BreakDancer: An algorithm for high-resolution mapping of genomic structural variation

Ken Chen, John W. Wallis, Michael D. McLellan, David E. Larson, Joelle M. Kalicki, Craig S. Pohl, Sean D. McGrath, Michael Wendl, Qunyuan Zhang, Devin P. Locke, Xiaoqi Shi, Robert Fulton, Timothy Ley, Richard K. Wilson, Li Ding, Elaine R. Mardis

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Abstract

Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancer's performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.

Original languageEnglish
Pages (from-to)677-681
Number of pages5
JournalNature Methods
Volume6
Issue number9
DOIs
StatePublished - Aug 10 2009

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    Chen, K., Wallis, J. W., McLellan, M. D., Larson, D. E., Kalicki, J. M., Pohl, C. S., McGrath, S. D., Wendl, M., Zhang, Q., Locke, D. P., Shi, X., Fulton, R., Ley, T., Wilson, R. K., Ding, L., & Mardis, E. R. (2009). BreakDancer: An algorithm for high-resolution mapping of genomic structural variation. Nature Methods, 6(9), 677-681. https://doi.org/10.1038/nmeth.1363