Diagnostic interpretation of array data using public databases and internet sources.
Diagnostic interpretation of array data using public databases and internet sources.
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DOI：
10.1002/humu.22049
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发表时间：
2012-06
期刊：
影响因子：
3.9
通讯作者：
Hastings, Ros
中科院分区：
文献类型：
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作者：
de Leeuw, Nicole;Dijkhuizen, Trijnie;Hehir-Kwa, Jayne Y.;Carter, Nigel P.;Feuk, Lars;Firth, Helen V.;Kuhn, Robert M.;Ledbetter, David H.;Martin, Christa Lese;van Ravenswaaij-Arts, Conny M. A.;Scherer, Steven W.;Shams, Soheil;Van Vooren, Steven;Sijmons, Rolf;Swertz, Morris;Hastings, Ros
商业上可用的阵列平台和分析软件包的范围正在扩大，它们的实用性正在提高，使得拷贝数变异（CNV）的可靠检测相对简单。然而，CNV数据的可靠解释通常是困难的，需要专业知识。随着我们对人类基因组的了解迅速增长，阵列测试的应用不断扩大，CNV检测的分辨率不断提高，这导致解释令人生畏的数据变得非常复杂。正确的CNV解释和最佳使用基因型信息提供的单核苷酸多态性探针阵列上很大程度上取决于知识存在于各种资源。除了宿主实验室自己的数据集和国家登记处的可用性外，还有几个公共数据库和互联网资源，其中包含可用于阵列数据解释的基因型和表型信息。现在有这么多的资源，重要的是要知道哪些是适合在诊断设置的目的。我们总结了最常用的互联网数据库和资源的特点，并提出了一个通用的数据解释策略，可用于比较杂交，比较强度，和基于基因型的阵列数据。
The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy-number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single-nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories’ own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit-for-purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype-based array data.
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影响因子：
1.3
作者：
Neill NJ;Torchia BS;Bejjani BA;Shaffer LG;Ballif BC
通讯作者：
Ballif BC
影响因子：
30.8
作者：
Alkan, Can;Kidd, Jeffrey M.;Marques-Bonet, Tomas;Aksay, Gozde;Antonacci, Francesca;Hormozdiari, Fereydoun;Kitzman, Jacob O.;Baker, Carl;Malig, Maika;Mutlu, Onur;Sahinalp, S. Cenk;Gibbs, Richard A.;Eichler, Evan E.
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Eichler, Evan E.
影响因子：
30.8
作者：
Iafrate, AJ;Feuk, L;Lee, C
通讯作者：
Lee, C
影响因子：
1.9
作者：
Feenstra, I.;Fang, J.;Schinzel, A.
通讯作者：
Schinzel, A.
影响因子：
64.8
作者：
通讯作者：
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