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BioMed Research International
Volume 2013 (2013), Article ID 185679, 6 pages
Enabling Large-Scale Biomedical Analysis in the Cloud
1Master’s Program in Biomedical Informatics and Biomedical Engineering, Feng Chia University, No. 100 Wenhwa Road, Seatwen, Taichung 40724, Taiwan
2Department of Applied Mathematics, Feng Chia University, No. 100 Wenhwa Road, Seatwen, Taichung 40724, Taiwan
3Department of Information Engineering and Computer Science, Feng Chia University, No. 100 Wenhwa Road, Seatwen, Taichung 40724, Taiwan
4Department of Computer Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan
Received 6 August 2013; Accepted 22 September 2013
Academic Editor: Chun-Yuan Lin
Copyright © 2013 Ying-Chih Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- F. Luciani, R. A. Bull, and A. R. Lioyd, “Next generation deep sequencing and vaccine design: today and tomorrow,” Trends in Biotechnology, vol. 30, no. 9, pp. 443–452, 2012.
- L. Liu, Y. Li, S. Li, et al., “Comparison of next-generation sequencing systems,” Journal of Biomedicine and Biotechnology, vol. 2012, Article ID 251364, 11 pages, 2012.
- L. D. Stein, “The case for cloud computing in genome informatics,” Genome Biology, vol. 11, no. 5, article 207, 2010.
- E. E. Schadt, M. D. Linderman, J. Sorenson, L. Lee, and G. P. Nolan, “Computational solutions to large-scale data management and analysis,” Nature Reviews Genetics, vol. 11, no. 9, pp. 647–657, 2010.
- A. Rosenthal, P. Mork, M. H. Li, J. Stanford, D. Koester, and P. Reynolds, “Cloud computing: a new business paradigm for biomedical information sharing,” Journal of Biomedical Informatics, vol. 43, no. 2, pp. 342–353, 2010.
- J. Chen, F. Qian, W. Yan, and B. Shen, “Translational biomedical informatics in the cloud: present and future,” BioMed Research International, vol. 2013, Article ID 658925, 8 pages, 2013.
- S. Sakr, A. Liu, D. M. Batista, and M. Alomari, “A survey of large scale data management approaches in cloud environments,” IEEE Communications Surveys & Tutorials, vol. 13, no. 3, pp. 311–336, 2011.
- 1000 Genomes Project and AWS, http://aws.amazon.com/1000genomes/.
- M. Shumway, G. Cochrane, and H. Sugawara, “Archiving next generation sequencing data,” Nucleic Acids Research, vol. 38, supplement 1, pp. D870–D871, 2009.
- 1000 Genomes Project Consortium, “An integrated map of genetic variation from 1, 092 human genomes,” Nature, vol. 491, pp. 56–65, 2012.
- E. Evangelou and J. P. A. Ioannidis, “Meta-analysis methods for genome-wide association studies and beyond,” Nature Reviews Genetics, vol. 14, pp. 379–389, 2013.
- S. J. Chapman and A. V. S. Hill, “Human genetic susceptibility to infectious disease,” Nature Reviews Genetics, vol. 13, no. 3, pp. 175–188, 2012.
- G. Gibson, “Rare and common variants: twenty arguments,” Nature Reviews Genetics, vol. 13, no. 2, pp. 135–145, 2012.
- W. Hoover, Transforming Health Care Through Big Data, Institute for Health Technology Transformation, 2013.
- P. di Tommaso, M. Orobitg, F. Guirado, F. Cores, T. Espinosa, and C. Notredame, “Cloud-Coffee: implementation of a parallel consistency-based multiple alignment algorithm in the T-coffee package and its benchmarking on the Amazon Elastic-Cloud,” Bioinformatics, vol. 26, no. 15, pp. 1903–1904, 2010.
- J. S. Almeida, A. Gruneberg, W. Maass, and S. Vinga, “Fractal MapReduce decomposition of sequence alignment,” Algorithms for Molecular Biology, vol. 7, article 12, 2012.
- M. C. Schatz, “CloudBurst: highly sensitive read mapping with MapReduce,” Bioinformatics, vol. 25, no. 11, pp. 1363–1369, 2009.
- T. Nguyen, W. Shi, and D. Ruden, “CloudAligner: a fast and full-featured MapReduce based tool for sequence mapping,” BMC Research Notes, vol. 4, article 171, 2011.
- L. Pireddu, S. Leo, and G. Zanetti, “Seal: a distributed short read mapping and duplicate removal tool,” Bioinformatics, vol. 27, no. 15, pp. 2159–2160, 2011.
- R. Li, Y. Li, X. Fang et al., “SNP detection for massively parallel whole-genome resequencing,” Genome Research, vol. 19, no. 6, pp. 1124–1132, 2009.
- B. Langmead, M. C. Schatz, J. Lin, M. Pop, and S. L. Salzberg, “Searching for SNPs with cloud computing,” Genome Biology, vol. 10, no. 11, article R134, 2009.
