Heterogeneous cloud framework for big data genome sequencing

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 12, Issue 1, 2015, Pages 166-178

  Wang, Chao ^a   Li, Xi ^a   Chen, Peng ^a   Wang, Aili ^a   Zhou, Xuehai ^a   Yu, Hong ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b INSTITUTE OF GENETICS AND DEVELOPMENTAL BIOLOGY   (China)

Author keywords

FPGA; genome sequencing; mapping; reconfigurable hardware; Short reads

Indexed keywords

COMPUTER HARDWARE; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); GENES; HARDWARE; MAPPING; QUALITY CONTROL; RECONFIGURABLE HARDWARE;

ACCELERATION APPROACH; FPGA-BASED ACCELERATIONS; GENOME SEQUENCING; HARDWARE ACCELERATION; MAP-REDUCE PROGRAMMING; NEXT-GENERATION SEQUENCING; PROPOSED ARCHITECTURES; SHORT READS;

BIG DATA;

ALGORITHM; CHROMOSOMAL MAPPING; CLOUD COMPUTING; DNA SEQUENCE; GENETIC DATABASE; GENETICS; GENOMICS; HUMAN; HUMAN GENOME; PROCEDURES; SEQUENCE ALIGNMENT;

ALGORITHMS; CHROMOSOME MAPPING; CLOUD COMPUTING; DATABASES, GENETIC; GENOME, HUMAN; GENOMICS; HUMANS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, DNA;

EID: 84923226117     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2014.2351800     Document Type: Article

References (47)

