Parallelization of ARACNe, an Algorithm for the Reconstruction of Gene Regulatory Networks

Uxía Casal,María J Martín,Jorge González-Domínguez

doi:10.3390/proceedings2019021025

Abstract

Gene regulatory networks are graphical representations of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression. There are different computational approaches for the reverse engineering of these networks. Most of them require all gene-gene evaluations using different mathematical methods such as Pearson/Spearman correlation, Mutual Information or topology patterns, among others. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) is one of the most effective and widely used tools to reconstruct gene regulatory networks. However, the high computational cost of ARACNe prevents its use over large biologic datasets. In this work, we present a hybrid MPI/OpenMP parallel implementation of ARACNe to accelerate its execution on multi-core clusters, obtaining a speedup of 430.46 using as input a dataset with 41,100 genes and 108 samples and 32 nodes (each of them with 24 cores).

Highlights

A Gene Regulatory Network (GRN) is a network that has been inferred from gene expression data, explaining how a collection of molecular regulators interact with each other and with other substances in the cell, to govern the gene expression levels and it is crucial for understanding normal cell physiology and complex pathological phenotypes
The approach to decrease the runtime consists in parallelizing the code using MPI routines and OpenMP directives so that the new implementation increases performance in clusters of multi-core nodes, a type of systems that nowadays are widely used by biologists as each day it is easier to have access to them through a supercomputing center
We have developed a hybrid OpenMP/MPI parallel implementation that works with the same input/output formats and guarantees the same results as the original tool but at significantly lower runtime

Summary

Introduction

A Gene Regulatory Network (GRN) is a network that has been inferred from gene expression data, explaining how a collection of molecular regulators interact with each other and with other substances in the cell, to govern the gene expression levels and it is crucial for understanding normal cell physiology and complex pathological phenotypes. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) [2] represents one of the most used methods in the scientific community to reconstruct GRNs. It is based on information theory and a network pruning process called Data Processing Inequality (DPI) theorem, which is used to infer direct regulatory relationships among transcriptional factors and their genes. Its main drawback is its quadratic complexity with the number of genes, resulting in a high computational cost that prevents its use for large datasets.

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