Abstract
Insight to utilize the Big data of Bioinformatics information generated by a paradigm; Cloud Computing is coming up as a guarantee to deal with big information storage and scrutiny challenges in the Bioinformatics field. Cloud computing is viewed to be a cost effectual technique to process and accumulate this immense quantity of data with parallel processing tools and carried as “Services” through the internet. Due to its fast and efficient performance for data processing on cloud clusters and easy to use environments, The Hadoop parallel programming framework is dominantly used. This document will be bearing in the direction of the productive course for economical Bioinformatics clouds for the Big data and also the challenges that would obstruct Bioinformatics Big data to take a stride towards the cloud. In this document, we state an outline of the applications of Bioinformatics clouds, merits, and limitations of the current research activity methods used for storing Big Data in Bioinformatics. The paper mentions how the existing dilemma can be addressed from the perspective of Cloud computing services in addition to Bioinformatics tools. For ensuring trust, a simulation comparing the trust values for different Cloud providers is being illustrated in Fog server. For Future enhancements, efforts are being made to build up an efficient cloud data storage system employing different Bioinformatics tools ensuring security so that various Healthcare organizations are benefited by this approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Galbraith. Frontiers in genomic assay technologies the grand challenges in enabling data-intensive biological research. Front. Genet. (2011). https://doi.org/10.3389/fgene.2011.00026
Merelli, I.: Managing, analyzing, and integrating big data in medical bioinformatics: open problems and future perspectives. BioMed Res. Int. Hindawi Publishing Corporation article id:134023 (2014). http://dx.doi.org/10.1155/2014/134023
Oliveira, G.S.S., Edward, R., et al.: ACOsched: a scheduling algorithm in a federated cloud infrastructure for bioinformatics applications. Bioinf. Biomed. (BIBM) IEEE (2013). https://doi.org/10.1109/bibm.2013.6732620
Marx, V.: The Big Challenges of Big Data, Technology Feature Nature 255, vol. 498 (2013)
Driscoll, O.A.: Big data, hadoop and cloud computing in genomics. J. Biomed. Inf. Elsevier (2013). http://dx.doi.org/10.1016/j/jbi.2013.07.001
Yang, A., Troup, M., et al.: Scalability and validation of big data bioinformatics software. Comput. Struct. Biotechnol. J. Elsevier 15, 379–386 (2017)
Fernandez, A., del Rio, S., et al.: Big data with cloud computing: an insight into the computing environment. In: MapReduce Programming frameworks, vol. 4, pp. 38–409. Wiley (2014)
Ibrar, Y., Salimah, M., et al.: The rise of big data on cloud computing. Inf. Syst. Elsevier 47, 98–115 (2014)
Oracle and Big Data. http://www.oracle.com/us/technologies/big-data/index.html
Thakur, R.S., Bandopadhyay, R.: Role of cloud computing in Bioinformatics research for handling the huge biological data. In: Biology of Useful Plants and Microbes Chapter 20 Narosa Publishing House
Nemade P.: Big Data in bioinformatics & the era of cloud computing. IOSR J. Comput. Eng. (IOSR-JCE) 14(2), 53–56 (2013). e-ISSN: 2278-0661
Mu, A., Kuo, H.: Opportunities and challenges of cloud computing to improve health care services. J. Med. Internet Res. 13(3) (2011). https://doi.org/10.2196/jmir.1867
Luo, J.: Big data application in biomedical research and healthcare. Biomed. Inf. Insights Libertas Acad. 8 (2016). https://doi.org/10.4137/bii.s31559
Hua, G.J., Chuan Yi ,T., et al.: Cloud computing service framework for bioinformatics tools. BIBM IEEE (2015). https://doi.org/10.1109/bibm.2015.735899
Jerico, R., Bethwaite, B., et al.: Development of a cloud-based bioinformatics training platform, briefings in bioinformatics, pp. 1–8 (2016). https://doi.org/10.1093/bib/bbw032
Calabrese, B., Cannataro, M.: Cloud Computing in Bioinformatics: Current Solutions and Challenges (2016). http://doi.org/10.7287/peerj.preprints.2261v1
Shanahan, P.H.: Bioinformatics on the Cloud Computing Platform Azure 9(7) Plusone e102642 (2014)
Prachi, S.: Big Genomic data in bio-informatics cloud applied microbiology: open access 2(2) (2016). https://doi.org/10.4172/2471-9315.1000113
