ukrainian checklist and international sources, i.e. GBIF Backbone Taxonomy, World Checklist of Vascular Plants (World Checklist of Vascular Plants (World Checklist of Vascular Plants (WCVP), World Flora Online (WFO) and Euro+Med PlantBase. We aim to integrate our data into the relevant global (TRY Plant Trait Database) and pan-European (FloraVeg.EU) databases. The current version of the database is freely available at the Zenodo repository and will be updated in the future. Until now, plant traits for the Ukrainian flora were scattered across literature, often focusing on single species and written mainly in Ukrainian. Additionally, many traits were in grey literature or remained non-digitised, which rendered them inaccessible to the global scientific community. Addressing this gap, our Ukrainian Plant Trait Database (UkrTrait v. 1.0) represents a significant step forward. We compiled and digitised plant traits from local Ukrainian literature sources. Furthermore, we performed our own field and laboratory measurements of various plant traits that were not previously available in literature. In the current version of the UkrTrait, we focus on vascular plant species that are absent from the other European trait databases, with emphasis on species that are representative for the steppe vegetation. Traits assembled from literature include life span (annuals, biennials, perennials), plant height, flowering period (flowering months), life form (by Raunkiaer), plant growth form and others. Our own measured traits include seed mass, seed shape, leaf area, leaf nitrogen concentration and leaf phosphorus concentration. The current version, i.e. UkrTrait v. 1.0, comprises digitised literature data of 287,948 records of 75 traits for 6,198 taxa and our own trait measurements of 2,390 records of 12 traits for 388 taxa.

TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature.

Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature.

Planteome 2024 Update: Reference Ontologies and Knowledgebase for Plant Biology.

The Planteome project (https://planteome.org/) provides a suite of reference and crop-specific ontologies and an integrated knowledgebase of plant genomics data. The plant genomics data in the Planteome has been obtained through manual and automated curation and sourced from more than 40 partner databases and resources. Here, we report on updates to the Planteome reference ontologies, namely, the Plant Ontology (PO), Trait Ontology (TO), the Plant Experimental Conditions Ontology (PECO), and integration of species/crop-specific vocabularies from our partners, the Crop Ontology (CO) into the TO ontology graph. Currently, 11 CO vocabularies are integrated into the Planteome with the addition of yam, sorghum, and potato since 2018. In addition, the size of the annotation database has increased by 34%, and the number of bioentities (genes, proteins, etc.) from 125 plant taxa has increased by 72%. We developed new tools to facilitate user requests and improvements to the CO vocabularies, and to allow fast searching and browsing of PO terms and definitions. These enhancements and future changes to automate the TO-CO mappings and knowledge discovery tools ensure that the Planteome will continue to be a valuable resource for plant biology.

OPIA: an open archive of plant images and related phenotypic traits.

High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566225 images with 2417186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.

Genome-Wide Single-Nucleotide Polymorphism-Based Genomic Diversity and Runs of Homozygosity for Selection Signatures in Equine Breeds.

The horse, one of the most domesticated animals, has been used for several purposes, like transportation, hunting, in sport, or for agriculture-related works. Kathiawari, Marwari, Manipuri, Zanskari, Bhutia, Spiti, and Thoroughbred are the main breeds of horses, particularly due to their agroclimatic adaptation and role in any kind of strong physical activity, and these characteristics are majorly governed by genetic factors. The genetic diversity and phylogenetic relationship of these Indian equine breeds using microsatellite markers have been reported, but further studies exploring the SNP diversity and runs of homozygosity revealing the selection signature of breeds are still warranted. In our study, the identification of genes that play a vital role in muscle development is performed through SNP detection via the whole-genome sequencing approach. A total of 96 samples, categorized under seven breeds, and 620,721 SNPs were considered to ascertain the ROH patterns amongst all the seven breeds. Over 5444 ROH islands were mined, and the maximum number of ROHs was found to be present in Zanskari, while Thoroughbred was confined to the lowest number of ROHs. Gene enrichment of these ROH islands produced 6757 functional genes, with AGPAT1, CLEC4, and CFAP20 as important gene families. However, QTL annotation revealed that the maximum QTLs were associated with Wither's height trait ontology that falls under the growth trait in all seven breeds. An Equine SNP marker database (EqSNPDb) was developed to catalogue ROHs for all these equine breeds for the flexible and easy chromosome-wise retrieval of ROH along with the genotype details of all the SNPs. Such a study can reveal breed divergence in different climatic and ecological conditions.

