Ontology highlight
ABSTRACT:
PROVIDER: PRJEB87510 | ENA |
REPOSITORIES: ENA
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Project description:The dataset contains genetic variant information derived from 330 chestnut accessions
2025-03-27 | PRJEB87510 | EVA
Project description:In this study, the Chinese chestnut ‘Huaihuang’ was used to explore the possible mechanisms of ovule abortion with respect to proteomics. The chestnut anthesis starts mid-June. The development of the burs of C. mollissima cv. ‘Huaihuang’ were monitored from 15 to 25 days after anthesis (DAA) And the burs for different times were collected from the Chestnut Experiment Station in Huairou District, Beijing, China. This experiment was conducted at the Beijing Protein Innovation Co., Ltd.
2021-01-20 | PXD021317 | Pride
Project description:The Chinese chestnut (Castanea mollissima) stands out as a plant with significant ecological and economic value, excellent nutritional quality and natural resistance to pests and diseases. Recent strides in high-throughput techniques have enabled the continuous accumulation of genomic data on chestnuts, presenting a promising future for genetic research and advancing traits in this species. To facilitate the accessibility and utility of this data, we have curated and validated a collection of genomic datasets for eight Castanea species, 213 RNA-Seq samples, and 348 resequencing samples. These datasets are publicly available on figshare, providing a robust resource for researchers studying Castanea genetics, functional genomics, and evolutionary biology. Additionally, the Castanea Genome Database (CGD, http://castaneadb.net) serves as a complementary platform, offering advanced data mining and analysis tools, including BLAST, Batch Query, GO/KEGG Enrichment Analysis, and Synteny Viewer, to enhance the usability of the curated datasets.
2024-12-24 | GSE284518 | GEO
Project description:The Chinese chestnut (Castanea mollissima) stands out as a plant with significant ecological and economic value, excellent nutritional quality and natural resistance to pests and diseases. Recent strides in high-throughput techniques have enabled the continuous accumulation of genomic data on chestnuts, presenting a promising future for genetic research and advancing traits in this species. To facilitate the accessibility and utility of this data, we have curated and validated a collection of genomic datasets for eight Castanea species, 213 RNA-Seq samples, and 348 resequencing samples. These datasets are publicly available on figshare, providing a robust resource for researchers studying Castanea genetics, functional genomics, and evolutionary biology. Additionally, the Castanea Genome Database (CGD, http://castaneadb.net) serves as a complementary platform, offering advanced data mining and analysis tools, including BLAST, Batch Query, GO/KEGG Enrichment Analysis, and Synteny Viewer, to enhance the usability of the curated datasets.
2024-12-24 | GSE284516 | GEO
Project description:The Chinese chestnut (Castanea mollissima) stands out as a plant with significant ecological and economic value, excellent nutritional quality and natural resistance to pests and diseases. Recent strides in high-throughput techniques have enabled the continuous accumulation of genomic data on chestnuts, presenting a promising future for genetic research and advancing traits in this species. To facilitate the accessibility and utility of this data, we have curated and validated a collection of genomic datasets for eight Castanea species, 213 RNA-Seq samples, and 348 resequencing samples. These datasets are publicly available on figshare, providing a robust resource for researchers studying Castanea genetics, functional genomics, and evolutionary biology. Additionally, the Castanea Genome Database (CGD, http://castaneadb.net) serves as a complementary platform, offering advanced data mining and analysis tools, including BLAST, Batch Query, GO/KEGG Enrichment Analysis, and Synteny Viewer, to enhance the usability of the curated datasets.
2024-12-24 | GSE284510 | GEO
Project description:The Chinese chestnut (Castanea mollissima) stands out as a plant with significant ecological and economic value, excellent nutritional quality and natural resistance to pests and diseases. Recent strides in high-throughput techniques have enabled the continuous accumulation of genomic data on chestnuts, presenting a promising future for genetic research and advancing traits in this species. To facilitate the accessibility and utility of this data, we have curated and validated a collection of genomic datasets for eight Castanea species, 213 RNA-Seq samples, and 348 resequencing samples. These datasets are publicly available on figshare, providing a robust resource for researchers studying Castanea genetics, functional genomics, and evolutionary biology. Additionally, the Castanea Genome Database (CGD, http://castaneadb.net) serves as a complementary platform, offering advanced data mining and analysis tools, including BLAST, Batch Query, GO/KEGG Enrichment Analysis, and Synteny Viewer, to enhance the usability of the curated datasets.