Hsu, H.C.: Biocloud: cloud computing for biological, genomics, and drug design. In: BioMed Research International Hindawi Publishing Corporation Article Id 909470 (2013)
Samuel, A.V.: CloVR: A Virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinf. http://www.biomedcentral.com/1471-2015/12/356 (2015)
Dowlin, N., Laine, K., et al.: Manual for using homomorphic encryption for bioinformatics. Proc. IEEE 105(3) IEEE (2017)
Misirli, G., Madsen, C., et al.: Constructing synthetic biology workflows in the cloud. Eng. Biol. IET 1(1), 61–65 (2017). https://doi.org/10.1049/enb.2017.0001
Moghaddasi, H., Tabrizi, T.A.: Applications of cloud computing in health systems. Global J. Health Sci. 9(6) (2017)
Kumar, V.: Cloud computing using bioinformatics MapReduce applications. In: Colossal data Analysis, and Networking (CDAN), IEEE (2016). https://doi.org/10.1109/cdan.2016.7570893
Calabrese, B.: Cloud computing in bioinformatics: current solutions and challenges. PeerJPreprints (2016). http://doi.org/10.7287/peerj.preprints.2261v1
Celesti, A.: New trends in Biotechnology: the point on NGS cloud computing solutions. In: IEEE Workshop on ICT Solutions for eHealth (2016). 978-1-5090-0679-3/16/$31.00
Guan, X., et al.: Cancer metastases: challenges and oppurtunities. Acta Pharmaceutica Sinica. B. 5(5):402–418 (2015)
Afgan, E., Krampis, K., et al.: Building and provisioning bioinformatics environments on public and private clouds. MIPRO IEEE (2015). https://doi.org/10.1109/mipro.2015.7160269
Lukas, F., Tomislav, L., et al.: Delivering bioinformatics MapReduce applications in the cloud. MIPRO (2014). https://doi.org/10.1109/mipro.2014.68595930
Coutinho, R., Drummond, L., et al.: Evaluating grasp-based cloud dimensioning for comparative genomics: a practical approach. In: Cluster Computing (CLUSTER). IEEE (2014). https://doi.org/10.1109/cluster.2014.6968789
Lin, L.Y.: Enabling large scale biomedical analysis in the cloud. BioMed Research International, Hindawi Publishing Corporation (2013). http://dx.doi.org/10.1155/2013/185679
Gabriel, D.: Food production vs biodiversity: comparing organic and conventional agriculture. J. Appl. Ecol. 50(2) (2013)
Che, H.L.: Cloud computing-based tagSNP selection algorithm for human genome data. Int. J. Mol. Sci. 16, 1096–1110 (2015)
Yixue, L., Chen, L.: Big biological data: challenges and opportunities. In: Genomics Proteomics Bioinformatics (2014). http://dx.doi.org/10.1016/j.gpb.2014.10.001
Nguyen, T.: CloudAligner: a fast and full featured MapReduce based tool for sequence mapping. BMC Res. Notes 4(1), 171 (2011)
Schatz, C., Langmead, B., et al.: CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25(11), 1363–1369 (2009)
Luca, P.: MapReducing a genomic sequencing workflow. In: Proceedings of the 2nd International Workshop on MapReduce and its Applications, pp. 67–74. ACM (2011)
Schatz, C.M.: Cloud computing and the DNA data race. Nature Biotechnol. 28(7), 691–693 (2010)
Langmead, B.: Searching for SNPs with cloud computing. Genome Biol. 10(11) (2009)
Gunarathne: Cloud computing paradigms for pleasingly parallel biomedical applications. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed computing, HDPC, pp. 460–469. ACM (2010)
Ben: CloudScale RNA-sequencing differential expression analysis with myrna. Genome Biol. 11, 1–11 (2010)
Hong, D.: RNA sequence analysis tool on the cloud. Bioinformatics 28, 721–723 (2012)
Jourdren, L.: Eoulsan: a cloud computing base framework facilitating high throughput sequencing analysis. Bioinformatics 28, 1542–1543 (2012)
Hydra, L.S.: A scalable proteomic search engine which utilizes the Hadoop distributed computing framework. BMC Bioinf. 13 (2012)
Matsunaga, A.: CloudBlast: combining MapReduce and virtualization on distributed resources for bioinformatics applications. In: IEEE Fourth International Conference On eScienceIndiana, pp. 222–229. USA (2008)
Niemenmaa, M.: Hadoop-BAM: directly manipulating next-generation sequencing data in the cloud. Bioinformatics 28, 876–877 (2012)
Merriman, B.: SeqWare query engine: storing and searching Sequence data in the cloud. BMC Bioinf. 11 (2010)
McKenna, A.: The genome analysis toolkit: MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 1297–1303 (2010)
Huang, H.L.: BlueSNP: a package for highly scalable genomic-wide association studies using Hadoop clusters. Bioinformatics 29, 135–136 (2013)
