论文信息 - Optimizing the PCIT algorithm on stampede's Xeon and Xeon Phi processors for faster discovery of biological networks

Optimizing the PCIT algorithm on stampede's Xeon and Xeon Phi processors for faster discovery of biological networks

The PCIT method is an important technique for detecting interactions between networks. The PCIT algorithm has been used in the biological context to infer complex regulatory mechanisms and interactions in genetic networks, in genome wide association studies, and in other similar problems. In this work, the PCIT algorithm is re-implemented with exemplary parallel, vector, I/O, memory and instruction optimizations for today's multi- and many-core architectures. The evolution and performance of the new code targets the processor architectures of the Stampede supercomputer, but will also benefit other architectures. The Stampede system consists of an Intel Xeon E5 processor base system with an innovative component comprised of Intel Xeon Phi Coprocessors. Optimized results and an analysis are presented for both the Xeon and the Xeon Phi.

[1] Antonio Reverter,et al. A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation , 2009, PLoS Comput. Biol..

[2] Antonio Reverter,et al. Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks , 2009, PloS one.

[3] Antonio Reverter,et al. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks , 2008, Bioinform..

[4] Antonio Reverter,et al. PCIT: an R package for weighted gene co-expression networks based on partial correlation and information theory approaches , 2010, Bioinform..

[5] Brian P. Dalrymple,et al. Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data , 2010, Bioinform..

[6] Shivashankar H. Nagaraj,et al. Gene network analyses of first service conception in Brangus heifers: use of genome and trait associations, hypothalamic-transcriptome information, and transcription factors. , 2012, Journal of animal science.

[7] A Reverter,et al. Breeding and Genetics Symposium: building single nucleotide polymorphism-derived gene regulatory networks: Towards functional genomewide association studies. , 2013, Journal of animal science.

[8] Shivashankar H. Nagaraj,et al. Association weight matrix for the genetic dissection of puberty in beef cattle , 2010, Proceedings of the National Academy of Sciences.