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.
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Acknowledgments
We thank the anonymous reviewers for their suggestions that contributed to improving the manuscript. And we thank the MessAge group and the Bioinformatics Core Unit at IMB for providing extra computational resources when they were needed.
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This work has been supported by the German Research Foundation (DFG) Grant RO3500/4-1 within the Research Unit FOR 1961 and by the German Federal Ministry of Research and Education, Grant 031A424 “HaematoOPT”.
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Kuhn, M., Stange, T., Herold, S. et al. Finding small somatic structural variants in exome sequencing data: a machine learning approach. Comput Stat 33, 1145–1158 (2018). https://doi.org/10.1007/s00180-016-0674-2
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DOI: https://doi.org/10.1007/s00180-016-0674-2