Noise cancellation for robust copy number variation detection using next generation sequencing data

Zare, Fatima; Ansari, Sardar; Najarian, Kayvan; Nabavi, Sheida

doi:10.1109/bibm.2017.8217654

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Preprocessing read-count data.-It has been shown that normalization and denoising read-count data can significantly increase the accuracy of CNV detection [34,36,38]. We developed a preprocessing method which consists of two parts to increase the detection accuracy of the proposed segmentation algorithm.…”

Section: 14mentioning

confidence: 99%

Copy Number Variation Detection Using Total Variation

Zare

Nabavi

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For highthroughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.

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Section: 14mentioning

confidence: 99%

Copy Number Variation Detection Using Total Variation

Zare

Nabavi

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Copy number variation detection using partial alignment information

Zare

Ansari

Najarian

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Noise cancellation using total variation for copy number variation detection

2018

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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.

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Noise cancellation for robust copy number variation detection using next generation sequencing data

Cited by 3 publications

References 36 publications

Copy Number Variation Detection Using Total Variation

Copy Number Variation Detection Using Total Variation

Copy number variation detection using partial alignment information

Noise cancellation using total variation for copy number variation detection

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