AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments.
AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments.
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DOI：
10.1093/database/bar045
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Stoeckert CJ Jr
中科院分区：
文献类型：
--
作者：
Zheng J;Stoyanovich J;Manduchi E;Liu J;Stoeckert CJ Jr
生物数据集的规模不断扩大，特别是在高通量技术背景下产生的数据集，需要开发丰富的数据探索工具。在这篇文章中，我们提出了AnnotCompute，一个信息发现平台，用于功能基因组学实验的存储库，如ArrayExpress。我们的系统利用语义注释的功能基因组学实验与控制词汇和本体术语，如那些从MGED本体，计算概念之间的差异对实验。这些差异，然后用来支持两种类型的探索性分析聚类和查询的例子。我们表明，我们提出的相异性措施对应于用户的直觉概念相异性，并可用于支持有效的查询的例子。我们还评估了基于这些措施的聚类质量。虽然AnnotCompute可以支持更丰富的数据探索体验，但由于可用注释的质量，其有效性在某些情况下是有限的。尽管如此，诸如AnnotCompute之类的工具可以为更丰富的实验注释提供激励。代码可在http://www.cbil.upenn.edu/downloads/AnnotCompute下载。数据库URL：http://www.cbil.upenn.edu/annotCompute/
The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute. Database URL: http://www.cbil.upenn.edu/annotCompute/
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影响因子：
5.8
作者：
Whetzel, PL;Parkinson, H;Stoeckert, CJ
通讯作者：
Stoeckert, CJ
影响因子：
46.9
作者：
Butte, AJ;Kohane, IS
通讯作者：
Kohane, IS
DOI：
10.1093/bioinformatics/btq099
发表时间：
2010-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Malone J;Holloway E;Adamusiak T;Kapushesky M;Zheng J;Kolesnikov N;Zhukova A;Brazma A;Parkinson H
通讯作者：
Parkinson H
影响因子：
14.9
作者：
Thorisson GA;Lancaster O;Free RC;Hastings RK;Sarmah P;Dash D;Brahmachari SK;Brookes AJ
通讯作者：
Brookes AJ
影响因子：
14.9
作者：
Sayers EW;Barrett T;Benson DA;Bolton E;Bryant SH;Canese K;Chetvernin V;Church DM;Dicuccio M;Federhen S;Feolo M;Geer LY;Helmberg W;Kapustin Y;Landsman D;Lipman DJ;Lu Z;Madden TL;Madej T;Maglott DR;Marchler-Bauer A;Miller V;Mizrachi I;Ostell J;Panchenko A;Pruitt KD;Schuler GD;Sequeira E;Sherry ST;Shumway M;Sirotkin K;Slotta D;Souvorov A;Starchenko G;Tatusova TA;Wagner L;Wang Y;John Wilbur W;Yaschenko E;Ye J
通讯作者：
Ye J