Connecting data for consumer preferences, food quality, and breeding in support of market-oriented breeding of root, tuber, and banana crops.

The 5-year project 'Breeding roots, tubers and banana products for end user preferences' (RTBfoods) focused on collecting consumers' preferences on 12 food products to guide breeding programmes. It involved multidisciplinary teams from Africa, Latin America, and Europe. Diverse data types were generated on preferred qualities of users (farmers, family and entrepreneurial processors, traders or retailers, and consumers). Country-based target product profiles were produced with a comprehensive market analysis, disaggregating gender's role and preferences, providing prioritised lists of traits for the development of new plant varieties. We describe the approach taken to create, in the roots, tubers, and banana breeding databases, a centralised and meaningful open access to sensory information on food products and genotypes. Biochemical, instrumental textural, and sensory analysis data are then directly connected to the specific plant record while user survey data, bearing personal information, were analysed, anonymised, and uploaded in a repository. Names and descriptions of food quality traits were added into the Crop Ontology for labelling data in the databases, along with the various methods of measurement used by the project. The development and application of standard operating procedures, data templates, and adapted trait ontologies improved the data quality and its format, enabling the linking of these to the plant material studied when uploaded in the breeding databases or in repositories. Some modifications to the database model were necessary to accommodate the food sensory traits and sensory panel trials. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Sainfoin (Onobrychis spp.) crop ontology: supporting germplasm characterization and international research collaborations.

Sainfoin (Onobrychis spp.) is a perennial forage legume that is also attracting attention as a perennial pulse with potential for human consumption. The dual use of sainfoin underpins diverse research and breeding programs focused on improving sainfoin lines for forage and pulses, which is driving the generation of complex datasets describing high dimensional phenotypes in the post-omics era. To ensure that multiple user groups, for example, breeders selecting for forage and those selecting for edible seed, can utilize these rich datasets, it is necessary to develop common ontologies and accessible ontology platforms. One such platform, Crop Ontology, was created in 2008 by the Consortium of International Agricultural Research Centers (CGIAR) to host crop-specific trait ontologies that support standardized plant breeding databases. In the present study, we describe the sainfoin crop ontology (CO). An in-depth literature review was performed to develop a comprehensive list of traits measured and reported in sainfoin. Because the same traits can be measured in different ways, ultimately, a set of 98 variables (variable = plant trait + method of measurement + scale of measurement) used to describe variation in sainfoin were identified. Variables were formatted and standardized based on guidelines provided here for inclusion in the sainfoin CO. The 98 variables contained a total of 82 traits from four trait classes of which 24 were agronomic, 31 were morphological, 19 were seed and forage quality related, and 8 were phenological. In addition to the developed variables, we have provided a roadmap for developing and submission of new traits to the sainfoin CO.

A combinatorial approach implementing new database structures to facilitate practical data curation management of QTL, association, correlation and heritability data on trait variants

A precise description of traits is essential in genetics and genomics studies to facilitate comparative genetics and meta-analyses. It is an ongoing challenge in research and production environments to unambiguously and consistently compare traits of interest from data collected under various conditions. Despite previous efforts to standardize trait nomenclature, it remains a challenge to fully and accurately capture trait nomenclature granularity in a way that ensures long-term data sustainability in terms of the data curation processes, data management logistics and the ability to make meaningful comparisons across studies. In the Animal Quantitative Trait Loci Database and the Animal Trait Correlation Database, we have recently introduced a new method to extend livestock trait ontologies by using trait modifiers and qualifiers to define traits that differ slightly in how they are measured, examined or combined with other traits or factors. Here, we describe the implementation of a system in which the extended trait data, with modifiers, are managed at the experiment level as 'trait variants'. This has helped us to streamline the management and curation of such trait information in our database environment. Database URL https://www.animalgenome.org/PGNET/.

GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals.

GWAS Atlas (https://ngdc.cncb.ac.cn/gwas/) is a manually curated resource of genome-wide genotype-to-phenotype associations for a wide range of species. Here, we present an updated implementation of GWAS Atlas by curating and incorporating more high-quality associations, with significant improvements and advances over the previous version. Specifically, the current release of GWAS Atlas incorporates a total of 278,109 curated genotype-to-phenotype associations for 1,444 different traits across 15 species (10 plants and 5 animals) from 830 publications and 3,432 studies. A collection of 6,084 lead SNPs of 439 traits and 486 experiment-validated causal variants of 157 traits are newly added. Moreover, 1,056 trait ontology terms are newly defined, resulting in 1,172 and 431 terms for Plant Phenotype and Trait Ontology and Animal Phenotype and Trait Ontology, respectively. Additionally, it is equipped with four online analysis tools and a submission platform, allowing users to perform data analysis and data submission. Collectively, as a core resource in the National Genomics Data Center, GWAS Atlas provides valuable genotype-to-phenotype associations for a diversity of species and thus plays an important role in agronomic trait study and molecular breeding.

