Total views : 313

Protein-protein Interaction Prediction using Variable Length Patterns with GPU

Affiliations

  • Department of computer Science Engineering, Lovely Professional University, Phagwara - 144411, Punjab, India

Abstract


Objectives: To design a new framework to efficiently parallelize the steps of VLASPD algorithm using a hybridized apriori and fp-growth on GPU; to implement the existing and proposed framework in CUDA;to improve the performance factors like computational time, memory and CPU utilization.Methods/Statistical Analysis: This paper proposes the acceleration of Protein-Protein Interactions (PPIs) prediction on Graphics Processing Units(GPUs). A GPU can provide more processing cores and computational power in the same cost as a CPU.Findings: The frequently occurring patterns in the protein sequences can be used for PPIs prediction.The moving of the approaches from fixed length to variable length lead to computational complexity but also is found to be advantageous.Applications/Improvements:Sequence biology is since being researched by various computer engineers, the GPUs can be employed for predicting various sequence interactions like DNA-Proteins, etc. Since the GPU runs the parallel code efficiently, the methodology can be further improved if efficiently parallelized.

Keywords

Parallel VLASPD, Protein Interaction Prediction, Protein Sequences, Variable Length Patterns.

Full Text:

 |  (PDF views: 271)

References


  • Luscombe NM, Greenbaum D, Gerstein M. What is bioinformatics? A proposed definition and overview of the field. Methods Information Medical. 2001; 40(4):346–58.
  • Kidera A, Konishi Y, Ooi T, Scheraga HA. Relation between sequence similarity and structural similarity in proteins. Role of important properties of amino acids. Journal of Protein Chemistry.1985; 4(5):265–97.
  • Pearson WR. Searching protein sequence libraries: Comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms. Genomics. 1991; 11(3):635–50.
  • Fogg CN, Kovats DE. Computational Biology: Moving into the future one click at a time. PLOS Computing Biology. 2015; 11(6).
  • Hu L, Chan K. Discovering variable-length patterns in protein sequences for protein-protein interaction prediction. IEEE Transaction on Nanobioscience. 2015; 14(4):409–16.
  • Luebke D, Humphreys G. How GPUs work. Computer (Long Beach Calif). 2007; 40(2):96–100.
  • Ben-Hur A, Noble WS. Kernel methods for predicting protein–protein interactions. Bioinformatics. 2005; 21(suppl 1):38–46.
  • Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K. Predicting protein–protein interactions based only on sequences information. Proceeding of National Acadamy Science. 2007; 104(11):4337–41.
  • Liu Y, Huang W, Johnson J, Vaidya S. GPU accelerated smith-waterman. Computational Science–ICCS 2006; 2006. p. 188–95.
  • Manavski SA, Valle G. CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment. BMC Bioinformatics. 2008; 9(2):1–9.
  • Striemer GM, Akoglu A. Sequence alignment with GPU: Performance and design challenges. 2009 IPDPS IEEE International Symposium on Parallel and Distributed Processing; 2009. p. 1–10.
  • Liu Y, Maskell DL, Schmidt B. CUDASW++: Optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units. BMC Research Notes. 2009; 2(1):73.
  • Ligowski Ł, Rudnicki W. An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases. 2009 IPDPS IEEE International Symposium on Parallel and Distributed Processing; 2009. p. 1–8.
  • Rudnicki WR, Jankowski A, Modzelewski A, Piotrowski A, Zadrożny A. The new SIMD implementation of the Smith-Waterman algorithm on Cell microprocessor. Fundam Informaticae. 2009; 96(1–2):181–94.
  • Farrar M. Striped Smith–Waterman speeds database searches six times over other SIMD implementations. Bioinformatics. 2007; 23(2):156–61.
  • Khajeh-Saeed A, Poole S, Perot JB. Acceleration of the Smith–Waterman algorithm using single and multiple graphics processors. Journal of Computational Physics. 2010; 229(11):4247–58.
  • Liu Y, Schmidt B, Maskell DL. CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions. BMC Research Notes. 2010; 3(1):93.
  • Blazewicz J, Frohmberg W, Kierzynka M, Pesch E, Wojciechowski P. Protein alignment algorithms with an efficient backtracking routine on multiple GPUs. BMC Bioinformatics. 2011; 12(181):1–17.
  • Hasan L, Kentie M, Al-Ars Z. GPU-accelerated protein sequence alignment. Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE; 2011. p. 2442–6.
  • Hains D, Cashero Z, Ottenberg M, Bohm W, Rajopadhye S. Improving CUDASW++, a parallelization of Smith-Waterman for CUDA enabled devices. IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW); 2011. p. 490–501.
  • Liu Y, Wirawan A, Schmidt B. CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions. BMC Bioinformatics. 2013; 14(1):117.
  • Lee ST, Lin CY, Hung CL. GPU-based cloud service for smith-waterman algorithm using frequency distance filtration scheme. Biomed Research International. 2013; 2013:8.
  • Liu Y, Hong Y, Lin C-Y, Hung C-L. Accelerating Smith-Waterman Alignment for protein database search using frequency distance filtration scheme based on CPU-GPU collaborative system. International Journal of Genomics. 2015; 2015:12.
  • Huang L, Wu C, Lai L, Li Y. Improving the mapping of Smith-Waterman sequence database searches onto CUDA-enabled GPUs. BioMed Research International. 2015; 2015:10.
  • Fang W, Lu M, Xiao X, He B, Luo Q. Frequent itemset mining on graphics processors. Proceedings of the fifth international workshop on data management on new hardware. ACM; 2009. p. 34–42.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.