HASV: Hadoop-Based NGS Analyzer for Predicting Genomic Structure Variations

  • Gunhwan KoEmail author
  • Jongcheol Yoon
  • Kyongseok Park
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 274)


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.


Structural Variation Fragment Size Insert Size Large Scale Data Analysis Commodity Server 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Kuehn, B.M.: 1000 Genomes Project promises closer look at variation in human genome. JAMA 300, 2715 (2008)CrossRefGoogle Scholar
  4. 4.
    Medvedev, P., Stanciu, M., Brudno, M.: Computational methods for discovering structural variation with next-generation sequencing. Nat. Methods 6, S13–S20 (2009)Google Scholar
  5. 5.
    Li, H., Durbin, R.: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Korean Bioinformatoin Center (KOBIC)DaejeonSouth Korea
  2. 2.Korea Institute of Science and Technology Information (KISTI)DaejeonSouth Korea

Personalised recommendations