Novel Computing Technologies for Bioinformatics and CheminformaticsView this Special Issue
Review Article | Open Access
Enabling Large-Scale Biomedical Analysis in the Cloud
Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.
In more and more cases, the ability to gain experimental data has far surpassed the capability in doing further analyses. DNA sequencing presents a particularly good example of this trend. By current next-generation sequencing (NGS) technologies, an individual laboratory can generate terabase-scales of DNA and RNA sequencing data within a day at a reasonable cost [1–3]. However, the computing technologies required to maintain, process, and integrate the massive datasets are beyond the reach of small laboratories and introduce serious challenges even for larger institutes. Success at all fields will heavily rely on the ability to explain these large-scale and great diversification datasets, which drives us to adopt advances in computing methods.
The coming age of sharp data growth and increasing data diversification is a major challenge for biomedical research in the postgenome era. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and massive computing on large-scale data [4–6]. More than this cloud platform can considerably save costs in server hardware, administration, and maintenance by the virtualization technology, which allows systems to act like real computers with flexible specification of the number of processors, memory, and disk size, operating system, and so on. With flexible cloud architectures that can harness petabyte scales of data, Internet-based companies, such as Google and Amazon, offer on-demand services to tens of thousands of users simultaneously. In addition, cloud storages allow large-scale and potentially shared datasets to be stored on the same infrastructure where further analyses can be run . A good example is the data from the 1000 Genomes Project, which has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals, and it is now available on the Amazon cloud . Developing translational biomedical applications with cloud technologies will enable significant breakthroughs in the diagnosis, prognosis, and high-quality healthcare. This study introduces the data-intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the massive datasets meaningful and usable.
This paper is organized as follows. Section 2 introduces the state of the art in the cloud developments of translational biomedical science. Subsequently, we review the framework and platforms for massive computing in the cloud in Section 3. Finally, Section 4 draws our conclusion.
2. Translational Biomedical Science in the Cloud
Over the last decades, biomedical informatics has contributed a vast amount of data. In the genomic side, the data deluge comes from genotyping, gene expression, NGS data, and so on. The sequence read archive (SRA) provides the scientific community with an archival destination for raw sequence data, whose volume has reached 1.6 petabytes in 2013 . A key goal of 1000 Genomes Project is to investigate the genetic contribution to human disease by characterizing the geographic and functional spectrum of genetic variation on a great deal of sequencing data . More genome-wide association studies (GWAS) continue to identify common genetic factors that influence health or cause disease [11–13]. On the other hand, the diagnosis side constantly generates data from pharmacy prescription data, electronic medical and insurance records, healthcare information, and so forth. Electronic health record (EHR) is a digital data for the traditional document-based patient chart and has been essential to manage the wealth of existing clinical information. US health care data alone reached 150 exabytes (=109 gigabytes) in 2011, while at this rate its volume would be zettabyte (=1012 gigabytes) scale soon . In many respects, the two sides of biomedical data growth have yet to converge; however, the biomedical infrastructure for big data analysis lags behind the applications. The healthcare system has no capacity yet to distill the implicit meaning of the planet-size data for timely medical decision making. Despite the strong challenge of big data, there are considerable works in the bioinformatics community to develop feasible solutions. In what follows, existing cloud-based resources and GPU computing are summarized to the two types of biomedical data.
2.1. Genomic-Driven Data
Today new technologies in genomics/proteomics generate biomedical data with an explosive rate. With data volume getting larger more quickly than traditional storage and computation can afford, it is the time for biomedical studies to migrate these challenges to the cloud. Cloud computing offers new computational paradigms to not only deal with data and analyses at scale but also reduce the building and operation costs. By cloud technologies, numerous works have reported successful applications in bioinformatics (Table 1). These recent developments and applications would facilitate biomedical studies to harness the planet-size data.
