Characterizing deep sequencing analytics using BFAST: Towards a scalable distributed architecture for next-generation sequencing data

ECMLS'11 - Proceedings of the 2nd International Workshop on Emerging Computational Methods for the Life Sciences

Volumn , Issue , 2011, Pages 23-31

  Kim, Joohyun ^a   Maddineni, Sharath ^a   Jha, Shantenu ^a  

^a LOUISIANA STATE UNIVERSITY   (United States)

Author keywords

BFAST; Burkerholderia glumae; Distributed computing; Genome sequence alignment; Human genome; Pilot job abstraction; Runtime environment; Simple API for grid applications (SAGA)

Indexed keywords

BFAST; BURKERHOLDERIA GLUMAE; GENOME SEQUENCE ALIGNMENT; GRID APPLICATIONS; HUMAN GENOMES; PILOT-JOB ABSTRACTION; RUNTIME ENVIRONMENTS;

COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; MAPPING;

GENE ENCODING;

EID: 79960954425     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1996023.1996027     Document Type: Conference Paper

References (42)

1. submitted to Teragrid 2011 Conference.
2. http://www.loni.org.
3. http://cyd01.cct.lsu.edu/dare-rfold, http://cyd01.cct.lsu.edu/dare-dock, http://cyder.cct.lsu.edu/dare-ngs.
4. P. P. Amaral, M. E. Dinger, T. R. Mercer, and J. S. Mattick. The Eukaryotic Genome as an RNA Machine. Science, 319:1787-1789, 2008. (Pubitemid 351451554)
5. D. Baek, J. Villen, C. Shin, F. D. Camargo, S. P. Gygi, and D. P. Bartel. The impact of microRNAs on protein output. Nature, 455:64-71, 2008.
6. A. Bateman and J. Quackenbush. Editorial -Bioinformatics for Next Generation Sequencing. Bioinformatics, 25:429, 2009.
7. E. Birney and et al. Identification and analysis of functional elements in 1 % of the human genome by the ENCODE pilot project. Nature, 447(7):799-816, 2007. (Pubitemid 46920138)
8. F. F. Costa. Non-coding RNAs and new opportunities for the private sector. Drug Discovery Today, 14:446-452, 2009.
9. R. A. Farrer, E. Kemen, J. D. G. Jones, and D. J. Studholme. De novo assembly of the Pseudomonas syringae pv. syringae B728a genome using Illumina/Solexa short sequence reads. FEMS Microbial Lett., 291:103-111, 2008.
10. G. C. Fox. Futuregrid and applications. 12 2009.
11. Y. Gilad, J. K. Pritchard, and K. Thornton. Characterizing natural variation using next-generation sequencing technologies. Trends in Genetics, 25(10):463-471, 2009.
12. J. Goecks, A. Nekrutenko, J. Taylor, and et al. A Comprehensive Approach for Supporting Accessible, Reproducible, and Transparent Computational Research in the Life Sciences. Genome Biol., 11(8):R86, 2010.
13. B. D. Halligan, J. F. Geiger, A. K. Vallejos, A. S. Greene, and S. N. Twigger. Low cost, scalable proteomics data analysis using amazonâ aws cloud computing services and open source search algorithms. Journal of Proteome Research, 8(6):3148-3153, 2009. PMID: 19358578.
14. D. Hernandez, P. Francois, L. Farinelli, M. Osteras, and J. Schrenzel. De novo bacterial genome sequencing: Millions of very short reads assembled on a desktop computer. Genome Res., 18:802-809, 2008. (Pubitemid 351645070)
15. N. Homer, B. Merriman, and S. F. Nelson. BFAST : An alignment tool for large scale genome resequencing. PLoS One, 4(11):e7767, 2009.
16. N. Homer, B. Merriman, and S. F. Nelson. Local alignment of two-base encoded DNA sequence. BMC Bioinformatics, 10(1):175, 2009.
17. S. Jha, Y. El-Khamra, and J. Kim. Developing Scientific Applications with Loosely-Coupled Sub-tasks. In Proceedings of the 9th International Conference on Computational Science: Part I, page 650. Springer, 2009.
18. S. Jha and et al. Developing Scientific Applications with Loosely-Coupled Sub-tasks. In Lecture Notes in Computer Science, pages 641-650. ICCS, 2009.
