MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters

In this work, we present <italic>MPIGeneNet</italic>, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool <italic>RMTGeneNet</italic>, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. <italic>MPIGeneNet</italic> returns the same results as <italic> RMTGeneNet</italic> but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that <italic>MPIGeneNet</italic> is significantly faster than <italic>RMTGeneNet</italic>. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. The source code of <italic>MPIGeneNet</italic>, as well as a reference manual, are available at <uri> https://sourceforge.net/projects/mpigenenet/</uri>.

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