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Finding small somatic structural variants in exome sequencing data: a machine learning approach

Author

Listed:
  • Matthias Kuhn

    (Technische Universität)

  • Thoralf Stange

    (Technische Universität)

  • Sylvia Herold

    (Universitätsklinikum der Technischen Universität
    Deutsches Krebsforschungszentrum)

  • Christian Thiede

    (Universitätsklinikum der Technischen Universität
    Deutsches Krebsforschungszentrum)

  • Ingo Roeder

    (Technische Universität)

Abstract

Genetic variation forms the basis for diversity but can as well be harmful and cause diseases, such as tumors. Structural variants (SV) are an example of complex genetic variations that comprise of many nucleotides ranging up to several megabases. Based on recent developments in sequencing technology it has become feasable to elucidate the genetic state of a person’s genes (i.e. the exome) or even the complete genome. Here, a machine learning approach is presented to find small disease-related SVs with the help of sequencing data. The method uses differences in characteristics of mapping patterns between tumor and normal samples at a genomic locus. This way, the method aims to be directly applicable for exome sequencing data to improve detection of SVs since specific SV detection methods are currently lacking. The method has been evaluated based on a simulation study as well as with exome data of patients with acute myeloid leukemia. An implementation of the algorithm is available at https://github.com/lenz99-/svmod .

Suggested Citation

  • Matthias Kuhn & Thoralf Stange & Sylvia Herold & Christian Thiede & Ingo Roeder, 2018. "Finding small somatic structural variants in exome sequencing data: a machine learning approach," Computational Statistics, Springer, vol. 33(3), pages 1145-1158, September.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:3:d:10.1007_s00180-016-0674-2
    DOI: 10.1007/s00180-016-0674-2
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    Cited by:

    1. Hans A. Kestler & Bernd Bischl & Matthias Schmid, 2018. "Proceedings of Reisensburg 2014–2015," Computational Statistics, Springer, vol. 33(3), pages 1125-1126, September.

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