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Author(s)

     A.Schoenrock, F.Dehne, J.R.Green, A.Golshani, S.Pitre

Title

     MP-PIPE: A Massively Parallel Protein-Protein Interaction Prediction Engine

Publication Name

     Conf. on Supercomputing (ICS '11)

Publication Type

     
Publisher
     ACM Dig. Library
Place
     
Editor(s)
     
Number
     
School
     
Volume
     
Chapter
     
Pages
      327-337
Other Publication Information
     
Publication Year
     2011
Abstract
     Interactions among proteins are essential to many biological functions in living cells but experimentally detected interactions represent only a small fraction of the real interaction network. Computational protein interaction prediction methods have become important to augment the experimental methods; in particular sequence based prediction methods that do not require additional data such as homologous sequences or 3D structure information which are often not available. Our Protein Interaction Prediction Engine (PIPE) method falls into this category. Park has recently compared PIPE with the other competing methods and concluded that our method“significantly outperforms the others in terms of recall-precision across both the yeast and human data”. Here, we present MP-PIPE, a new massively parallel PIPE implementation for large scale, high throughput protein interaction prediction. MP-PIPE enabled us to perform the first ever complete scan of the entire human protein interaction network; a massively parallel computational experiment which took three months of full time 24/7 computation on a dedicated SUN UltraSparc T2+ based cluster with 50 nodes, 800 processor cores and 6,400 hardware supported threads. The implications for the understanding of human cell function will be significant as biologists are starting to analyze the 130,470 new protein interactions and possible new pathways in Human cells predicted by MP-PIPE.
Keywords
     Massively Parallel Application, Computational Biology, Protein Interaction Prediction, High Throughput.
Website
     http://people.scs.carleton.ca/~dehne/publications/2-90.pdf
Paper Status
     Accepted
DOI / Publication ID
     



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