Parallel Framework for Inferring Genome-Scale Gene Regulatory Networks

Abstract

Genome-scale network inference is essential to understand comprehensive interaction patterns. Current methods are limited to the reconstruction of small to moderate-size networks. The most obvious alternative is to propose a novel method or alter existing methods that may leverage parallel computing paradigms. Very few attempts also have been made to re-engineer existing methods by executing selective iterative steps concurrently. We propose a generic framework in this paper that leverages parallel computing without re-engineering the original methods. The proposed framework uses state-of-the-art methods as a black box to infer sub-networks of the segmented data matrix. A simple merger was designed based on preferential attachment to generate the global network by merging the sub-networks. Fifteen (15) inference methods were considered for experimentation. Qualitative and speedup analysis was carried out using DREAM challenge networks. The proposed framework was implemented on all the 15 inference methods using large expression matrices. The results were auspicious as we could infer large networks in reasonable time without compromising the qualitative aspects of the original (serial) algorithm. CLR, the top performer, was then used to infer the network from the expression profiles of an Alzheimers disease (AD) affected mouse model consisting of 45,101 genes. We have also highlighted few hub genes from the network that are functionally related to various diseases.