Angiuoli, B.D., Matalka, M., et al.: CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinf. 12 (2011)
Krampis, K.: Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community. BMC Bioinf. 13 (2012)
Shi, L., Wang, Z., et al.: A case study of tuning MapReduce for efficient bioinformatics in the cloud. Parallel Comput. 61, 83–95 (2017)
MeteAkgun, A., Sagiroglu, S., et al.: Privacy preserving processing of genomic data: a survey. J. Biomed. Inf. Elsevier 56, 103–111 (2015)
Mushegian. Grand challenges in Bioinformatics and Computational Biology. Front. Genet. (2011). https://doi.org/10.3389/fgene.2011.00060
Heitor, H., Aleteia, P., et al.: Attribute-based access control in federated clouds: a case study in bioinformatics. Inf. Syst. Technol. IEEE (2017). https://doi.org/10.23919/cisti.2017.7975855
Yamamota, U., Oguchi, M.: A decentralized system of genome secret search implemented with fully homomorphic encryption. In: Smart Computing (SMARTCOMP), IEEE (2017). https://doi.org/10.1109/smartcomp.2017.7946977
Namasudra, S., Roy, P., et al.: Time efficient secure DNA based access control model for cloud computing environment. FGCS Elsevier 73, 90–105 (2017)
Silva, S.B.S., Deborah, H.M., et al.: Secure and robust cloud computing for high-throughput forensic microsatellite sequence analysis and data basing. Forensic Sci. Int. Genet. Elsevier 31, 40–47 (2017)
Abdulunabi, M., Haqbi, A., et al.: A distributed framework for health information exchange using smartphone technologies. J. Biomed. Inf. Elsevier 69, 230–250 (2017)
Goyat, S., Jain, S.: A secure cryptographic cloud communication using DNA cryptographic technique. In: Inventive Computation Technologies (ICICT), IEEE (2016). https://doi.org/10.1109/inventive.2016.7830158
Nepal, S.: TruXy: trusted storage cloud for scientific workflows. IEEE Trans. Cloud Comput. 5(3), 428–442 (2016). https://doi.org/10.1109/tcc.2015.2489638
Siddaramappa, V., Ramesh, B.K.: Cryptography and bioinformatics techniques for secure information transmission over insecure channels. Appl. Theoret. Comput. Commun. Technol. IEEE (2015). https://doi.org/10.1109/icatcct.2015.7456870
Liu, B., Madduri, R., et al.: Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses. J. Biomed. Inf. Science direct 49, 119–133 (2014)
Razick: The eGenVar Data management system-cataloging and sharing sensitive data and metadata for life sciences, database (2014)
Rodrigues: Analysis of the security and privacy requirements of cloud-based electronic health record systems. J. Med. Internet Res. (2013)
Alex: Data management in the cloud: challenges and oppurtunities. Mogan & Claypool Publishers. ISBN:9781608459247 (2011)
Ney, P., Koscher, K., et al.: Computer security, privacy, and DNA sequencing: compromising computers with synthesized DNA, privacy leaks, and more. In: 26th UNENIX Security Symposium (2017). ISBN: 978-1-931971-40-9
Hamid Abdulaziz, H., Rahman Mizanur, Md.S.K., et al.: A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography, IEEE ACCESS (2017). https://doi.org/10.1109/access.2017.2757844
Gonzalez, N., Goya, W., et al.: Fog Computing: Data Analytics and Cloud Distributed Processing on the Network Edges, IEEE (2016). 978-1-5090-3339-3/16/$31.00_c
Stojmenovic, I., Wen, S.: The fog computing paradigm: scenarios and security issues. In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, ACSIS, vol. 2, pp. 1–8. IEEE (2014). https://doi.org/10.15439/2014f503
Yi, S.: Security and Privacy Issues of Fog Computing, WASA (2015). https://doi.org/10.1007/978-3-319-21837-3_67
Hashem, T., Yaqoob, I., Anuar, B., et al.: The rise of “big data” on cloud computing: review and open research issues. Inform Syst 47:98–115 Elsevier (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Seth, B., Dalal, S., Kumar, R. (2019). Securing Bioinformatics Cloud for Big Data: Budding Buzzword or a Glance of the Future. In: Kumar, R., Wiil, U. (eds) Recent Advances in Computational Intelligence. Studies in Computational Intelligence, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-030-12500-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-12500-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12499-1
Online ISBN: 978-3-030-12500-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)