- M. C. Schatz, A. L. Delcher, and S. L. Salzberg, “Assembly of large genomes using second-generation sequencing,” Genome Research, vol. 20, no. 9, pp. 1165–1173, 2010.
- L. Jourdren, M. Bernard, M.-A. Dillies, and S. L. Crom, “Eoulsan: a cloud computing-based framework facilitating high throughput sequencing analyses,” Bioinformatics, vol. 28, no. 11, pp. 1542–1543, 2012.
- D. R. Kelley, M. C. Schatz, and S. L. Salzberg, “Quake: quality-aware detection and correction of sequencing errors,” Genome Biology, vol. 11, no. 11, article R116, 2010.
- B. Langmead, K. D. Hansen, and J. T. Leek, “Cloud-scale RNA-sequencing differential expression analysis with Myrna,” Genome Biology, vol. 11, article R83, 2010.
- D. Hong, A. Rhie, S.-S. Park et al., “FX: an RNA-seq analysis tool on the cloud,” Bioinformatics, vol. 28, no. 5, pp. 721–723, 2012.
- A. Goncalves, A. Tikhonov, A. Brazma, and M. Kapushesky, “A pipeline for RNA-seq data processing and quality assessment,” Bioinformatics, vol. 27, no. 6, pp. 867–869, 2011.
- H. Lee, Y. Yang, H. Chae, et al., “BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2,” IEEE Transactions on Nanobioscience, vol. 11, no. 3, pp. 266–272, 2012.
- M. Niemenmaa, A. Kallio, A. Schumacher, P. Klemelä, E. Korpelainen, and K. Heljanko, “Hadoop-BAM: directly manipulating next generation sequencing data in the cloud,” Bioinformatics, vol. 28, no. 6, pp. 876–877, 2012.
- B. D. O'Connor, B. Merriman, and S. F. Nelson, “SeqWare Query Engine: storing and searching sequence data in the cloud,” BMC Bioinformatics, vol. 11, no. 12, article S2, 2010.
- X. Feng, R. Grossman, and L. Stein, “PeakRanger: a cloud-enabled peak caller for ChIP-seq data,” BMC Bioinformatics, vol. 12, article 139, 2011.
- L. Zhang, S. Gu, Y. Liu, B. Wang, and F. Azuaje, “Gene set analysis in the cloud,” Bioinformatics, vol. 28, no. 2, pp. 294–295, 2012.
- A. McKenna, M. Hanna, E. Banks et al., “The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data,” Genome Research, vol. 20, no. 9, pp. 1297–1303, 2010.
- K. Krampis, T. Booth, B. Chapman et al., “Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community,” BMC Bioinformatics, vol. 13, article 42, 2012.
- S. V. Angiuoli, M. Matalka, A. Gussman et al., “CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing,” BMC Bioinformatics, vol. 12, article 356, 2011.
- H. Chae, I. Jung, H. Lee, et al., “Bio and health informatics meets cloud: BioVLab as an example,” Health Information Science and Systems, vol. 1, no. 6, 9 pages, 2013.
- B. Meng, G. Pratx, and L. Xing, “Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment,” Medical Physics, vol. 38, no. 12, pp. 6603–6609, 2011.
- G. Patel, “DICOM medical image management the challenges and solutions: cloud as a service (CaaS),” Open Access Scientific Reports, vol. 1, no. 4, 4 pages, 2012.
- L. A. B. Silva, C. Costa, and J. L. Oliveira, “DICOM relay over the cloud,” International Journal of Computer Assisted Radiology and Surgery, vol. 8, pp. 323–333, 2013.
- S. G. Shinia, T. Thomas, and K. Chithraranjana, “Cloud based medical image exchange-security challenges,” Procedia Engineering, vol. 38, pp. 3454–3461, 2012.
- J.-S. Varré, B. Schmidt, S. Janot, and M. Giraud, “Manycore high-performance computing in bioinformatics,” in Advances In Genomic Sequence Analysis and Pattern Discovery, L. Elnitski, H. Piontkivska, and L. R. Welch, Eds., chapter 8, World Scientific, 2011.
- A. Eklund, P. Dufort, D. Forsberg, and S. M. LaConte, “Medical image processing on the GPU—past, present and future,” Medical Image Analysis, vol. 17, no. 8, pp. 1073–1094, 2013.
- L. Shi, W. Liu, H. Zhang, et al., “A survey of GPU-based medical image computing techniques,” Quantitative Imaging in Medicine and Surgery, vol. 2, no. 3, pp. 188–206, 2012.
- TOP500 Supercomputer Sites, http://www.top500.org/.
- Intel, “Heterogeneous computing in the cloud: crunching big data and democratizing HPC access for the life sciences,” Intel White Paper, 2013.
