Delacruzbenton4099

0 and IWGSC RefSeq v2.0). Selective footprints for Aegilops tauschii, wild emmer, domesticated emmer, bread wheat landrace and bread wheat variety are evaluated with two statistical tests (FST and Pi) based on SNPs from whole-genome resequencing data. In addition, we provide the Genome Browser to visualize the genomic variations, the selective footprints, the genotype patterns and the read coverage depth, and the alignment tool Blast to search the homologous regions between sequences. All of these features of WGVD will promote wheat functional studies and wheat breeding.

http//animal.nwsuaf.edu.cn/code/index.php/Wheat.

http//animal.nwsuaf.edu.cn/code/index.php/Wheat.Citrus canker type A is a serious disease caused by Xanthomonas citri subsp. citri (X. citri), which is responsible for severe losses to growers and to the citrus industry worldwide. To date, no canker-resistant citrus genotypes are available, and there is limited information regarding the molecular and genetic mechanisms involved in the early stages of the citrus canker development. Here, we present the CitrusKB knowledge base. This is the first in vivo interactome database for different citrus cultivars, and it was produced to provide a valuable resource of information on citrus and their interaction with the citrus canker bacterium X. citri. CitrusKB provides tools for a user-friendly web interface to let users search and analyse a large amount of information regarding eight citrus cultivars with distinct levels of susceptibility to the disease, with controls and infected plants at different stages of infection by the citrus canker bacterium X. citri. Currently, CitrusKB comprises a reference citrus genome and its transcriptome, expressed transcripts, pseudogenes and predicted genomic variations (SNPs and SSRs). The updating process will continue over time by the incorporation of novel annotations and analysis tools. We expect that CitrusKB may substantially contribute to the field of citrus genomics. CitrusKB is accessible at http//bioinfo.deinfo.uepg.br/citrus. Users can download all the generated raw sequences and generated datasets by this study from the CitrusKB website.Accumulating evidences have shown that the deregulation of circRNA has close association with many human cancers. selleckchem However, these experimental verified circRNA-cancer associations are not collected in any database. Here, we develop a manually curated database (circR2Cancer) that provides experimentally supported associations between circRNAs and cancers. The current version of the circR2Cancer contains 1439 associations between 1135 circRNAs and 82 cancers by extracting data from existing literatures and databases. In addition, circR2Cancer contains the information of cancer exacted from Disease Ontology and basic biological information of circRNAs from circBase. At the same time, circR2Cancer provides a simple and friendly interface for users to conveniently browse, search and download the data. It will be a useful and valuable resource for researchers to understanding the regulation mechanism of circRNA in cancers.

http//www.biobdlab.cn8000.

http//www.biobdlab.cn8000.The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein-protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL https//github.com/liseda-lab/kgsim-benchmark.Publicly available genetic databases promote data sharing and fuel scientific discoveries for the prevention, treatment and management of disease. In 2018, we built Color Data, a user-friendly, open access database containing genotypic and self-reported phenotypic information from 50 000 individuals who were sequenced for 30 genes associated with hereditary cancer. In a continued effort to promote access to these types of data, we launched Color Data v2, an updated version of the Color Data database. This new release includes additional clinical genetic testing results from more than 18 000 individuals who were sequenced for 30 genes associated with hereditary cardiovascular conditions as well as polygenic risk scores for breast cancer, coronary artery disease and atrial fibrillation. In addition, we used self-reported phenotypic information to implement the following four clinical risk models Gail Model for 5-year risk of breast cancer, Claus Model for lifetime risk of breast cancer, simple office-based Framingham Coronary Heart Disease Risk Score for 10-year risk of coronary heart disease and CHARGE-AF simple score for 5-year risk of atrial fibrillation. These new features and capabilities are highlighted through two sample queries in the database. We hope that the broad dissemination of these data will help researchers continue to explore genotype-phenotype correlations and identify novel variants for functional analysis, enabling scientific discoveries in the field of population genomics. Database URL https//data.color.com/.Species checklists are a crucial source of information for research and policy. Unfortunately, many traditional species checklists vary wildly in their content, format, availability and maintenance. The fact that these are not open, findable, accessible, interoperable and reusable (FAIR) severely hampers fast and efficient information flow to policy and decision-making that are required to tackle the current biodiversity crisis. Here, we propose a reproducible, semi-automated workflow to transform traditional checklist data into a FAIR and open species registry. We showcase our workflow by applying it to the publication of the Manual of Alien Plants, a species checklist specifically developed for the Tracking Invasive Alien Species (TrIAS) project. Our approach combines source data management, reproducible data transformation to Darwin Core using R, version control, data documentation and publication to the Global Biodiversity Information Facility (GBIF). This checklist publication workflow is openly available for data holders and applicable to species registries varying in thematic, taxonomic or geographical scope and could serve as an important tool to open up research and strengthen environmental decision-making.