Abstract
Copy number variation (CNV) is a well-known type of genomic mutation that is associated with the development of human cancer diseases. Detection of CNVs from the human genome is a crucial step for the pipeline of starting from mutation analysis to cancer disease diagnosis and treatment. Next-generation sequencing (NGS) data provides an unprecedented opportunity for CNVs detection at the base-level resolution, and currently, many methods have been developed for CNVs detection using NGS data. However, due to the intrinsic complexity of CNVs structures and NGS data itself, accurate detection of CNVs still faces many challenges. In this paper, we present an alternative method, called KNNCNV (K-Nearest Neighbor based CNV detection), for the detection of CNVs using NGS data. Compared to current methods, KNNCNV has several distinctive features: 1) it assigns an outlier score to each genome segment based solely on its first k nearest-neighbor distances, which is not only easy to extend to other data types but also improves the power of discovering CNVs, especially the local CNVs that are likely to be masked by their surrounding regions; 2) it employs the variational Bayesian Gaussian mixture model (VBGMM) to transform these scores into a series of binary labels without a user-defined threshold. To evaluate the performance of KNNCNV, we conduct both simulation and real sequencing data experiments and make comparisons with peer methods. The experimental results show that KNNCNV could derive better performance than others in terms of F1-score.
Highlights
Copy number variations (CNVs) of DNA sequences are accountable for functional phenotypic diversity in many species and play an important role in human genomic variation and cancer initiation (Schrider et al, 2013; Unckless et al, 2016)
The average value of these distances is regarded as the outlier score of the genome segment, which is easy to extend to other data types and boosts the power of detection CNVs, especially the local CNVs that are likely to be masked by their surrounding regions
TP denotes the number of duplicate genomic positions between the declared CNVs and confirmed CNVs, and PP represents the total number of genomic positions in the declared CNVs, and P is the total number of positions in the confirmed CNVs
Summary
Copy number variations (CNVs) of DNA sequences are accountable for functional phenotypic diversity in many species and play an important role in human genomic variation and cancer initiation (Schrider et al, 2013; Unckless et al, 2016). CNV is a commonly reported variation from the diploid state caused by amplification or deletion of genomic regions ranging from one kilo-base to several mega-bases (Redon et al, 2006; Li et al, 2020). Tumor-derived CNVs are one of the most significant genomic anomalies, alongside somatic mutations and structural variations (SVs). Tumor suppressor gene inactivation or oncogene activation are frequently ascribed to copy number loss or gain, respectively (Yuan et al, 2012). Gains may contain oncogenes, and losses may include tumor-suppressor genes (Xie et al, 2021).
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