BioMed Research International
Volume 2013 (2013), Article ID 185679, 6 pages
http://dx.doi.org/10.1155/2013/185679
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.
Linked References
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar
- L. D. Stein, “The case for cloud computing in genome informatics,” Genome Biology, vol. 11, no. 5, article 207, 2010. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 1000 Genomes Project Consortium, “An integrated map of genetic variation from 1, 092 human genomes,” Nature, vol. 491, pp. 56–65, 2012. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- G. Gibson, “Rare and common variants: twenty arguments,” Nature Reviews Genetics, vol. 13, no. 2, pp. 135–145, 2012. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- M. C. Schatz, “CloudBurst: highly sensitive read mapping with MapReduce,” Bioinformatics, vol. 25, no. 11, pp. 1363–1369, 2009. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- X. Feng, R. Grossman, and L. Stein, “PeakRanger: a cloud-enabled peak caller for ChIP-seq data,” BMC Bioinformatics, vol. 12, article 139, 2011. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Google Scholar
- 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. View at Google Scholar
- S. G. Shinia, T. Thomas, and K. Chithraranjana, “Cloud based medical image exchange-security challenges,” Procedia Engineering, vol. 38, pp. 3454–3461, 2012. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar · View at Scopus
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- R. E. Bryant, “Data-intensive scalable computing for scientific applications,” Computing in Science and Engineering, vol. 13, no. 6, pp. 25–33, 2011. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- J. Dean and S. Ghemawat, “Map Reduce: a flexible data processing tool,” Communications of the ACM, vol. 53, no. 1, pp. 72–77, 2010. View at Publisher · View at Google Scholar · View at Scopus
- J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- 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. View at Google Scholar