1-20 of 8893
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GrameneOrzya site infrastructure and its components. The Ensembl data cores...
Published: 05 April 2025
Figure 2.
GrameneOrzya site infrastructure and its components. The Ensembl data cores for genomes, genetic variation, and comparative analyses (protein and DNA) were installed on a dedicated 250 GB Linux CentOS image with 16 GB memory and 2 CPUs. The core layer, represented in yellow, and the outer layer, dep
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Community curations in GrameneOryza interface. (a) Homology-based gene stru...
Published: 05 April 2025
Figure 6.
Community curations in GrameneOryza interface. (a) Homology-based gene structure inspection interface allows users to flag problematic models for further inspection and improvement. (b) Paper tab displays the functions curated by RAP-DB and GeneRIF, but also prompts users to enter their curation of
Journal Article
mirTarCLASH: a comprehensive miRNA target database based on chimeric read-based experiments
Database, Volume 2025, 2025, baaf023, https://doi.org/10.1093/database/baaf023
Published: 05 April 2025
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The detail page in mirTarCLASH. (a) The query settings for the miRNA–mRNA p...
Published: 05 April 2025
Figure 3.
The detail page in mirTarCLASH. (a) The query settings for the miRNA–mRNA pair under investigation. (b) The confidence filters for the listed pairs. The detailed search results of a specific miRNA–target transcript pair in the tabular (c) and graphical (d) view.
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The browse mode in mirTarCLASH. (a) The browse settings. (b) The listed miR...
Published: 05 April 2025
Figure 4.
The browse mode in mirTarCLASH. (a) The browse settings. (b) The listed miRNA results when users intend to browse by miRNA. (c) The listed mRNA results when users intend to browse by mRNA. Click the “Show detail” link or the “Show miRNA details” link, and the detailed chimeric read information of th
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A systems level view of the core functionality of the GrameneOryza site. (a...
Published: 05 April 2025
Figure 1.
A systems level view of the core functionality of the GrameneOryza site. (a) Input data: omics data to load to the core database, this includes processed data as well as metadata, (b) Databases: build core database as well as database for search, (c) Analysis workflow: in-house workflows to generate
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GrameneOryza user interface: (a) Search: textual query search for genes. (b...
Published: 05 April 2025
Figure 3.
GrameneOryza user interface: (a) Search: textual query search for genes. (b) Quick links to Genome Browser, News, Release Notes, Guides, and Feedback form. (c) Tool and services panel with access to curated and published rice genes and other Gramene pansites as well as tools like BLAST, CLIMtools fo
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The search mode in mirTarCLASH. (a) The query settings. (b) The query resul...
Published: 05 April 2025
Figure 2.
The search mode in mirTarCLASH. (a) The query settings. (b) The query result table. Click the “Show detail” link, and the detailed chimera analysis information for the pair will be provided.
Journal Article
GrameneOryza: a comprehensive resource for Oryza genomes, genetic variation, and functional data
Database, Volume 2025, 2025, baaf021, https://doi.org/10.1093/database/baaf021
Published: 05 April 2025
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Use Case for finding copy number variations (CNVs) using GrameneOryza inter...
Published: 05 April 2025
Figure 4.
Use Case for finding copy number variations (CNVs) using GrameneOryza interface: (a) Gene Search: Keyword search produces many hits in different categories. One of the hits in the category “gene” is the rice version of SLB OsSLB1 . (b) Homology View: the neighborhood view shows CNV across the lands
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Identification of PTVs using the GrameneOryza interface. (a) Germplasm: dis...
Published: 05 April 2025
Figure 5.
Identification of PTVs using the GrameneOryza interface. (a) Germplasm: displays a list of germplasms containing predicted loss-of-function alleles for the GS3 gene. Clicking the hyperlinked text “Variant image” will bring you to the Ensembl genome browser’s gene page Variant Image panel. Clicking
Journal Article
Post-composing ontology terms for efficient phenotyping in plant breeding
Database, Volume 2025, 2025, baaf020, https://doi.org/10.1093/database/baaf020
Published: 21 March 2025
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Database schema diagram of the tables required for storing post-composed on...
Published: 21 March 2025
Figure 1.
Database schema diagram of the tables required for storing post-composed ontology terms.
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The user interface for post-composing traits from SweetPotatoBase. Selectin...
Published: 21 March 2025
Figure 2.
The user interface for post-composing traits from SweetPotatoBase. Selecting the trait ontology term CO_331:0000294 along with time terms “month 1, month 2, month 3, and month 4” and the breeding event term “after harvest.” The interface shows one of the combinations is already stored in the databas
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Post-composing traits using orthogonal ontologies by crop. Percentage of us...
Published: 21 March 2025
Figure 4.
Post-composing traits using orthogonal ontologies by crop. Percentage of usage of each orthogonal ontology ( y -axis) by crop ( x -axis). Time of year and event ontologies are common for all crops, while treatment, cycle, plant section, and plant level are crop-specific.
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Ratios of post-composed/pre-composed trait annotations by crop. (a) cassava...
Published: 21 March 2025
Figure 3.
Ratios of post-composed/pre-composed trait annotations by crop. (a) cassava, yam, and sweet potato. (b) Banana. The numeric values for this graph are listed in Table 1
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Most used orthogonal ontology terms (x-axis) by percentage usage (...
Published: 21 March 2025
Figure 5.
Most used orthogonal ontology terms ( x -axis) by percentage usage ( y -axis) in each Breedbase crop.
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Percentage and total number of unique post-composed and unique pre-composed...
Published: 21 March 2025
Figure 6.
Percentage and total number of unique post-composed and unique pre-composed traits by crop (cassava, banana, yam, and sweet potato) and trait group (A, B, and C).
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AUC performance of the five classifiers across different splits of dataset ...
Published: 19 March 2025
Figure 3.
AUC performance of the five classifiers across different splits of dataset A2 across 10 clients in (a), FNN results when all three AML datasets A1–A3 are used with the original data are shown in (b), and (c) shows the FNN results on A1–A3 using the locally scaled data.
