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本文引用的文献

1
Evaluation of different domain-based methods in protein interaction prediction.蛋白质相互作用预测中不同基于结构域方法的评估。
Biochem Biophys Res Commun. 2009 Dec 18;390(3):357-62. doi: 10.1016/j.bbrc.2009.09.130. Epub 2009 Oct 2.
2
Mammalian two-hybrids come of age.哺乳动物双杂交技术日臻成熟。
Trends Biochem Sci. 2009 Nov;34(11):579-88. doi: 10.1016/j.tibs.2009.06.009. Epub 2009 Sep 26.
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Recent molecular biological progress in Marfan syndrome and Marfan-associated disorders.马凡综合征及相关疾病的最新分子生物学进展。
Ageing Res Rev. 2010 Jul;9(3):363-8. doi: 10.1016/j.arr.2009.09.001. Epub 2009 Sep 17.
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From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways.从单核苷酸多态性到信号通路:序列变异功能效应在细胞信号通路模型中的整合
BMC Bioinformatics. 2009 Aug 27;10 Suppl 8(Suppl 8):S6. doi: 10.1186/1471-2105-10-S8-S6.
5
Epistasis and its implications for personal genetics.上位效应及其对个人遗传学的影响。
Am J Hum Genet. 2009 Sep;85(3):309-20. doi: 10.1016/j.ajhg.2009.08.006.
6
Genome-wide association studies: progress in identifying genetic biomarkers in common, complex diseases.全基因组关联研究:在常见复杂疾病中识别遗传生物标志物的进展
Biomark Insights. 2007 Aug 8;2:283-92.
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Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip.设计全基因组关联研究:样本量、效能、填补以及基因分型芯片的选择
PLoS Genet. 2009 May;5(5):e1000477. doi: 10.1371/journal.pgen.1000477. Epub 2009 May 15.
8
Association between polymorphisms in RMI1, TOP3A, and BLM and risk of cancer, a case-control study.RMI1、TOP3A和BLM基因多态性与癌症风险的关联:一项病例对照研究
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9
The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies.鲜有人走的道路:在大规模药物遗传学关联研究的全基因组数据分析中,从候选基因迈向候选通路
Curr Pharmacogenomics Person Med. 2008;6(3):150-159. doi: 10.2174/1875692110806030150.
10
Protein-protein interaction databases: keeping up with growing interactomes.蛋白质-蛋白质相互作用数据库:紧跟不断增长的相互作用组
Hum Genomics. 2009 Apr;3(3):291-7. doi: 10.1186/1479-7364-3-3-291.

蛋白质-蛋白质相互作用数据库在人类遗传学中的作用。

Role for protein-protein interaction databases in human genetics.

机构信息

Computational Genetics Laboratory and Department of Genetics, Dartmouth Medical School, Lebanon, NH, USA.

出版信息

Expert Rev Proteomics. 2009 Dec;6(6):647-59. doi: 10.1586/epr.09.86.

DOI:10.1586/epr.09.86
PMID:19929610
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2813729/
Abstract

Proteomics and the study of protein-protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein-protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein-protein interactions in human genetics and genetic epidemiology. Since protein-protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.

摘要

蛋白质组学和蛋白质-蛋白质相互作用的研究在我们从系统层面理解人类疾病的努力中变得越来越重要。由于蛋白质相互作用数据库的开发和整理,这些相互作用网络的最新信息可供科学界获取和公开使用。随着我们对蛋白质-蛋白质相互作用的了解不断增加,我们必须考虑这些资源可能会以不同的方式影响生物医学研究。在本文中,我们强调了蛋白质-蛋白质相互作用在人类遗传学和遗传流行病学中的重要性。由于蛋白质-蛋白质相互作用显示了基因之间最强的功能关系之一,因此将基因组数据与可用的蛋白质组数据相结合,可能会让我们更深入地了解常见的人类疾病。在这篇综述中,我们将讨论一些蛋白质相互作用的基本原理、可公开获取的数据库以及如何使用这些数据库来促进全基因组遗传研究。