2024-12-24 | GSE284517 | GEO
Project description:<p>Garrod’s concept of “chemical individuality” has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P<1.25x10^-11) within 330 genomic regions, with rare variants (MAF≤1%) explaining 9.4% of associations. Jointly modelling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called GIMs (Genetically Influenced Metabotypes). We assigned causal genes for 62.4% of GIMs, providing new insights into fundamental metabolite physiology and their clinical relevance, including metabolite guided discovery of potential adverse drug effects (<em>DPYD</em>, <em>SRD5A2</em>). We show strong enrichment of Inborn Errors of Metabolism (IEM)-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of IEMs. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential aetiological relationships.</p><p><br></p><p><strong>EPIC-Norfolk study assays</strong> are reported in the current study <strong>MTBLS833</strong></p><p><strong>INTERVAL study assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS834' rel='noopener noreferrer' target='_blank'><strong>MTBLS834</strong></a></p>
2022-09-09 | MTBLS833 | MetaboLights
Project description:A major effort is underway to study the natural variation within the model plant species, Arabidopsis thaliana. Much of this effort is focused on genome resequencing, however the translation of genotype to phenotype will be largely effected through variations within the transcriptomes at the sequence and expression levels. To examine the cross-talk between natural variation in genomes and transcriptomes, we have examined the transcriptomes of three divergent A. thaliana accessions using tiling arrays. Combined with genome resequencing efforts, we were able to adjust the tiling array datasets to account for polymorphisms between the accessions and therefore gain a more accurate comparison of the transcriptomes. The corrected results for the transcriptomes allowed us to correlate higher gene polymorphism with greater variation in transcript level among the accessions. Our results demonstrate the utility of combining genomic data with tiling arrays to assay non-reference accession transcriptomes.
2012-05-22 | GSE38109 | GEO
Project description:A major effort is underway to study the natural variation within the model plant species, Arabidopsis thaliana. Much of this effort is focused on genome resequencing, however the translation of genotype to phenotype will be largely effected through variations within the transcriptomes at the sequence and expression levels. To examine the cross-talk between natural variation in genomes and transcriptomes, we have examined the transcriptomes of three divergent A. thaliana accessions using tiling arrays. Combined with genome resequencing efforts, we were able to adjust the tiling array datasets to account for polymorphisms between the accessions and therefore gain a more accurate comparison of the transcriptomes. The corrected results for the transcriptomes allowed us to correlate higher gene polymorphism with greater variation in transcript level among the accessions. Our results demonstrate the utility of combining genomic data with tiling arrays to assay non-reference accession transcriptomes.
2012-03-12 | GSE24669 | GEO
Project description:<p>Garrod’s concept of “chemical individuality” has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P<1.25x10^-11) within 330 genomic regions, with rare variants (MAF≤1%) explaining 9.4% of associations. Jointly modelling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called GIMs (Genetically Influenced Metabotypes). We assigned causal genes for 62.4% of GIMs, providing new insights into fundamental metabolite physiology and their clinical relevance, including metabolite guided discovery of potential adverse drug effects (<em>DPYD</em>, <em>SRD5A2</em>). We show strong enrichment of Inborn Errors of Metabolism (IEM)-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of IEMs. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential aetiological relationships.</p><p><br></p><p><strong>INTERVAL study assays</strong> are reported in the current study <strong>MTBLS834</strong></p><p><strong>EPIC-Norfolk study assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS833' rel='noopener noreferrer' target='_blank'><strong>MTBLS833</strong></a></p>
2022-09-09 | MTBLS834 | MetaboLights