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NIH Knockout Mouse Phenotyping Program (KOMP2) and IMPC: Database to Discover New Roles of Genes in Cardiovascular Physiology and Disease (Retraction Article, 2019) (Retraction of 2019)

机译:NIH淘汰鼠标表型计划(KOMP2)和IMPC:数据库,以发现心血管生理和疾病中基因的新作用(撤回文章,2019年)(2019年撤回)

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摘要

The collaborative goal of NIH Knockout Mouse Phenotyping Program (KOMP2) and International Knockout Mouse Phenotyping Consortium (IMPC) is to discover functional insight for every gene in the mouse genome by 2021, by generating and systematically phenotyping approximately 20,000 unique knockout (KO) mouse strains. The purpose of the present study is to introduce the KOMP2/IMPC program and its publicly-accessible gene-phenotype database to the research community and to specifically illustrate its utility for the identification of novel gene candidates in cardiovascular (CV) disease. In this report, we have focused on single gene deletions found associated with CV phenotype as part of the KOMP2/IMPC phenotyping of broad physiological domains in more than 5,500 single gene KO mice. Among the 694 single genes found to result in a CV phenotype, we identified about one third (36%, n=248) that had not been previously associated with the CV system. We also searched the remainder of more than 5,500 genes that had not been previously associated with the CV system. We used the results of gene-disease relationship analysis from Medline sentences with an algorithm called Ensemble Biclustering for Classification (EBC), which uncovers relationships between biomedical entities. In addition, we studied their interactions in protein-protein interaction networks. These genes may present opportunities for new CV research and may provide new targets for therapeutic intervention for various CV diseases.
机译:NIH淘汰赛鼠标表型计划(KOMP2)和国际淘汰赛小鼠表型联盟(IMPC)的协作目标是通过产生和系统地表型以约20,000个独特的敲除(KO)小鼠菌株来发现小鼠基因组中每个基因的功能洞察力。本研究的目的是向研究界引入KOMP2 / IMPC程序及其可公开可访问的基因表型数据库,并具体说明其用于鉴定心血管(CV)疾病中新型基因候选的实用性。在本报告中,我们专注于发现与CV表型相关的单一基因缺失,作为大于5,500个单一基因KO小鼠的广泛生理结构域的KOMP2 / IMPC表型的一部分。在导致CV表型的694个单一基因中,我们鉴定了尚未与CV系统相关的三分之一(36%,n = 248)。我们还搜索了以前没有与CV系统相关联的超过5,500个基因的剩余部分。我们利用来自Medline句子的基因疾病关系分析结果与称为集分类(EBC)的集合BICLUSTING的算法,其揭示了生物医学实体之间的关系。此外,我们研究了它们在蛋白质 - 蛋白质相互作用网络中的相互作用。这些基因可能为新的CV研究提供机会,并且可以为各种CV疾病提供治疗干预的新目标。

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