Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel®Xeon Phi™ Coprocessor

Abstract

Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

BugMat and FindNeighbour: command line and server applications for investigating bacterial relatedness

“…It splits the workload across cores, but does not support distribution of work across multiple computational nodes [ 12 ]. Secondly, it is presently restricted to CPU-based architectures supporting OpenMP, including the emerging Intel Xeon Phi [ 13 ] architecture; however, similar sharding of tasks across multiple computational cores can be achieved using graphical processing units and CUDA. Thirdly, by design it builds matrices in memory.…”

Dependency of the Cancer-Specific Transcriptional Regulation Circuitry on the Promoter DNA Methylome

“…In the present study, for finding specific types of regulation in the usually multiplex and non-linear regulatory relationships, we used the concepts of mutual information and cMI to assess the associations between multiple factors. We adopted the Kraskov approach for estimation of the mutual information, which has been widely used in multiple areas (Frenzel and Pompe, 2007;Misra et al, 2015;Reshef et al, 2011). Because DNA copy number is also a major defining factor of the gene expression, we designed a 2-step procedure ( Figure 1A).…”

Parallel Pairwise Correlation Computation on Intel Xeon Phi Clusters

“…The offload model relies on compiler pragmas/directives to offload highly-parallel parts of an application to the Phi, while the native model treats a Phi as a symmetric multi-processing computer. As of today, Phis have been used to accelerate important computational problems in diverse research fields such as bioinformatics [13] [14] and machine learning [15] [16].…”