- J. Haughton, “Look up: the right EHR may be in the cloud. Major advantages include interoperability and flexibility,” Health Management Technology, vol. 32, no. 2, p. 52, 2011.
- J. Vilaplana, F. Solsona, F. Abella, et al., “The cloud paradigm applied to e-Health,” BMC Medical Informatics and Decision Making, vol. 13, article 10, 2013.
- L. Khansa, J. Forcade, G. Nambari, et al., “Proposing an intelligent cloud-based electronic health record system,” International Journal of Business Data Communications and Networking, vol. 8, no. 3, pp. 57–71, 2012.
- S. P. Ahuja, S. Mani, and J. Zambrano, “A survey of the state of cloud computing in healthcare,” Network and Communication Technologies, vol. 1, no. 2, pp. 12–19, 2012.
- F. Magrabi, J. Aarts, C. Nohr, et al., “A comparative review of patient safety initiatives for national health information technology,” International Journal of Medical Informatics, vol. 82, pp. e139–e148, 2013.
- H. Singh, J. S. Ash, and D. F. Sittig, “Safety assurance factors for electronic health record resilience (SAFER): study protocol,” BMC Medical Informatics and Decision Making, vol. 13, article 8, 2013.
- D. F. Sittig and H. Singh, “Electronic health records and national patient-safety goals,” The New England and Journal of Medicine, vol. 367, no. 19, pp. 1854–1860, 2012.
- T. S. Chen, C. H. Liu, T. L. Chen, et al., “Secure dynamic access control scheme of PHR in cloud computing,” Journal of Medical Systems, vol. 36, no. 6, pp. 4005–4020, 2012.
- R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599–616, 2009.
- R. E. Bryant, “Data-intensive scalable computing for scientific applications,” Computing in Science and Engineering, vol. 13, no. 6, pp. 25–33, 2011.
- A. Iosup, S. Ostermann, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema, “Performance analysis of cloud computing services for many-tasks scientific computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 6, pp. 931–945, 2011.
- J. Dean and S. Ghemawat, “Map Reduce: a flexible data processing tool,” Communications of the ACM, vol. 53, no. 1, pp. 72–77, 2010.
- J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008.
- Apache Hadoop, http://hadoop.apache.org/.
- J. Ekanayake, T. Gunarathne, and J. Qiu, “Cloud technologies for bioinformatics applications,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 6, pp. 998–1011, 2011.
- M. E. Colosimo, M. W. Peterson, S. Mardis, and L. Hirschman, “Nephele: genotyping via complete composition vectors and MapReduce,” Source Code for Biology and Medicine, vol. 6, article 13, 2011.
- M. Malawski, M. Kuzniar, P. Wojcik, and M. Bubak, “How to use Google App engine for free computing,” IEEE Internet Computing, vol. 17, no. 1, pp. 50–59, 2013.
- R. Prodan, M. Sperk, and S. Ostermann, “Evaluating high-performance computing on google app engine,” IEEE Software, vol. 29, no. 2, pp. 52–58, 2012.
- J. J. Rehr, F. D. Vila, J. P. Gardner, L. Svec, and M. Prange, “Scientific computing in the cloud,” Computing in Science and Engineering, vol. 12, no. 3, pp. 34–43, 2010.
- D. P. Wall, P. Kudtarkar, V. A. Fusaro, R. Pivovarov, P. Patil, and P. J. Tonellato, “Cloud computing for comparative genomics,” BMC Bioinformatics, vol. 11, article 259, 2010.
- V. A. Fusaro, P. Patil, E. Gafni, D. P. Wall, and P. J. Tonellato, “Biomedical cloud computing with amazon web services,” PLoS Computational Biology, vol. 7, no. 8, Article ID e1002147, 2011.
- R. L. Grossman and K. P. White, “A vision for a biomedical cloud,” Journal of Internal Medicine, vol. 271, no. 2, pp. 122–130, 2012.
- Q. Xing and E. Blaisten-Barojas, “A cloud computing system in windows azure platform for data analysis of crystalline materials,” Concurrency and Computation, vol. 25, no. 15, pp. 2157–2169, 2013.
- I. Kim, J.-Y. Jung, T. F. DeLuca, et al., “Cloud computing for comparative genomics with windows azure platform,” Evolutionary Bioinformatics Online, vol. 8, pp. 527–534, 2012.
- S. J. Johnston, N. S. O’Brien, H. G. Lewis, et al., “Clouds in space: scientific computing using windows azure,” Journal of Cloud Computing, vol. 2, article 2, 2013.
- C. Vecchiola, R. N. Calheiros, D. Karunamoorthy, and R. Buyya, “Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka,” Future Generation Computer Systems, vol. 28, no. 1, pp. 58–65, 2012.
- M. Taifi, A. Khreishah, and J. Y. Shi, “Building a private HPC cloud for compute and data-intensive applications,” International Journal on Cloud Computing, vol. 3, no. 2, 20 pages, 2013.