1. C. B. Olson, M. Kim, C. Clauson, B. Kogon, C. Ebeling, S. Hauck, and W. L. Ruzzo, "Hardware acceleration of short read mapping," in Proc. IEEE 20th Annu. Int. Symp. Field-Programmable Custom Comput. Mach., 2012, pp. 161-168.
2. B. Langmead, C. Trapnell, M. Pop, and S. L. Salzberg, "Ultrafast and memory-efficient alignment of short DNA sequences to the human genome," Genome Biol., vol. 10, no. 3, p. R25, 2009.
3. H. Li and R. Durbin, "Fast and accurate short read alignment with Burrows-Wheeler transform," Bioinformatics, vol. 25, no. 14, pp. 1754-1760, 2009.
4. N. Homer, B. Merriman, and S. F. Nelson, "BFAST: An alignment tool for large scale genome resequencing," PLoS ONE, vol. 4, no. 11, p. e7767, 2009.
5. M. C. Schatz, "CloudBurst: Highly sensitive read mapping with MapReduce," Bioinformatics, vol. 25, no. 11, pp. 1363-1369, 2009.
6. O. Knodel, T. B. Preusser, and R. G. Spallek, "Next-generation massively parallel short-read mapping on FPGAs," in Proc. IEEE Int. Conf. Appl.-Specific Syst., Archit. Processors, 2011, pp. 195-201.
7. E. Fernandez, W. Najjar, E. Harris, and S. Lonardi, "Exploration of short reads genome mapping in hardware," in Proc. Int. Conf. Field Programmable Logic Appl., 2010, pp. 360-363.
8. FCCM 2011 Top 10 Predictions for FCCMs in 2016. (2011). [Online] Available: http://www.fccm.org/2012/Previous.html
9. D. Jeffrey and G. Sanjay, "MapReduce: Simplified data processing on large clusters," Commun. ACM, vol. 51, no. 1, pp. 107-113, 2008.
10. Y. Shan, B. Wang, J. Yan, Y. Wang, N. Xu, and H. Yang, "FPMR: MapReduce framework on FPGA," in Proc. 18th Annu. ACM/SIGDA Int. Symp. Field Programmable Gate Arrays, Monterey, CA, USA, , 2010, pp. 93-102.
11. M. Burrows and D. J. Wheeler, A Block-Sorting Lossless Data Compression Algorithm. Palo Alto, CA, USA: Digital Equipment Corporation, 1994, p. 124.
12. P. Ferragina and G. Manzini, "Opportunistic data structures with applications," in Proc. 41st Annu. Symp. Found. Comput. Sci., 2000, pp. 390-398.
13. R. A. Baeza-Yates and C. H. Perleberg, "Fast and practical approximate string matching," in Proc. 3rd Annu. Symp. Combinatorial Pattern Matching. 1992.
14. T. F. Smith and M. S. Waterman, "Identification of common molecular subsequences," J. Mol. Biol., vol. 147, no. 1, pp. 195-197, 1981.
15. A. D. Smith, Z. Xuan, and M. Q. Zhang, "Using quality scores and longer reads improves accuracy of Solexa read mapping," BMC Bioinformat., vol. 9, no. 128, pp. 1471-2105, 2008.
16. S. A Manavski and G. Valle, "A compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment," BMC Bioinformat., vol. 9, no. Suppl. 2, p. S10, 2008.
17. M. C Schatz, C. Trapnell, A. L Delcher, and A. Varshney, "Highthroughput sequence alignment using graphics processing units," BMC Bioinformat., vol. 8, p. 474, 2007.
18. C. Trapnell and M. C. Schatz, "Optimizing data intensive GPGPU computations for DNA sequence alignment," Parallel Comput., vol. 35, nos. 8/9, pp. 429-440, 2009.
19. Y. Liu, B. Schmidt, and D. L. Maskell, "CUDASW++2.0: Enhanced Smith-Waterman protein database search on CUDAenabled GPUs based on SIMT and virtualized SIMD abstractions," BMC Res. Notes, vol. 3, p. 93, 2010.
20. T. B. Preusser, O. Knodel, and R. G. Spallek, "Short-read mapping by a systolic custom FPGA computation," in Proc. IEEE 20th Annu. Int. Symp. Field-Programmable Custom Comput. Mach., 2012, pp. 169-176.
21. W. Tang, W. Wang, B. Duan, C. Zhang, G. Tan, P. Zhang, and N. Sun, "Accelerating millions of short reads mapping on a heterogeneous architecture with FPGA accelerator," in Proc. IEEE 20th Annu. Int. Symp. Field-Programmable Custom Comput. Mach., 2012, pp. 184-187.
22. A. Mahram and M. C. Herbordt, "FMSA: FPGA-Accelerated clustaIW-based multiple sequence alignment through pipelined prefiltering," in Proc. IEEE 20th Annu. Int. Symp. Field-Programmable Custom Comput., Mach., 2012, pp. 177-183.
23. S. Lloyd and Q. O Snell, "Accelerated large-scale multiple sequence alignment," BMC Bioinformat., vol. 12, p. 466, 2011.
24. Y. Chen, B. Schmidt, and D. L. Maskell, "Accelerating short read mapping on an FPGA (abstract only)," in Proc. ACM/SIGDA Int. Symp. Field Programmable Gate Arrays, Monterey, California CA, USA, 2012, p. 265.
