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Found 4 result(s)

Open Archive for Miscellaneous Data

多元数据归档库

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The Open Archive for Miscellaneous Data (OMIX) database is a data repository developed and maintained by the National Genomics Data Center (NGDC). The database specializes in descriptions of biological studies, including genomic, proteomic, and metabolomic, as well as data that do not fit in the structured archives at other databases in NGDC. It can accept various types of studies described via a simple format and enables researchers to upload supplementary information and link to it from the publication.

Animal QTLdb

Animal Quantitative Trait Loci

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This Animal Quantitative Trait Loci (QTL) database (Animal QTLdb) is designed to house all publicly available QTL and trait mapping data (i.e. trait and genome location association data; collectively called "QTL data" on this site) on livestock animal species for easily locating and making comparisons within and between species. New database tools are continuely added to align the QTL and association data to other types of genome information, such as annotated genes, RH / SNP markers, and human genome maps. Besides the QTL data from species listed below, the QTLdb is open to house QTL/association date from other animal species where feasible. Note that the JAS along with other journals, now require that new QTL/association data be entered into a QTL database as part of their publication requirements.

National Genomics Data Center

国家基因组科学数据中心

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The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), advances life & health sciences by providing open access to a suite of resources, with the aim to translate big data into big discoveries and support worldwide activities in both academia and industry.

Genome-Scale Metabolic Network DataBase

GSMNDB

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<<<!!!<<< 2019-12-23: the repository is offline >>>!!!>>> Introduction of genome-scale metabolic network: The completion of genome sequencing and subsequent functional annotation for a great number of species enables the reconstruction of genome-scale metabolic networks. These networks, together with in silico network analysis methods such as the constraint based methods (CBM) and graph theory methods, can provide us systems level understanding of cellular metabolism. Further more, they can be applied to many predictions of real biological application such as: gene essentiality analysis, drug target discovery and metabolic engineering