Reverse engineering cellular networks
Reverse engineering cellular networks
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DOI：
10.1038/nprot.2006.106
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发表时间：
2006-01-01
期刊：
影响因子：
14.8
通讯作者：
Califano, Andrea
中科院分区：
文献类型：
--
作者：
Margolin, Adam A.;Wang, Kai;Califano, Andrea
我们描述了ARACNE算法的计算协议，这是一种利用微阵列表达谱数据识别基因产物之间转录相互作用的信息论方法。与其他算法类似，ARACNE通过识别基因产物之间的统计依赖性来预测基因之间的潜在功能关联，或未表征基因的新功能。然而，基于生化验证、文献检索和DNA结合位点富集分析，即使在复杂的哺乳动物网络中，ARACNE也被证明在识别真正的转录靶点方面是有效的。因此，我们设想ARACNE的预测，特别是在补充了先验知识或额外数据源的情况下，可以为蜂窝网络的进一步研究提供适当的假设。虽然本协议中的示例仅使用基因表达谱数据，但该算法的理论基础很容易扩展到各种其他高通量测量，例如通路特异性或全基因组蛋白质组学，microRNA和代谢组学数据。随着这些数据变得容易获得，我们期望ARACNE在阐明潜在的相互作用模型方面可能会越来越有用。对于包含类似10,000个探针的微阵列数据集，使用带有Pentium 4处理器的台式计算机，围绕单个探针重建网络只需几分钟。重建全基因组网络通常需要一个计算集群，特别是如果使用推荐的引导过程。
We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing similar to 10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.