Noise cancellation using total variation for copy number variation detection

Abstract

BackgroundDue to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data’s noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm.ResultsTo investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments’ breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates.ConclusionsIn this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

“…13 – 17 ). Copy number calling has been reported to be noisy for several data types 27 , 28 , and we observed that quantitative comparisons between CNA profiles are sensitive to: (1) the thresholds and baselines used to define gains and losses; (2) the dynamic range of copy number values from each platform; and (3) the differential impacts of normal cell contamination for different measurements. To control for such systematic biases, we assessed the similarity between two CNA profiles using the Pearson correlation of their log 2 [copy number ratio] values across the genome in 100-kilobase (kb) windows.…”

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

“…13 – 17). Copy number calling has been reported to be noisy for several data types 39,40 , and we observed that quantitative comparisons between CNA profiles are sensitive to: (1) the thresholds and baselines used to define gains and losses, (2) the dynamic range of copy number values from each platform, and (3) the differential impacts of normal cell contamination for different measurements. To control for such systematic biases, we assessed the similarity between two CNA profiles using the Pearson correlation of their log 2 (CN ratio) values across the genome in 100kb windows.…”

Development and Validation of a 34-Gene Inherited Cancer Predisposition Panel Using Next-Generation Sequencing

“…However, accurate CNV detection in targeted NGS data remains challenging. Bias and noise in NGS coverage data, derived from various sources during library preparation, capture, and sequencing, distort the association between copy numbers and read coverages [38,39]. Despite the excellent sensitivity and specificity of our CNV flagging algorithm, we pseudogene specific variants according to the reference sequence are also found in real gene, and vice versa [36].…”

“…Further optimization of NGS library preparation procedures, such as using normalized high-quality DNA for library preparation, may improve coverage uniformity and, therefore, CNV specificity. In addition, employing recently described denoising methods based on a signal processing technique may enhance the detection accuracy of our CNV algorithm [38]. Another factor that may confound accurate CNV analysis is a processed pseudogene if its existence is unknown.…”