Department of Statistical Sciences, University of Padova, 35121 Padova, Italy.
Bioinformatics. 2011 Jul 1;27(13):1876-7. doi: 10.1093/bioinformatics/btr274. Epub 2011 Apr 29.
Inferring large transcriptional networks using mutual information has been shown to be effective in several experimental setup. Unfortunately, this approach has two main drawbacks: (i) several mutual information estimators are prone to biases and (ii) available software still has large computational costs when processing thousand of genes.
Here, we present parmigene (PARallel Mutual Information estimation for GEne NEtwork reconstruction), an R package that tries to fill the above gaps. It implements a mutual information estimator based on k-nearest neighbor distances that is minimally biased with respect to the other methods and uses a parallel computing paradigm to reconstruct gene regulatory networks. We test parmigene on in silico and real data. We show that parmigene gives more precise results than existing softwares with strikingly less computational costs.
The parmigene package is available on the CRAN network at http://cran.r-project.org/web/packages/.
使用互信息推断大型转录网络已在几种实验设置中被证明是有效的。不幸的是，这种方法有两个主要缺点：（i）几种互信息估计器容易出现偏差，（ii）可用的软件在处理数千个基因时仍然具有较大的计算成本。
在这里，我们提出了 parmigene（用于基因网络重建的并行互信息估计），这是一个 R 包，旨在填补上述空白。它实现了一种基于最近邻距离的互信息估计器，相对于其他方法具有最小的偏差，并使用并行计算范例来重建基因调控网络。我们在模拟和真实数据上测试了 parmigene。我们表明，与现有的软件相比，parmigene 以惊人的低计算成本给出了更精确的结果。
parmigene 软件包可在 CRAN 网络上获得，网址为 http://cran.r-project.org/web/packages/。
