CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm

International Journal of High Performance Computing Applications

Volumn 30, Issue 2, 2016, Pages 200-211

  Dang, Hoang Vu ^a   Schmidt, Bertil ^a   Hildebrandt, Andreas ^a   Tran, Tuan Tu ^a   Hildebrandt, Anna Katharina ^b  

^a JOHANNES GUTENBERG UNIVERSITY   (Germany)

^b MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

bioinformatics; clustering; CUDA; protein docking; protein structures

Indexed keywords

BIOINFORMATICS; COMPUTER GRAPHICS; COMPUTER GRAPHICS EQUIPMENT; GERMANIUM COMPOUNDS; GRAPHICS PROCESSING UNIT; HIERARCHICAL CLUSTERING; IMAGE CODING; PROGRAM PROCESSORS; PROTEINS;

BIOINFORMATICS WORKFLOWS; CLUSTERING; CUDA; PROTEIN DOCKING; PROTEIN STRUCTURES; ROOT MEAN SQUARE DEVIATIONS; STRUCTURE PREDICTION; THREE-DIMENSIONAL STRUCTURE;

CLUSTERING ALGORITHMS;

EID: 84964344032     PISSN: 10943420     EISSN: 17412846     Source Type: Journal    
DOI: 10.1177/1094342015597988     Document Type: Article

References (28)

1. Bell N, Garland M, (2012) Cusp: Generic Parallel algorithms for sparse matrix and graph computations. Available at: http://cusp-library.googlecode.com. (, 2015).
2. Benzécri J-P, (1982) Construction d'une classification ascendante hiérarchique par la recherche en chaîne des voisins réciproques. Cah l'analyse des données 7 (2): 209-218.
3. Berenger F, Shrestha R, Zhou Y, et al. (2012) Durandal: Fast exact clustering of protein decoys. Journal of Computational Chemistry 33 (4): 471-474.
4. Chang D, Kantardzic M, Ouyang M, (2009) Hierarchical clustering with CUDA/GPU. In: proceedings of ISCA PDCCS, pp. 7-12.
5. Chaudhury S, Berrondo M, Weitzner BD, et al. (2011) Benchmarking and analysis of protein docking performance in Rosetta v3.2. PLoS One 6: 8, e22477.
6. Chen Y-R, Hung CL, Lin Y-S, et al. (2012) Parallel UPGMA algorithm on graphics processing units using CUDA. In: 2012 IEEE 14th international conference on high performance computing and communication & 2012 IEEE 9th international conference on embedded software and systems (HPCC-ICESS), pp. 849-854.
7. Hildebrandt A, Dehof AK, Rurainski A, et al. (2010) BALL-biochemical algorithms library 1.3. BMC Bioinformatics 11: 531.
8. Hildebrandt AK, Dietzen M, Lengauer T, et al. (2014) Efficient computation of root mean square deviations under rigid transformations. Journal of Computational Chemistry 35: 765-771.
9. Hildebrandt AK, Stöckel D, Fischer NM, et al. (2015) Ballaxy: web services for structural bioinformatics. Bioinformatics 31 (1): 121-122.
10. Jamroz M, Kolinski A, (2013) ClusCo: Clustering and comparison of protein models ClusCo: Clustering and comparison of protein models. BMC Bioinformatics 14: 62.
11. Juan J, (1982) Programme de classification hiérarchique par l'algorithme de la recherche en chaîne des voisins réciproques. Cah l'analyse des données 7 (2): 219-225.
12. Kirk D, Hwu W-MW, (2013) Programming Massively Parallel Processors: A Hands-on Approach. 2nd ed. San Francisco, CA: Morgan Kaufmann.
13. Li S, Ng Y, (2010) Calibur: A tool for clustering large numbers of protein decoys. BMC Bioinformatics 11: 25.
14. Liu Y, Schmidt B, Maskell DL, (2009) Parallel reconstruction of neighbor-joining trees for large multiple sequence alignments using CUDA. In: IPDPS 2009-proceedings of the 2009 IEEE international parallel and distributed processing symposium.
15. Moll A, Hildebrandt A, Lenhof H-P, et al. (2005) BALLView: An object-oriented molecular visualization and modeling framework. Journal of Computer-Aided Molecular Design 11: 791-800.
16. Moll A, Hildebrandt A, Lenhof H-P, et al. (2006) BALLView: A tool for research and education in molecular modeling. Bioinformatics 22 (3): 365-366.
17. Murtagh F, (1984) Complexities of hierarchic clustering algorithms: State of the art. Computational Statistics Quarterly 1 (2): 101-113.
18. Nickolls J, Buck I, Garland M, et al. (2008) Scalable parallel programming with CUDA. Queue 6: 40.
19. NVIDIA CUDA, (2011) C Programming Guide. Changes: 173. Available at: http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA-C-Programming-Guide.pdf. (, 2015).
20. Shalom S, Dash M, (2013) Efficient Partitioning based hierarchical agglomerative clustering using graphics accelerations with CUDA. International Journal of Atrificial Intelleigence and Applications 4 (2): 13-33.
21. Sørensen T, (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter 5: 1-34.
22. Ward JHJ, (1963) Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58: 236-244.
23. Wu S, Skolnick J, Zhang Y, (2007) Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biology 5: 17.
24. Zhang Y, Skolnick J, (2004a) SPICKER: A clustering approach to identify near-native protein folds. Journal of Computational Chemistry 25: 865-871.
25. Zhang Y, Skolnick J, (2004b) Scoring function for automated assessment of protein structure template quality. Proteins: Structure, Functions and Genetics 57: 702-710.
26. Zhao Y, Sheong FK, Sun J, et al. (2013) A fast parallel clustering algorithm for molecular simulation trajectories. Journal of Computational Chemistry 34: 95-104.
27. Zheng R, Zhang Q, Jin H, (2012). Parallelization Mechanisms of neighbor-joining for CUDA enabled devices. In: proceedings of ChinaGrid'12, pp. 182-188.
28. Zhou J, Wishart DS, (2013) An improved method to detect correct protein folds using partial clustering. BMC Bioinformatics 14: 11.