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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Noise cancellation using total variation for copy number variation detection.

Fatima Zare1, Abdelrahman Hosny2, Sheida Nabavi3

  • 1Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA. fatemeh.zare@uconn.edu.

BMC Bioinformatics
|October 23, 2018
PubMed
Summary

We developed a novel denoising method, Taut String, to improve copy number variation (CNV) detection from next-generation sequencing data. This method enhances accuracy by mitigating noise and biases, leading to more precise identification of CNVs.

Keywords:
Copy number variationDenoisingNext generation sequencingSignal processingTaut stringTotal variation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Signal Processing

Background:

  • Next-generation sequencing (NGS) enables copy number variation (CNV) detection.
  • Read depth (RD) methods are common for CNV identification but are affected by noise and biases.
  • Accurate CNV detection requires mitigation of these data distortions.

Purpose of the Study:

  • To propose a novel denoising method for precise and efficient sequence-based CNV detection.
  • To address limitations of existing methods in identifying CNVs due to noise and biases.
  • To enhance the accuracy of CNV segmentation algorithms.

Main Methods:

  • Developed a denoising method based on total variation and the Taut String algorithm.
  • Applied signal processing techniques to mitigate noise and biases in read count data.
  • Compared Taut String with Moving Average (MA) and Discrete Wavelet Transforms (DWT).

Main Results:

  • Taut String demonstrated higher sensitivity and lower false discovery rates compared to MA and DWT.
  • The method showed improved detection of narrow CNVs and preservation of breakpoints.
  • Performance was validated using both simulated and real sequencing data.

Conclusions:

  • A novel denoising method, Taut String, significantly enhances CNV detection accuracy.
  • Effective denoising is crucial for improving CNV segmentation algorithms.
  • Non-linear denoising methods considering sparsity and piecewise constant characteristics are beneficial for CNV detection.