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forestSV: structural variant discovery through statistical learning

Nature Methods volume 9, pages 819–821 (2012)Cite this article

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Abstract

Detecting genomic structural variants from high-throughput sequencing data is a complex and unresolved challenge. We have developed a statistical learning approach, based on Random Forests, that integrates prior knowledge about the characteristics of structural variants and leads to improved discovery in high-throughput sequencing data. The implementation of this technique, forestSV, offers high sensitivity and specificity coupled with the flexibility of a data-driven approach.

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Figure 1: The forestSV framework.
Figure 2: Mapping of features to structural variant calls by Random Forests (RF).

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References

  1. Sebat, J. et al. Science 305, 525–528 (2004).

    Article  CAS  Google Scholar 

  2. Iafrate, A.J. et al. Nat. Genet. 36, 949–951 (2004).

    Article  CAS  Google Scholar 

  3. Stankiewicz, P. & Lupski, J.R. Annu. Rev. Med. 61, 437–455 (2010).

    Article  CAS  Google Scholar 

  4. Sebat, J., Levy, D.L. & McCarthy, S.E. Trends Genet. 25, 528–535 (2009).

    Article  CAS  Google Scholar 

  5. Yoon, S., Xuan, Z., Makarov, V., Ye, K. & Sebat, J. Genome Res. 19, 1586–1592 (2009).

    Article  CAS  Google Scholar 

  6. Abyzov, A., Urban, A.E., Snyder, M. & Gerstein, M. Genome Res. 21, 974–984 (2011).

    Article  CAS  Google Scholar 

  7. Handsaker, R.E., Korn, J.M., Nemesh, J. & McCarroll, S.A. Nat. Genet. 43, 269–276 (2011).

    Article  CAS  Google Scholar 

  8. Chen, K. et al. Nat. Methods 6, 677–681 (2009).

    Article  CAS  Google Scholar 

  9. Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. Bioinformatics 25, 2865–2871 (2009).

    Article  CAS  Google Scholar 

  10. Mills, R.E. et al. Nature 470, 59–65 (2011).

    Article  CAS  Google Scholar 

  11. Breiman, L. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  12. 1000 Genomes Project Consortium. Nature 467, 1061–1073 (2010).

  13. Conrad, D.F. et al. Nature 464, 704–712 (2010).

    Article  CAS  Google Scholar 

  14. McCarroll, S.A. et al. Nat. Genet. 40, 1166–1174 (2008).

    Article  CAS  Google Scholar 

  15. Malhotra, D. & Sebat, J. Cell 148, 1223–1241 (2012).

    Article  CAS  Google Scholar 

  16. Li, H. et al. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  17. Bentley, D.R. et al. Nature 456, 53–59 (2008).

    Article  CAS  Google Scholar 

  18. Liaw, A. & Wiener, M. R News 2, 18–22 (2002).

    Google Scholar 

Download references

Acknowledgements

This work was performed under US National Institutes of Health grants HG005725 and MH076431 and with support from the Beyster Family Foundation. We also thank the 1000 Genomes Project for access to data and J. Wang, H. Zheng, Y. Li, X. Jin and Y. Shi from BGI-Shenzhen for their roles in producing the unpublished autism sequencing data.

Author information

Authors and Affiliations

  1. Beyster Center for Molecular Genomics of Neuropsychiatric Diseases, University of California, San Diego, La Jolla, California, USA.,

    Jacob J Michaelson & Jonathan Sebat

  2. Department of Psychiatry, University of California, San Diego, La Jolla, California, USA.,

    Jacob J Michaelson & Jonathan Sebat

  3. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California, USA

    Jonathan Sebat

Authors
  1. Jacob J Michaelson
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  2. Jonathan Sebat
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Contributions

J.J.M. conceived of and implemented forestSV. J.J.M. and J.S. wrote the manuscript.

Corresponding author

Correspondence to Jonathan Sebat.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14, Supplementary Tables 1 and 2 and Supplementary Results (PDF 2737 kb)

Supplementary Data 1

Genomic regions used for training (TXT 13315 kb)

Supplementary Data 2

Structural variant calls produced by forestSV in NA12878, NA12891, NA12892, NA19240, NA19238 and NA19239 (TXT 18421 kb)

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Michaelson, J., Sebat, J. forestSV: structural variant discovery through statistical learning. Nat Methods 9, 819–821 (2012). https://doi.org/10.1038/nmeth.2085

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