ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information.
ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information.
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DOI：
10.1093/bioinformatics/btw216
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发表时间：
2016-07-15
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影响因子：
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通讯作者：
Califano A
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文献类型：
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作者：
Lachmann A;Giorgi FM;Lopez G;Califano A
摘要：从大规模分子图谱数据集准确重建基因调控网络是系统生物学面临的重大挑战之一。精确蜂窝网络重构算法(ARACNe)是实现这一目标的最有效工具之一。然而，初始固定带宽(FB)实现效率低下，并且不能处理提供很大程度上不均匀的概率密度空间覆盖的样本集。在这里，我们提出了一种全新的算法实现，基于用于估计相互信息的自适应划分策略(AP)。新的AP实现(ARACNe-AP)在保持原始算法的互信息估计器和网络推理精度的同时，实现了比以前的方法显著提高的计算性能(平均200倍)。鉴于以前版本的ARACNe要求极其苛刻，新版本的算法将允许即使拥有有限计算资源的研究人员也可以从数百个基因表达谱构建复杂的调控网络。可用性和实现：可在Sourceforge(http://sourceforge.net/projects/aracne-ap).)上免费获得ARACNe的JAVA跨平台命令行可执行文件，以及所有源代码和详细的使用指南需要Java版本8或更高版本。联系方式：califano@c2b2.belbia.edu补充信息：补充数据可在BioInformation Online上获得。
Summary: The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space. Here, we present a completely new implementation of the algorithm, based on an Adaptive Partitioning strategy (AP) for estimating the Mutual Information. The new AP implementation (ARACNe-AP) achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the Mutual Information estimator and the Network inference accuracy of the original algorithm. Given that the previous version of ARACNe is extremely demanding, the new version of the algorithm will allow even researchers with modest computational resources to build complex regulatory networks from hundreds of gene expression profiles. Availability and Implementation: A JAVA cross-platform command line executable of ARACNe, together with all source code and a detailed usage guide are freely available on Sourceforge (http://sourceforge.net/projects/aracne-ap). JAVA version 8 or higher is required. Contact: califano@c2b2.columbia.edu Supplementary information: Supplementary data are available at Bioinformatics online.