Cloud-based tools in Table 1 combine distributed computing and large-scale storage to come with an effective solution in terms of data transfer, storage, computation, and analysis of big biomedical data. By deploying applications with these tools, small laboratories could maintain and process the large-scale datasets within affordable costs, which is increasingly thorny even for large institutes. For example, BioVLab infrastructure [28, 36] built on the cloud is developed for genome analysis by utilizing the virtual collaborative lab, a suite of tools that allow scientists to orchestrate a sequence of data analysis tasks using remote computing resources and data storage facilities on demand from local devices. Furthermore, the Crossbow  genotyping program applies the MapReduce workflow on Hadoop to launch many copies of the short-read aligner Bowtie  in parallel. Once the aligned reads are generated, Hadoop automatically starts the MapReduce workflow of consensus calling to sort and aggregate the alignments. In the benchmark set on the Amazon EC2 cloud, Crossbow genotyped a human sample comprising 2.7 billion reads in less than 3 hours using a 320-CPU cluster for a total cost of $85 .
2.2. Diagnosis-Driven Data
More and more requirements to the healthcare quality raise difficulties in processing both the heavy and heterogeneous biomedical data. For example, the high-resolution and dynamic data of medicinal images imply that the data transfer and image analysis are extremely time-consuming. Several works leverage the cloud approach to tackle the difficulties. MapReduce, the parallel computing framework in cloud, has been used to develop an ultrafast and scalable image reconstruction method for 4D cone-beam CT . A solution to power the cloud infrastructure for digital imaging communication in medicine (DICOM) is introduced as a robust cloud-based service . Whereas cloud-based medical image exchange is increasingly prevalent in medicine, its security and privacy issues to the data storage and communication need to be improved [39, 40].
An alternative to attack compute-intensive problems relies on the graphics processing unit (GPU), where there are two dominant APIs for GPU computing: CUDA and OpenCL . GPU architectures feature several multiprocessors with each number of stream processors. The kernel is a function on GPU, while it splits works into blocks and threads. Blocks are assigned to run on multiprocessors, each of which is composed of a user-defined number of threads. The number of threads in a block can be different to the number of stream processors inside a multiprocessor because they run in groups of constant threads called warps. Stream processors are similar to CPU cores, but they share a single fetch-decode unit within the same multiprocessor, which forces threads to execute in lockstep. The mechanism likes the traditional single instruction multiple data (SIMD) instruction; however, any thread can diverge from the common execution path so as to increase the flexibility. Two review papers present the works on GPU accelerated medical image processing and cover algorithms that are specific to individual modalities [42, 43]. Intel quite recently unveiled its new Xeon Phi coprocessor as their many integrated core (MIC) product, while the China Tianhe-2 with the coprocessor inside was announced by TOP500 as the world’s fastest supercomputer in 2013 . The new coprocessor has a dramatic impact on the high-performance computing field and will drive more bioinformatics applications .
As to the clinical informatics, a major challenge is to integrate a wide range of heterogeneous data into a single and space-saving database for further queries and analyses. EHR could be an ideal solution because it is the patient-centered record by integrating and managing personal medical information from various sources. EHRs are built to share information with other healthcare providers and organizations, while the cloud technologies can facilitate EHR integration and sharing. Developing EHR services on the cloud can not only reduce the building and operation costs but also support the interoperability and flexibility . There are a great number of works that contributed different cloud-supported frameworks to improve EHR services. For instance, an e-health cloud system is defined to be capable of adapting itself to different diseases and growing numbers of patients, that is, improving the scalability . Khansa et al. proposed an intelligent cloud-based EHR system and claimed that it has the potential to reduce medical errors and improve patients’ quality of life . A recent work introduces the state of cloud computing in healthcare . Moreover, there are a number of security issues/concerns associated with cloud computing, which is one of the major obstacles for the commercial considerations. As the emerging cloud technology to the healthcare system, more recent studies investigate the security and privacy issues [50–53].
3. Massive Computing in the Cloud
Cloud computing started with the promise of inexhaustible resources so that the data-intensive computing can be easily deployed. The three service models of cloud computing, that is, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS), drive more complex and sophisticated markets. What makes cloud computing different from traditional IT technologies are mainly service delivery and consumer utilization models. Cloud platform is rapidly growing as a new paradigm for provisioning both storage and computing as a utility . Based on the platforms, the IT capability is raised so that services can be easily deployed in a pay-as-you-go model. Subsequently, lots of resources could be acquired with a relatively low cost to test novel ideas or conduct extensive simulations. One could access more computing resources in lab to carry out his innovation based on a self-service and self-managed environment. Also, the feature of scalability for cloud platforms allows a lab-scale tool to be extended to a cloud application or a data-intensive scalable computing (DISC) system with fewer efforts [55, 56].
