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 C. Wendl; Qunyuan Zhang; Devin P. Locke; Xiaoqi Shi; Robert S. Fulton; Timothy J. Ley; Richard K. Wilson; Li Ding; Elaine R. Mardis

doi:10.1038/nmeth.1363

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 C. Wendl, Qunyuan Zhang, Devin P. Locke, Xiaoqi Shi, Robert S. Fulton, Timothy J. Ley, Richard K. Wilson, Li Ding, Elaine R. Mardis

Research output: Contribution to journal › Article › peer-review

1109 Scopus citations

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 language	English (US)
Pages (from-to)	677-681
Number of pages	5
Journal	Nature Methods
Volume	6
Issue number	9
DOIs	https://doi.org/10.1038/nmeth.1363
State	Published - 2009
Externally published	Yes

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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10.1038/nmeth.1363

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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. C., Zhang, Q., Locke, D. P., Shi, X., Fulton, R. S., Ley, T. J., 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

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title = "BreakDancer: An algorithm for high-resolution mapping of genomic structural variation",

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.",

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

note = "Funding Information: We thank the Genomics of AML Program Project Grant team at Washington University Medical School (US National Cancer Institute PO1 CA101937; principal investigagor, T.J.L.) and the 1,000 Genomes Consortium members for providing the data. We thank members of the 1,000 Genomes structural variation group and H. Li for methodology discussions; D. Bentley and M. Ross (Illumina), C. Alkan and J. Kidd (University of Washington), and Y. Li and H. Zheng (Beijing Genome Institute) for providing validation data; and A. Chinwalla, D. Dooling, S. Smith, J. Eldred, C. Harris, L. Cook, V. Magrini, Y. Tang, H. Schmidt, C. Haipek, G. Elliott and R. Abbott for assistance. This work was supported by the National Human Genome Research Institute (HG003079; principal investigator, R.K.W.).",

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AU - Kalicki, Joelle M.

AU - Pohl, Craig S.

AU - McGrath, Sean D.

AU - Wendl, Michael C.

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AU - Locke, Devin P.

AU - Shi, Xiaoqi

AU - Fulton, Robert S.

AU - Ley, Timothy J.

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AU - Ding, Li

AU - Mardis, Elaine R.

N1 - Funding Information: We thank the Genomics of AML Program Project Grant team at Washington University Medical School (US National Cancer Institute PO1 CA101937; principal investigagor, T.J.L.) and the 1,000 Genomes Consortium members for providing the data. We thank members of the 1,000 Genomes structural variation group and H. Li for methodology discussions; D. Bentley and M. Ross (Illumina), C. Alkan and J. Kidd (University of Washington), and Y. Li and H. Zheng (Beijing Genome Institute) for providing validation data; and A. Chinwalla, D. Dooling, S. Smith, J. Eldred, C. Harris, L. Cook, V. Magrini, Y. Tang, H. Schmidt, C. Haipek, G. Elliott and R. Abbott for assistance. This work was supported by the National Human Genome Research Institute (HG003079; principal investigator, R.K.W.).

PY - 2009

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AB - 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.

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

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