19. J. Kim, F. Francis, and J. H. Ham. Comparative analyses of the whole-genomes of two Burkhoderia glumae strains from different geographic origins. to be submitted.
20. J. Kim, W. Huang, S. Maddineni, F. Aboul-ela, and S. Jha. Exploring the RNA Folding Energy Landscape Using Scalable Distributed Cyber infrastructure. In ACM International Symposium on High Performance Distributed Computing (HPDC2010), June 2010.
21. B. Langmead and et al. Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol., 11(8):R83, 2010.
22. B. Langmead, M. C. Schatz, J. Lin, M. Pop, and S. L. Salzberg. Searching for SNPs with cloud computing. Genome Biol., 19(11):R134, 2009.
23. H. Li, B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, N. Home, G. Marth, G. Abecasis, R. Durbin, and . G. P. D. P. Subgroup. The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25:2078-2079, 2009.
24. A. Luckow, S. Jha, J. Kim, A. Merzky, and B. Schnor. Adaptive distributed replica-exchange simulations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1897):2595-2606, 2009.
25. A. Luckow, L. Lacinski, and S. Jha. Saga bigjob: An extensible and interoperable pilot-job abstraction for distributed applications and systems. In The 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2010.
26. A. C. M. Luyf, B. D. C. van Schaik, M. de Vries, F. Baas, A. H. C. van Kampen, and S. D. Olabarriaga. Initial steps towards a production platform for DNA sequence analysis on the grid. BMC Bioinformatics, 11:598, 2010.
27. E. R. Mardis. Next-Generation DNA Sequencing Methods. Annu. Rev. Genomics Hum. Genet., 9:387-402, 2008.
28. E. R. Mardis. The impact of next-generation sequencing technology on genetics. Trends in Genetics, 24(3):133-141, 2008.
29. A. Matsunaga, M. Tsugawa, and J. Fortes. CloudBlast: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications. Fourth IEEE Conference on eScience 2008, 2009.
30. A. McKenna, M. Hanna, E. Banks, and et al. analyzing next-generation DNA sequencing data The Genome Analysis Toolkit: A MapReduce framework for . Genome Res., 20:1297-1303, 2010.
31. M. L. Metzker. Sequencing technologies - the next generation. Nat. Rev. Genet., 11(1):31-46, 2010.
32. A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, and B. Wold. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods, 5(7):621-628, 2008. (Pubitemid 351911867)
33. S. Pepke, B. Wold, and A. Mortazavi. Computation for ChIP-seq and RNA-seq studies. Nature Methods, 6:S22-S32, 2009.
34. M. Pop, S. L. Salzberg, and M. Shumway. Genome Sequence Assembly: Algorithms and Issues. Computer, 35(7):47-54, 2002.
35. SAGA - A Simple API for Grid Applications. http://saga.cct.lsu.edu/.
36. M. C. Schatz. CloudBurst: highly highly sensitive read mapping with MapReduce. Bioinformatics, 25(11):1363-1369, 2009.
37. K. Scheibye-Alsing, S. Hoffmann, A. Frankel, P. Jensen, P. F. Stadler, Y. Mang, N. Tommerup, M. J. Gilchrist, A. B. Nygard, S. Cirera, C. B. Jorgensen, M. Fredholm, and J. Gorodkin. Sequence asembly. Comp. Biol and Chem., 33:121-136, 2009.
38. R. Sork and P. Cossart. Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nat. Rev. Genet., 11:9-16, 2010.
39. R. C. Taylor. An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. BMC Bioinformatics, 11:S1, 2010.
40. The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature, 467:1061-1073, 2010.
41. C. Trapnell and S. L. Salzberg. How to map billions of short reads onto genomes. Nat. Biotech., 27(5):455-457, 2009.
42. Z. Wang, M. Gerstein, and M. Snyder. RNA-seq : a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10(1):57-63, 2009.