Larmande Pierre, Pittolat Bertrand, Tando Ndomassi, Pomie Yann, Happi Happi Bill Gates, Guignon Valentin, Ruiz Manuel
{"title":"AgroLD: a knowledge graph for the plant sciences.","authors":"Larmande Pierre, Pittolat Bertrand, Tando Ndomassi, Pomie Yann, Happi Happi Bill Gates, Guignon Valentin, Ruiz Manuel","doi":"10.1186/s12863-025-01359-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The demand for food is expected to grow substantially in the coming years. To address this challenge, especially in the context of climate change, a deeper understanding of genotype-phenotype relationships is crucial for improving crop yields. Recent advances in high-throughput technologies have transformed the landscape of plant science research. However, there is an urgent need to integrate and consolidate complementary data to understand the biological system.</p><p><strong>Results: </strong>We introduce AgroLD, a knowledge graph that uses Semantic Web technologies to seamlessly integrate plant science data. AgroLD is designed to facilitate hypothesis formulation and validation within the scientific community. With approximately 1.08 billion triples, it integrates and annotates data from more than 151 datasets across 19 distinct sources.</p><p><strong>Conclusion: </strong>The overarching goal is to provide a specialized knowledge platform addressing complex biological questions in the plant sciences, including gene participation in plant disease resistance and adaptive responses to climate change.</p>","PeriodicalId":72427,"journal":{"name":"BMC genomic data","volume":"26 Suppl 1","pages":"73"},"PeriodicalIF":2.5000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495601/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC genomic data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12863-025-01359-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The demand for food is expected to grow substantially in the coming years. To address this challenge, especially in the context of climate change, a deeper understanding of genotype-phenotype relationships is crucial for improving crop yields. Recent advances in high-throughput technologies have transformed the landscape of plant science research. However, there is an urgent need to integrate and consolidate complementary data to understand the biological system.
Results: We introduce AgroLD, a knowledge graph that uses Semantic Web technologies to seamlessly integrate plant science data. AgroLD is designed to facilitate hypothesis formulation and validation within the scientific community. With approximately 1.08 billion triples, it integrates and annotates data from more than 151 datasets across 19 distinct sources.
Conclusion: The overarching goal is to provide a specialized knowledge platform addressing complex biological questions in the plant sciences, including gene participation in plant disease resistance and adaptive responses to climate change.