A study of biases of DNA copy number estimation based on PICR model

Abstract

Affymetrix single-nucleotide polymorphism (SNP) arrays have been widely used for SNP genotype calling and copy number variation (CNV) studies, both of which are dependent on accurate DNA copy number estimation significantly. However, the methods for copy number estimation may suffer from kinds of difficulties: probe dependent binding affinity, crosshybridization of probes, and the whole genome amplification (WGA) of DNA sequences. The probe intensity composite representation (PICR) model, one former established approach, can cope with most complexities and achieve high accuracy in SNP genotyping. Nevertheless, the copy numbers estimated by PICR model still show array and site dependent biases for CNV studies. In this paper, we propose a procedure to adjust the biases and then make CNV inference based on both PICR model and our method. The comparison indicates that our correction of copy numbers is necessary for CNV studies.

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

“…Although the RD-based approach has to correct distortions caused by NGS biases, the relationship between the read count and true copy number can be distorted by several effects. The PCR process is known to be one major cause of this distortion, where genomic fragments with a lower PCR amplification rate often result in less reads [ 69 , 70 ]. Furthermore, sequencing process can also introduce system noise [ 55 , 71 ].…”