Progress Towards and Challenges in Biological Big Data
Abstract
Biological big data represents a vast amount of data in bioinformatics and this could lead to the transformation of the research pattern into large scale. In medical research, a large amount of data can be generated from tools including genomic sequencing machines. The availability of advanced tools and modern technology has become the main reason for the expansion of biological data in a huge amount. Such immense data should be utilized in an efficient manner in order to distribute this valuable information. Besides that, storing and dealing with those big data has become a great challenge as the data generation are tremendously increasing over years. As well, the blast of data in healthcare systems and biomedical research appeal for an immediate solution as health care requires a compact integration of biomedical data. Thus, researchers should make use of this available big data for analysis rather than keep creating new data as they could provide meaningful information with the use of current advanced bioinformatics tools.
Keywords:
Big data, Bioinformatics, Biomedical, Database, Sequence
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References
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Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analysis to personalized medicine for all: challenges and opportunities. BMC Medical Genomics, 8, 33. https://doi.org/10.1186/s12920-015-0108-y
Anderson, J. & Rainie, L. (2012). The future of big data. Pew Research Center Internet & Technology.
Check Hayden, E. (2015). Genome researchers raise alarm over big data. Nature News.
Chen, M., Mao, S. & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.
Gebelhoff, R. (2015). Sequencing the genome creates so much data we don’t know what to do with it. The Washington Post 1-3.
Greene, C. S., Tan, J., Ung, M., Moore, J. H. & Cheng, C. (2014). Big data bioinformatics. Journal of Cellular Physiology, 229(12), 1896-1900.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A. & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.
Kashyap, H., Ahmed, H. A., Hoque, N., Roy, S. & Bhattacharyya, D. K. (2015). Big data analytics in bioinformatics: A machine learning perspective. Journal of Latex Class Files, 13, 20.
Kho, A. N., Rasmussen, L. V., Connolly, J. J., Peissig, P. L., Starren, J., Hakonarson, H. & Hayes, M. G. (2013). Practical challenges in integrating genomic data into the electronic health record. Genetics in Medicine, 15, 772-778.
Kozubek, J. (2018). Modern prometheus: Editing the human genome with crispr-cas9, Cambridge University Press.
Lathe, W., Williams, J., Mangan, M. & Karolchik, D. (2008). Genomic Data Resources: Challenges and Promises. Nature Education, 1, 1.
Levy, S. E. & Myers, R. M. (2016). Advancements in Next-Generation Sequencing. Annual Review of Genomics and Human Genetics, 17, 95-115.
Li, Y. & Chen, L. (2014). Big Biological Data: Challenges and Opportunities. Genomics Proteomics Bioinformatics, 12, 187-189.
Liang, Y. & Kelemen, A. (2016). Big Data science and its applications in health and medical research: Challenges and opportunities. Austin Journal of Biometrics & Biostatistics, 7.
Liu, B., Madduri, R. K., Sotomayor, B., Chard, K., Lacinski, L., Dave, U. J., Li, J., Liu, C. & Foster, I. T. (2014). Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses. Journal of Biomedical Informatics, 49, 119-133.
Luo, J., Wu, M., Gopukumar, D. & Zhao, Y. (2016). Big Data Application in Biomedical Research and Health Care: A Literature Review. Biomedical Informatics Insights, 8, 1-10.
Marx, V. (2013). Biology: The big challenges of big data. Nature Publishing Group.
Mcafee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. & Barton, D. (2012). Big Data: The Management Revolution. Harvard Business Review, 90, 1-9.
Merelli, I., Perez-Sanchez-Sanchez, H., Gesing, S. & D’Agostino, D. (2014). Managing, Analysing, and Integrating Big Data in Medical Bioinformatics: Open Problems and Future Perspectives. BioMed research international, 2014, 13.
Muir, P., Li, S., Lou, S., Wang, D., Spakowicz, D. J., Salichos, L., Zhang, J., Weinstock, G. M., Isaacs, F. & Rozowsky, J. (2016). The real cost of sequencing: scaling computation to keep pace with data generation. Genome biology, 17, 53.
Nekrutenko, A. & Taylor, J. (2012). Next-generation sequencing data interpretation: enhancing reproducibility and accessibility. Nature Reviews Genetics, 13, 667-672.
O’Driscoll, A., Daugelaite, J. & Sleator, R. D. (2013). ‘Big data’, Hadoop and cloud computing in genomics. Journal of biomedical informatics, 46, 774-781.
Schuster, S. C. (2008). Next-generation sequencing transforms today's biology. Nature methods, 5, 16-18.
Simon, R. (2008). Interpretation of Genomic Data: Questions and Answers. Seminars in hematology. Elsevier, 196-204.
Stephens, Z. D., Lee, S. Y., Faghri, F., Campbell, R. H., Zhai, C., Efron, M. J., Iyer, R., Schatz, M. C., Sinha, S. & Robinson, G. E. (2015). Big data: astronomical or genomical? PLOS BIOLOGY, 13.
Wall, D. P., Kudtarkar, P., Fusaro, V. A., Pivovarov, R., Patil, P. & Tonellato, P. J. (2010). Cloud computing for comparative genomics. BMC Bioinformatics, 11, 259.
Wang, X., Williams, C., Liu, Z. H. & Croghan, J. (2017). Big data management challenges in health research—a literature review. Briefings in bioinformatics, 1-12.
Ware, A., Janvale, G., Shaikh, F. & Harke, S. (2017). HADOOP: Solution for Big Data Challenges in Bioinformatics and its Prospective in India. Journal of Computer Engineering, 51-54.
Wetterstrand, K. A. (2013). DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP).
Xie, X., Ho, J., Murphy, C., Kaiser, G., Xu, B. & Chen, T. Y. (2009). Application of Metamorphic Testing to Supervised Classifiers. Ninth International Conference on Quality Software. IEEE Computer Society, 135-144.
Yang, A., Troup, M. & Ho, J. W. (2017). Scalability and Validation of Big Data Boinformatics Software. Computational and Structural Biotechnology Journal, 15, 379-386.
Zhao, S., Watrous, K., Zhang, C. & Zhang, B. (2017). Cloud Computing for Next-Generation Sequencing Data Analysis. Cloud Computing-Architecture and Applications, InTech, Rijeka, 29-51.
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How to Cite
Sritharan, M., & Avin, F. (2018). Progress Towards and Challenges in Biological Big Data. International Journal of Advancement in Life Sciences Research, 1(4), 35-38. Retrieved from http://ijalsr.org/index.php/journal/article/view/39
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Research Articles
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