Abstract
The NGS technology produces large scale biologic data sets much cheaper and faster than the previous methods. As it is almost impossible to store or analyze such large scale NGS data with a traditional method on a commodity server, many problems arise. Hadoop is an alternative to this requirement. We aim to address the issues involved in the large scale data analysis on the cloud in bioinformatics. Accordingly, we propose analysis service for predicting genome structural variations associated with diseases by using Hadoop. The result of this study reveals that the system proposed in this study efficiently predicts genomic variations from large scale data sets.
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References
Xia, J., Wang, Q., Jia, P., Wang, B., Pao, W., Zhao, Z.: NGS catalog: A database of next generation sequencing studies in humans. Hum. Mutat. 33, E2341–E2355 (2012)
Chen, K., Wallis, J.W., McLellan, M.D., Larson, D.E., Kalicki, J.M., Pohl, C.S., McGrath, S.D., Wendl, M.C., Zhang, Q., Locke, D.P., Shi, X., Fulton, R.S., Ley, T.J., Wilson, R.K., Ding, L., Mardis, E.R.: BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat. Methods 6, 677–681 (2009)
Kuehn, B.M.: 1000 Genomes Project promises closer look at variation in human genome. JAMA 300, 2715 (2008)
Medvedev, P., Stanciu, M., Brudno, M.: Computational methods for discovering structural variation with next-generation sequencing. Nat. Methods 6, S13–S20 (2009)
Li, H., Durbin, R.: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010)
Duclos, A., Charbonnier, F., Chambon, P., Latouche, J.B., Blavier, A., Redon, R., Frebourg, T., Flaman, J.M.: Pitfalls in the use of DGV for CNV interpretation. Am. J. Med. Genet. A 155A, 2593–2596 (2011)
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Ko, G., Yoon, J., Park, K. (2014). HASV: Hadoop-Based NGS Analyzer for Predicting Genomic Structure Variations. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_49
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DOI: https://doi.org/10.1007/978-3-642-40675-1_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40674-4
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