Database: The Journal of Biological Databases and Curation最新文献

Rice is a vital staple crop, sustaining over half of the global population, and is a key model for genetic research. To support the growing need for comprehensive and accessible rice genomic data, GrameneOryza (https://oryza.gramene.org) was developed as an online resource adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data management. It distinguishes itself through its comprehensive multispecies focus, encompassing a wide variety of Oryza genomes and related species, and its integration with FAIR principles to ensure data accessibility and usability. It offers a community curated selection of high-quality Oryza genomes, genetic variation, gene function, and trait data. The latest release, version 8, includes 28 Oryza genomes, covering wild rice and domesticated cultivars. These genomes, along with Leersia perrieri and seven additional outgroup species, form the basis for 38 K protein-coding gene family trees, essential for identifying orthologs, paralogs, and developing pan-gene sets. GrameneOryza's genetic variation data features 66 million single-nucleotide variants (SNVs) anchored to the Os-Nipponbare-Reference-IRGSP-1.0 genome, derived from various studies, including the Rice Genome 3 K (RG3K) project. The RG3K sequence reads were also mapped to seven additional platinum-quality Asian rice genomes, resulting in 19 million SNVs for each genome, significantly expanding the coverage of genetic variation beyond the Nipponbare reference. Of the 66 million SNVs on IRGSP-1.0, 27 million acquired standardized reference SNP cluster identifiers (rsIDs) from the European Variation Archive release v5. Additionally, 1200 distinct phenotypes provide a comprehensive overview of quantitative trait loci (QTL) features. The newly introduced Oryza CLIMtools portal offers insights into environmental impacts on genome adaptation. The platform's integrated search interface, along with a BLAST server and curation tools, facilitates user access to genomic, phylogenetic, gene function, and QTL data, supporting broad research applications. Database URL: https://oryza.gramene.org.

Rice is a vital staple crop, sustaining over half of the global population, and is a key model for genetic research. To support the growing need for comprehensive and accessible rice genomic data, GrameneOryza (https://oryza.gramene.org) was developed as an online resource adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data management. It distinguishes itself through its comprehensive multispecies focus, encompassing a wide variety of Oryza genomes and related species, and its integration with FAIR principles to ensure data accessibility and usability. It offers a community curated selection of high-quality Oryza genomes, genetic variation, gene function, and trait data. The latest release, version 8, includes 28 Oryza genomes, covering wild rice and domesticated cultivars. These genomes, along with Leersia perrieri and seven additional outgroup species, form the basis for 38 K protein-coding gene family trees, essential for identifying orthologs, paralogs, and developing pan-gene sets. GrameneOryza's genetic variation data features 66 million single-nucleotide variants (SNVs) anchored to the Os-Nipponbare-Reference-IRGSP-1.0 genome, derived from various studies, including the Rice Genome 3 K (RG3K) project. The RG3K sequence reads were also mapped to seven additional platinum-quality Asian rice genomes, resulting in 19 million SNVs for each genome, significantly expanding the coverage of genetic variation beyond the Nipponbare reference. Of the 66 million SNVs on IRGSP-1.0, 27 million acquired standardized reference SNP cluster identifiers (rsIDs) from the European Variation Archive release v5. Additionally, 1200 distinct phenotypes provide a comprehensive overview of quantitative trait loci (QTL) features. The newly introduced Oryza CLIMtools portal offers insights into environmental impacts on genome adaptation. The platform's integrated search interface, along with a BLAST server and curation tools, facilitates user access to genomic, phylogenetic, gene function, and QTL data, supporting broad research applications. Database URL: https://oryza.gramene.org.

MicroRNAs (miRNAs) can target messenger RNAs to control their degradation or translation repression effects. Therefore, identifying the target and binding sites of different miRNAs is essential for understanding miRNA functions. To investigate these interactions, researchers have employed the cross-linking, ligation, and sequencing of hybrids (CLASH-seq) and similar CLASH-like approaches to generate chimeric reads formed by miRNAs and their targeting segments. These chimeric reads allow for the direct extraction of both the miRNA-target gene pairs and their corresponding binding sites. Nevertheless, these studies lack user-friendly platforms for researchers to investigate these interactions efficiently, thus hindering scientists' ability to explore miRNA functions. To address this gap, we developed mirTarCLASH, a comprehensive database that deposits 502 061/322 707/224 452 unique hybrid reads from human/mouse/worm miRNA chimeric read-based experiments. In mirTarCLASH, the chimera analysis algorithm ChiRA and two distinct binding site inference tools, RNAup and miRanda, were adopted to facilitate the exploration of miRNA-target pairs derived from CLASH-like experiments. Compared with existing similar repositories, mirTarCLASH further enables several confidence evaluation filters with visualization functions for the extracted results. The results can be further refined based on the key properties of the miRNA targeting sites, including read depths, numbers of supporting algorithms, and cross-linking-induced mutations, to enhance confidence levels. In addition, these miRNA-binding sites are visually represented through an integrated transcript atlas. Finally, we demonstrated the biological applicability of mirTarCLASH via the well-characterized example interaction between cel-let-7-5p and lin-41 in Caenorhabditis elegans, showcasing the potential of mirTarCLASH to provide novel insights for subsequent experimental research designs. The constructed mirTarCLASH database is freely available at https://cosbi.ee.ncku.edu.tw/MirTarClash. Database URL: https://cosbi.ee.ncku.edu.tw/MirTarClash.

