Abstract
The Next-generation sequencing technology is a widely used sequencing method, and many genome researches are based on its sequencing data. Currently, there are many methods of detection of genomic Structure Variation, based on NGS data. And a lot of Copy Number Variation (CNV) detection methods based on the statistical models of read depth. However, since CNV has multiple subtypes and long variant lengths, the traditional detection tools have many limitations. Therefore, this paper proposes AssCNV, a new detection method for CNV, which integrated sequence assembly strategy and read depth strategy. The subtypes of CNV considered in this paper are insertion, deletion, and duplication. Our experimental results showed that AssCNV maintains a higher level of precision and sensitivity in the simulation data of different coverage, which is much better than other available tools.
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Acknowledgement
Project supported by the National Natural Science Foundation of China (Grant No. 61472026) and Beijing Natural Science Foundation (5182018).
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Liu, W., Gao, J. (2018). An Integrated Method of Detecting Copy Number Variation Based on Sequence Assembly. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_58
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DOI: https://doi.org/10.1007/978-3-319-95930-6_58
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