25. P. Zhang, G. Tan, and G. R. Gao, "Implementation of the Smith-Waterman algorithm on a reconfigurable supercomputing platform," in Proc. 1st Int. Workshop High-Performance Reconfigurable Comput. Technol. Appl., Reno, NV, USA, , 2007, pp. 39-48.
26. Q. Wang and V. K. Prasanna, "Multi-core architecture on FPGA for large dictionary string matching," in Proc. 17th IEEE Symp. Field Programmable Custom Comput. Mach., 2009, pp. 96-103.
27. I. Sourdis and D. Pnevmatikatos, "Fast, large-scale string match for a 10 Gbps FPGA-based network intrusion detection system," in Proc. 13th Int. Conf. Field Programmable Logic Appl., 2003, pp. 880-889.
28. T. V. Court and M. C. Herbordt, "Families of FPGA-based algorithms for approximate string matching," in Proc. 15th IEEE Int. Conf. Appl.-Specific Syst., Archit. Process., 2004, pp. 354-364.
29. M. Aldwairi, T. Conte, and P. Franzon, "Configurable string matching hardware for speeding up intrusion detection," ACM SIGARCH Comput. Archit. News, vol. 33, no. 1, pp. 99-107, 2005.
30. D. Pao, W. Lin, and B. Liu, "Pipelined architecture for multi-string matching," IEEE Comput. Archit. Lett., vol. 7, no. 2, pp. 33-36, Jul.-Dec. 2008.
31. Z. K. Baker and V. K. Prasanna, "A computationally efficient engine for flexible intrusion detection," IEEE Trans. Very Large Scale Integr. Syst., vol. 13, no. 10, pp. 1179-1189, Oct. 2005.
32. Z. K. Baker and V. K. Prasanna, "Automatic synthesis of efficient intrusion detection systems on FPGAs," IEEE Trans. Dependable Secure Comput., vol. 3, no. 4, pp. 289-300, Oct.-Nov. 2006.
33. V. Dimopoulos, I. Papaefstathiou, and D. Pnevmatikatos, "A memory-efficient reconfigurable aho-corasick FSM implementation for intrusion detection systems," in Proc. Int. Conf. Embedded Comput. Syst.: Archit., Model. Simul., 2007, pp. 186-193.
34. A. V. Aho and M. J. Corasick, "Efficient string matching: An aid to bibliographic search," Commun. ACM, vol. 18, no. 6, pp. 333-340, 1975.
35. D. Pao, W. Lin, and B. Liu, "A memory-efficient pipelined implementation of the aho-corasick string-matching algorithm," ACM Trans. Archit. Code Optim., vol. 7, no. 2, pp. 1-27, 2010.
36. C. Wang, X. Li, X. Zhou, J. Martin, and R. C. C. Cheung, "Genome sequencing using mapreduce on FPGA with multiple hardware accelerators," in Proc. ACM/SIGDA Int. Symp. Field Programmable Gate Arrays, 2013, p. 266.
37. C. Wang, X. Li, X. Zhou, Y. Chen, and R. C. C. Cheung, "Big data genome sequencing on Zynq based clusters in 2014," in Proc. ACM/SIGDA Int. Symp. Field-Programmable Gate Arrays, 2014, p. 247.
38. P. Chen, C. Wang, X. Li, and X. Zhou, "Accelerating the next generation long read mapping with the FPGA-based system," IEEE/ACM Trans. Comput. Biol. Bioinformat., vol. PP, no. 99, p. 1, 2014.
39. P. Chen, C. Wang, X. Li, and X. Zhou, "Hardware acceleration for the banded Smith-Waterman algorithm with the cycled systolic array, in Proc. Int. Conf. Field-Programmable Technol., 2013, pp. 480-481.
40. D. Dai, X. Li, C. Wang, and X. Zhou, "Cloud based short read mapping service," in Proc. IEEE Int. Conf. Cluster Comput., 2012, pp. 601-604.
41. Apache. HBase-Apache HBase Home. (2014). [Online]. Available: http://hbase.apache.org/
42. Apache. Hadoop. (2014). [Online]. Available: http://hadoop. apache.org/
43. HDFS Architecture Guide. (2014). [Online]. Available: http://hadoop.apache.org/docs/r1.2.1/hdfs-design.html
44. H. Li and R. Durbin, "Fast and accurate long-read alignment with Burrows-Wheeler transform," Bioinformatics, vol. 26, no. 5, pp. 589-595, 2010.
45. H. Li, J. Ruan, and R. Durbin, "Mapping short DNA sequencing reads and calling variants using mapping quality scores," Genome Res., vol. 18, no. 11, pp. 1851-1858, 2008.
46. B. Harris, A. C. Jacob, J. M. Lancaster, J. Buhler, and R. D. Chamberlain, "A banded smith-waterman FPGA accelerator for mercury BLASTP," in Proc. Int. Conf. Field Programmable Logic Appl., 2007, pp. 765-769.
47. X. Guo, H. Wang, and V. Devabhaktuni, "A systolic array-based FPGA parallel architecture for the BLAST algorithm," ISRN Bioinformat., vol. 2012, p. 11, Article ID 195658, 2012.