MicroRNAs (miRNAs) can target messenger RNAs to control their degradation or translation repression effects. Therefore, identifying the target and binding sites of different miRNAs is essential for understanding miRNA functions. To investigate these interactions, researchers have employed the cross-linking, ligation, and sequencing of hybrids (CLASH-seq) and similar CLASH-like approaches to generate chimeric reads formed by miRNAs and their targeting segments. These chimeric reads allow for the direct extraction of both the miRNA-target gene pairs and their corresponding binding sites. Nevertheless, these studies lack user-friendly platforms for researchers to investigate these interactions efficiently, thus hindering scientists' ability to explore miRNA functions. To address this gap, we developed mirTarCLASH, a comprehensive database that deposits 502 061/322 707/224 452 unique hybrid reads from human/mouse/worm miRNA chimeric read-based experiments. In mirTarCLASH, the chimera analysis algorithm ChiRA and two distinct binding site inference tools, RNAup and miRanda, were adopted to facilitate the exploration of miRNA-target pairs derived from CLASH-like experiments. Compared with existing similar repositories, mirTarCLASH further enables several confidence evaluation filters with visualization functions for the extracted results. The results can be further refined based on the key properties of the miRNA targeting sites, including read depths, numbers of supporting algorithms, and cross-linking-induced mutations, to enhance confidence levels. In addition, these miRNA-binding sites are visually represented through an integrated transcript atlas. Finally, we demonstrated the biological applicability of mirTarCLASH via the well-characterized example interaction between cel-let-7-5p and lin-41 in Caenorhabditis elegans, showcasing the potential of mirTarCLASH to provide novel insights for subsequent experimental research designs. The constructed mirTarCLASH database is freely available at https://cosbi.ee.ncku.edu.tw/MirTarClash. Database URL: https://cosbi.ee.ncku.edu.tw/MirTarClash.

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be quite complicated due to privacy and legal concerns of the data. In such cases, federated learning has the potential to vastly speed up the research cycle. Although federated and central learning have been compared from a theoretical perspective, an extensive experimental comparison of performances and learning behavior still lacks. We have performed a comprehensive experimental comparison between federated and centralized learning. We evaluated various classifiers on various datasets exploring influences of different sample distributions as well as different class distributions across the clients. The results show similar performances under a wide variety of settings between the federated and central learning strategies. Federated learning is able to deal with various imbalances in the data distributions. It is sensitive to batch effects between different datasets when they coincide with location, similar to central learning, but this setting might go unobserved more easily. Federated learning seems to be robust to various challenges such as skewed data distributions, high data dimensionality, multiclass problems, and complex models. Taken together, the insights from our comparison gives much promise for applying federated learning as an alternative to sharing data. Code for reproducing the results in this work can be found at: https://github.com/swiergarst/FLComparison.

Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be quite complicated due to privacy and legal concerns of the data. In such cases, federated learning has the potential to vastly speed up the research cycle. Although federated and central learning have been compared from a theoretical perspective, an extensive experimental comparison of performances and learning behavior still lacks. We have performed a comprehensive experimental comparison between federated and centralized learning. We evaluated various classifiers on various datasets exploring influences of different sample distributions as well as different class distributions across the clients. The results show similar performances under a wide variety of settings between the federated and central learning strategies. Federated learning is able to deal with various imbalances in the data distributions. It is sensitive to batch effects between different datasets when they coincide with location, similar to central learning, but this setting might go unobserved more easily. Federated learning seems to be robust to various challenges such as skewed data distributions, high data dimensionality, multiclass problems, and complex models. Taken together, the insights from our comparison gives much promise for applying federated learning as an alternative to sharing data. Code for reproducing the results in this work can be found at: https://github.com/swiergarst/FLComparison.

Variant interpretation guidelines guide the process of determining the role of DNA variants in patients' health. Currently, hundreds of guidelines exist, each applicable to a particular clinical domain. However, they are scattered across multiple resources and scientific literature. To address this issue, we present VarGuideAtlas, a comprehensive repository of variant interpretation guidelines that compiles information from ClinGen, ClinVar, and PubMed. Our repository offers a user-friendly web interface with advanced search capabilities, enabling clinicians and researchers to efficiently find relevant guidelines tailored to specific genes, diseases, or variant types. We employ ontologies to characterize each guideline, ensuring consistency and improving interoperability with bioinformatics tools. VarGuideAtlas represents a significant advance toward standardizing variant interpretation practices, facilitating more informed decision-making, improved clinical outcomes, and more precise genomic research. VarGuideAtlas is publicly accessible via a web-based platform (https://genomics-hub.pros.dsic.upv.es:3016/).

Variant interpretation guidelines guide the process of determining the role of DNA variants in patients' health. Currently, hundreds of guidelines exist, each applicable to a particular clinical domain. However, they are scattered across multiple resources and scientific literature. To address this issue, we present VarGuideAtlas, a comprehensive repository of variant interpretation guidelines that compiles information from ClinGen, ClinVar, and PubMed. Our repository offers a user-friendly web interface with advanced search capabilities, enabling clinicians and researchers to efficiently find relevant guidelines tailored to specific genes, diseases, or variant types. We employ ontologies to characterize each guideline, ensuring consistency and improving interoperability with bioinformatics tools. VarGuideAtlas represents a significant advance toward standardizing variant interpretation practices, facilitating more informed decision-making, improved clinical outcomes, and more precise genomic research. VarGuideAtlas is publicly accessible via a web-based platform (https://genomics-hub.pros.dsic.upv.es:3016/).