A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer's disease network.

Abstract

The inference of large-scale gene regulatory networks is essential for understanding comprehensive interactions among genes. Most existing methods are limited to reconstructing networks with a few hundred nodes. Therefore, parallel computing paradigms must be leveraged to construct large networks. We propose a generic parallel framework that enables any existing method, without re-engineering, to infer large networks in parallel, guaranteeing quality output. The framework is tested on 15 inference methods (not limited to) employing in silico benchmarks and real-world large expression matrices, followed by qualitative and speedup assessment. The framework does not compromise the quality of the base serial inference method. We rank the candidate methods and use the top-performing method to infer an Alzheimer's Disease (AD) affected network from large expression profiles of a triple transgenic mouse model consisting of 45,101 genes. The resultant network is further explored to obtain hub genes that emerge functionally related to the disease. We partition the network into 41 modules and conduct pathway enrichment analysis, revealing that a good number of participating genes are collectively responsible for several brain disorders, including AD. Finally, we extract the interactions of a few known AD genes and observe that they are periphery genes connected to the network's hub genes. Availability: The R implementation of the framework is downloadable from https://github.com/Netralab/GenericParallelFramework.