A Comparison Analysis for Protein-Protein Interaction Network-Based Methods in Prioritizing Arabidopsis Functional Genes

Background: Connecting genes to phenotypes is still a great challenge in genetics. Research related to gene-phenotype associations has made remarkable progress recently due to high-throughput sequencing technology and genome-wide association study (GWAS). However, these genes, which are considered to be significantly associated with a target phenotype according to traditional GWAS, are less precise or subject to greater confounding. Objective: The present study is an attempt to prioritize functional genes for complex phenotypes employing protein-protein interaction (PPI) network-based systems genetics methods on available GWAS results. Methods: In this paper, we calculated the functional gene enrichment ratios of the trait ontology of A. thaliana for three common systems genetics methods (i.e. GeneRank, K-shell and HotNet2). Then, comparison of gene enrichment ratios obtained by PPI network-based methods was performed. Finally, a hybrid model was proposed, integrating GeneRank, comprehensive score algorithm and HotNet diffusion- oriented subnetworks (HotNet2) to prioritize functional genes. Results: These PPI network-based systems genetics methods were indeed useful for prioritizing 775henoltype-associated genes. And functional gene enrichment ratios calculated from the top 20% of GeneRank-identified genes were higher than these ratios of K-shell and these ratios of HotNet2 for most phenotypes. However, the hybrid model can improve the efficiency of functional gene enrichment for A. thaliana (up to 40%). Conclusion: The present study provides a hybrid method integrating GeneRank, comprehensive score algorithm and HotNet2 to prioritize functional genes. The method will contribute to functional genomics in plants. The source data and codes are freely available at http://47.242.161.60/Plant/.

The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development

BackgroundOntologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease.ResultsHere we present the Xenopus phenotype ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated.ConclusionsThe XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype–phenotype data that can be directly related to other uPheno compliant resources.

KNOX II transcription factor HOS59 functions in regulating rice grain size.

Plant Knotted1-like homeobox (KNOX) genes encode homeodomain-containing transcription factors. In rice (Oryza sativa L.), little is known about the downstream target genes of KNOX Class II subfamily proteins. Here we generated chromatin immunoprecipitation (ChIP)-sequencing datasets for HOS59, a member of the rice KNOX Class II subfamily, and characterized the genome-wide binding sites of HOS59. We conducted trait ontology (TO) analysis of 9705 identified downstream target genes, and found that multiple TO terms are related to plant structure morphology and stress traits. ChIP-quantitative PCR (qPCR) was conducted to validate some key target genes. Meanwhile, our IP-MS datasets showed that HOS59 was closely associated with BELL family proteins, some grain size regulators (OsSPL13, OsSPL16, OsSPL18, SLG, etc.), and some epigenetic modification factors such as OsAGO4α and OsAGO4β, proteins involved in small interfering RNA-mediated gene silencing. Furthermore, we employed CRISPR/Cas9 editing and transgenic approaches to generate hos59 mutants and overexpression lines, respectively. Compared with wild-type plants, the hos59 mutants have longer grains and increased glume cell length, a loose plant architecture, and drooping leaves, while the overexpression lines showed smaller grain size, erect leaves, and lower plant height. The qRT-PCR results showed that mutation of the HOS59 gene led to upregulation of some grain size-related genes such as OsSPL13, OsSPL18, and PGL2. In summary, our results indicate that HOS59 may be a repressor of the downstream target genes, negatively regulating glume cell length, rice grain size, plant architecture, etc. The identified downstream target genes and possible interaction proteins of HOS59 improve our understanding of the KNOX regulatory networks.

PlantGSAD: a comprehensive gene set annotation database for plant species.

