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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Abstract

Genotyping of structural variations considering copy number variations (CNVs) is an infancy and challenging problem. CNVs, a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells, often affect transcription and contribute to a variety of diseases. The characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms, which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream analysis. As the allelic copy number increases, the error rate of genotyping increases sharply. Some instances with different copy numbers play an auxiliary role in the genotyping classification problem, but some will seriously interfere with the accuracy of the model. Motivated by these, we propose a transfer learning-based method to genotype structural variations accurately considering CNVs. The method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype datasets. It maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct genotyping. By adjusting the weights of the instances with different allelic copy numbers, the contribution of all the instances to genotyping can be maximized, and the genotyping errors of heterozygote variations caused by CNVs can be minimized. We applied the proposed method to both the simulated and real datasets, and compared it to some popular algorithms including GATK, Facets and Gindel. The experimental results demonstrate that the proposed method outperforms the others in terms of accuracy, stability and efficiency. The source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant No. 31701150) and the Fundamental Research Funds for the Central Universities (CXTD2017003).

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Authors and Affiliations

  1. Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Tian Zheng, Xinyang Qian & Jiayin Wang

  2. Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, 710049, China

    Tian Zheng, Xinyang Qian & Jiayin Wang

Authors
  1. Tian Zheng
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  2. Xinyang Qian
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  3. Jiayin Wang
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Correspondence to Jiayin Wang.

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Supporting information

The supporting information is available online at journal.hep.com.cn and link.springer.com.

Tian Zheng is currently a PhD candidate in the School of Computer Science and Technology, Xi’an Jiaotong University, China. Her current research interests include bioinformatics, machine learning and data mining.

Xinyang Qian is studying for his master’s degree in the School of Computer Science and Technology, Xi’an Jiaotong University, China. His current research interests include bioinformatics and machine learning.

Jiayin Wang is a Professor in the School of Computer Science and Technology, Xi’an Jiaotong University, China. His current research interests include cancer genomics and bioinformatics.

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Zheng, T., Qian, X. & Wang, J. A structural variation genotyping algorithm enhanced by CNV quantitative transfer. Front. Comput. Sci. 16, 166905 (2022). https://doi.org/10.1007/s11704-021-1177-z

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  • DOI: https://doi.org/10.1007/s11704-021-1177-z

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