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      MBKbase for rice: an integrated omics knowledgebase for molecular breeding in rice

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          Abstract

          To date, large amounts of genomic and phenotypic data have been accumulated in the fields of crop genetics and genomic research, and the data are increasing very quickly. However, the bottleneck to using big data in breeding is integrating the data and developing tools for revealing the relationship between genotypes and phenotypes. Here, we report a rice sub-database of an integrated omics knowledgebase (MBKbase-rice, www.mbkbase.org/rice), which integrates rice germplasm information, multiple reference genomes with a united set of gene loci, population sequencing data, phenotypic data, known alleles and gene expression data. In addition to basic data search functions, MBKbase provides advanced web tools for genotype searches at the population level and for visually displaying the relationship between genotypes and phenotypes. Furthermore, the database also provides online tools for comparing two samples by their genotypes and finding target germplasms by genotype or phenotype information, as well as for analyzing the user submitted SNP or sequence data to find important alleles in the germplasm. A soybean sub-database is planned for release in 3 months and wheat and maize will be added in 1–2 years. The data and tools integrated in MBKbase will facilitate research in crop functional genomics and molecular breeding.

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          The genome of Chenopodium quinoa

          Constructing a reference genome for quinoa (Chenopodium quinoa) allows for genetic diversity during the evolution of sub-genomes in quinoa to be characterized and markers that may be used to develop sweet commercial varieties are identified.
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            Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice

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              Molecular genetic dissection of quantitative trait loci regulating rice grain size.

              Grain size is one of the most important factors determining rice yield. As a quantitative trait, grain size is predominantly and tightly controlled by genetic factors. Several quantitative trait loci (QTLs) for grain size have been molecularly identified and characterized. These QTLs may act in independent genetic pathways and, along with other identified genes for grain size, are mainly involved in the signaling pathways mediated by proteasomal degradation, phytohormones, and G proteins to regulate cell proliferation and cell elongation. Many of these QTLs and genes have been strongly selected for enhanced rice productivity during domestication and breeding. These findings have paved new ways for understanding the molecular basis of grain size and have substantial implications for genetic improvement of crops.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 January 2020
                18 October 2019
                18 October 2019
                : 48
                : D1
                : D1085-D1092
                Affiliations
                [1 ] State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences , Beijing 100101, China
                [2 ] University of Chinese Academy of Sciences , Beijing 100049, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 10 64801262; Fax: +86 10 64801262; Email: cliang@ 123456genetics.ac.cn
                Article
                gkz921
                10.1093/nar/gkz921
                7145604
                31624841
                4fa6532c-0de5-4cac-994a-4b7d0b4c48f2
                © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 08 October 2019
                : 04 October 2019
                : 14 August 2019
                Page count
                Pages: 8
                Funding
                Funded by: Strategic Priority Research Program
                Award ID: XDA08020302
                Categories
                Database Issue

                Genetics
                Genetics

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