3.1. MapReduce Framework
One cannot mention DISC without mentioning MapReduce, while even many works regard MapReduce as the de facto standard for DISC [55, 57]. In 2004, Google announced a distributed computing framework, MapReduce, as the key technology for processing large datasets on a cluster made by upwards of one thousand commodity machines . The MapReduce framework facilitates the management and development of massively parallel computing applications. A MapReduce program consists of two user-specified functions: map and reduce. The map function processes a <key, value> pair to generate a set of intermediate pairs, whereas the reduce function merges all intermediate results associated with the same key. In the beginning, the programming framework is used to assist Google in speedy searches, and nowadays more than 10,000 distant programs have been conducted at Google for the large-scale data analysis . Once applications are modeled to the MapReduce manner, they all enjoy the scalability and fault-tolerance inherent in its execution platform supported by Google File System (GFS), whereas the successful implementation of the MapReduce model, the open-source platform Hadoop, along with the MapReduce framework, has been extensively used outside of Google by academia and industry . Moreover, Ekanayake et al. compared the performances of Hadoop MapReduce, Microsoft Dryad-LINQ, and MPI implementation on two bioinformatics applications and suggested that the flexibility of MapReduce will become the preferred approach . Recently, more and more MapReduce applications are proposed for bioinformatics studies [16–18, 33, 37, 61].
3.2. Cloud Platform
PaaS provides a substantial boost with the manageable cost, and there have been a number of solutions, such as Google App Engine (GAE), Amazon Elastic Compute Cloud (EC2), and Windows Azure. GAE offers a robust and extensible runtime environment for developing and hosting web-based applications in Google-managed infrastructure, rather than providing direct access to a customized virtual machine. Malawski et al. investigated how to use GAE service for free of charge execution of compute-intensive problems , while Prodan et al. compared GAE and Amazon EC2 in performance and resource consumption by four basic algorithms . EC2 is a cloud service whereby one can rent virtual machines from Amazon data center and deploy scalable applications on them. Several works are conducted to evaluate EC2 performance . Wall et al. concluded that the effort to transform existing comparative genomics algorithms from local infrastructures to cloud is not trivial, but the cloud environment is an economical alternative in the speed and flexibility considerations . Further, two works explore the biomedical cloud built on Amazon service with several case studies [66, 67].
Windows Azure platform provides a series of services for developing and deploying Windows-based applications on the cloud, and it makes use of Microsoft infrastructure to host services and scale them seamlessly [68–70]. Moreover, Aneka provides a flexible model for developing distributed applications, which can be integrated with external cloud platforms further. Aneka presents the possibility to avoid vendor lockin through a virtual infrastructure, a private datacentre, or a server, so that one could freely scale to cloud platforms when required. Its deadline-driven provisioning mechanism also supports QoS-aware execution of scientific applications in hybrid clouds . It is handy to leverage famous PaaS platforms for compute-intensive applications; however, commercial cloud services charge for CPU time, storage space, bandwidth usage, and advanced functions. Apart from the service charge, the commercial cloud platform is still difficult for data-intensive applications. The critical factor is that current network infrastructure is too slow to enable terabytes of data to be routinely transferred. A feasible solution for transferring planet-size data is to copy the data into a big storage drive and then send the drive to the destination. In addition, the private cloud solution helps developers to construct cloud platforms for local use .
Recent technologies on next-generation sequencing and high-throughput experiments cause an exponential growth of biomedical data, and subsequently serious challenges arise in processing data volume and complexity. Numerous works have reported successful bioinformatics applications to harness the big data. Developing cloud-based biomedical applications can integrate the vast amount of diversification data in one place and analyze them on a continuous basis. This would make a significant breakthrough to launch a high-quality healthcare. This work briefly introduces the data-intensive computing systems and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical applications to make the planet-size data meaningful and usable.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported in part by the National Science Council under contract number NSC-102-2218-E-035-004.
- 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.
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.