With the accumulation of massive data sets from high-throughput experiments and the rapid emergence of new types of omics data, gene sets have become more diverse and essential for the refinement of gene annotation at multidimensional levels. Accordingly, we collected and defined 236 007 gene sets across different categories for 44 plant species in the Plant Gene Set Annotation Database (PlantGSAD). These gene sets were divided into nine main categories covering many functional subcategories, such as trait ontology, co-expression modules, chromatin states, and liquid-liquid phase separation. The annotations from the collected gene sets covered all of the genes in the Brassicaceae species Arabidopsis and Poaceae species Oryza sativa. Several GSEA tools are implemented in PlantGSAD to improve the efficiency of the analysis, including custom SEA for a flexible strategy based on customized annotations, SEACOMPARE for the cross-comparison of SEA results, and integrated visualization features for ontological analysis that intuitively reflects their parent-child relationships. In summary, PlantGSAD provides numerous gene sets for multiple plant species and highly efficient analysis tools. We believe that PlantGSAD will become a multifunctional analysis platform that can be used to predict and elucidate the functions and mechanisms of genes of interest. PlantGSAD is publicly available at http://systemsbiology.cau.edu.cn/PlantGSEAv2/.

Semantic Search in Legacy Biodiversity Literature: Integrating data from different data infrastructures

Nowadays, obtaining information by entering queries into a web search engine is routine behaviour. With its search portal, the Specialised Information Service Biodiversity Research (BIOfid) adapts the exploration of legacy biodiversity literature and data extraction to current standards (Driller et al. 2020). In this presentation, we introduce the BIOfid search portal and its functionalities in a How-To short guide. To this end, we adapted a knowledge graph representation of our thematic focus of Central European, primarily German language, biodiversity literature of the 19th and 20th centuries. Now, users can search our text-mined corpus containing to date more than 8.700 full-text articles from 68 journals, and particularly focussing on birds, lepidopterans and vascular plants. The texts are automatically preprocessed by the Natural Language Processing provider TextImager (Hemati et al. 2016) and will be linked to various databases such as Wikidata, Wikipedia, the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL), Geonames, the Integrated Authority File (GND) and WordNet. For data retrieval, users can filter search results and download the article metadata as well as text annotations and database links in JavaScript Object Notation (JSON) format. For example, literature that mentions taxa from certain decades or co-occurrences of species can be searched. Our search engine recognises scientific and vernacular taxon names based on the GBIF Backbone Taxonomy and offers search suggestions to support the user. The semantic network of the BIOfid search portal is also enriched with data from the EoL trait bank, so that trait data can be included in the search queries. Thus, scientists can enhance their own data sets with the search results and feed them into the relevant biodiversity data repositories to sustainably expand the corresponding knowledge graphs with reliable data. Since BIOfid applies standard ontology terms, all data mobilized from literature can be combined with data on natural history collection objects or data from current research projects in order to generate more comprehensive knowledge. Furthermore, taxonomy, ecology and trait ontologies that have been built or extended within this project will be made available through appropriate platforms such as The Open Biological and Biomedical Ontology (OBO) Foundry and the Terminology Service of The German Federation for Biological Data (GFBio).

Integrated bioinformatic analysis identifies UBE2Q1 as a potential prognostic marker for high grade serous ovarian cancer

BackgroundHigh grade serous ovarian cancer (HGSOC) accounts for nearly 60% of total cases of epithelial ovarian cancer. It is the most aggressive subtype, which shows poor prognosis and low patient survival. For better management of HGSOC patients, new prognostic biomarkers are required to facilitate improved treatment strategies and ensure suitable healthcare decisions.MethodsWe performed genome wide expression analysis of HGSOC patient samples to identify differentially expressed genes (DEGs) using R based Limma package, Clust and other statistical tools. The identified DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to identify co-expression patterns of relevant genes. Module trait and gene ontology analyses were performed to establish important gene co-expression networks and their biological functions. Overlapping the most relevant DEG cluster 4 with prominent WGCNA cyan module identified strongest correlation of UBE2Q1 with ovarian cancer and its prognostic significance on survival probability of ovarian cancer patients was investigated. The predictive value of UBE2Q1 as a potential biomarker was analysed by correlating its expression with 12-months relapse free survival of patients in response to platin/taxane, the standard first-line chemotherapy for ovarian cancer, and analysing area under the ROC curve.ResultsAn integrated gene expression analysis and WGCNA, identified UBE2Q1 as a potential prognostic marker associated with poor relapse-free survival and response outcome to platin/taxane treatment of patients with high grade serous ovarian cancer.ConclusionsOur study identifies a potential UBE2Q1 – B4GALT3 functional axis in ovarian cancer, where only the E2 conjugating enzyme showed a poor prognostic impact on the disease.