Year: 2017
Type: article
Abstract: BACKGROUND: The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) represents one of the most effective tools to reconstruct gene regulatory networks from large-scale molecular profile datasets. However, previous implementations require intensive computing resources and, in some cases, restrict the number of samples that can be used. These issues can be addressed elegantly in a GPU computing framework, where repeated mathematical computation can be done efficiently, but requires extensive redesign to apply parallel computing techniques to the original serial algorithm, involving detailed optimization efforts based on a deep understanding of both hardware and software architecture. RESULT: Here, we present an accelerated parallel implementation of ARACNE (GPU-ARACNE). By taking advantage of multi-level parallelism and the Compute Unified Device Architecture (CUDA) parallel kernel-call library, GPU-ARACNE successfully parallelizes a serial algorithm and simplifies the user experience from multi-step operations to one step. Using public datasets on comparable hardware configurations, we showed that GPU-ARACNE is faster than previous implementations and is able to reconstruct equally valid gene regulatory networks. CONCLUSION: Given that previous versions of ARACNE are extremely resource demanding, either in computational time or in hardware investment, GPU-ARACNE is remarkably valuable for researchers who need to build complex regulatory networks from large expression datasets, but with limited budget on computational resources. In addition, our GPU-centered optimization of adaptive partitioning for Mutual Information (MI) estimation provides lessons that are applicable to other domains.
Source: BMC Systems Biology
Authors: Jing He, Zhou Zhou, Michael Reed, Andrea Califano
Institution: Columbia University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,290
Awards: Storage System for High Performance Computing, Centers for Cancer Systems Therapeutics (CaST), Systems Biology of Tumor Progression and Drug Resistance, High-performance compute cluster for biomedical computing, Systematic Identification and Pharmacological Targeting of Tumor Dependencies for Precision Cancer Medicine, Predicting Cancer Cell Response to Endogenous and Exogenous Perturbations at the Single Cell Level
OpenAlex ID: W2757003517