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Accelerated parallel algorithm for gene network reverse engineering

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Τίτλος: Accelerated parallel algorithm for gene network reverse engineering
Συγγραφείς: He, Jing, Zhou, Zhou, Reed, Michael K., Califano, Andrea
Έτος έκδοσης: 2017
Συλλογή: Columbia University: Academic Commons
Θεματικοί όροι: Parallel programming (Computer science), Gene regulatory networks, Gene expression--Data processing, Systems biology
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://doi.org/10.7916/D8DZ0MZD
DOI: 10.7916/D8DZ0MZD
Διαθεσιμότητα: https://doi.org/10.7916/D8DZ0MZD
Αριθμός Καταχώρησης: edsbas.364A3FEE
Βάση Δεδομένων: BASE
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  Data: Accelerated parallel algorithm for gene network reverse engineering
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  Data: <searchLink fieldCode="AR" term="%22He%2C+Jing%22">He, Jing</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Zhou%22">Zhou, Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Reed%2C+Michael+K%2E%22">Reed, Michael K.</searchLink><br /><searchLink fieldCode="AR" term="%22Califano%2C+Andrea%22">Califano, Andrea</searchLink>
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  Data: 2017
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  Data: Columbia University: Academic Commons
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  Data: <searchLink fieldCode="DE" term="%22Parallel+programming+%28Computer+science%29%22">Parallel programming (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+regulatory+networks%22">Gene regulatory networks</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+expression--Data+processing%22">Gene expression--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+biology%22">Systems biology</searchLink>
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  Data: 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.
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