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Detection of Copy Number Alterations Using Single Cell Sequencing
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A Pipeline for Reconstructing Somatic Copy Number Alternation's Subclonal Population-Based Next-Generation Sequencing

Yanshuo Chu1, Chenxi Nie1, Yadong Wang1

  • 1Center of Bioinfomatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Frontiers in Genetics
|March 18, 2020
PubMed
Summary

Next-generation sequencing (NGS) subclonal reconstruction for somatic copy number alternations (SCNAs) is improved by a new computational pipeline. This method enhances accuracy and efficiency in analyzing tumor sequencing data.

Keywords:
absolute copy numberbias correctionsomatic copy number alternationsubclonal frequencysubclonal reconstruction

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) based subclonal reconstruction methods struggle with somatic copy number alternations (SCNAs).
  • Existing methods face challenges due to simultaneous estimation requirements for subclonal frequency and absolute copy number, alongside complex biases and noise in sequencing data.
  • The read count on radio (RCR) metric, commonly used, is sensitive to sequencing errors and biases, leading to inaccurate SCNA segmentation and inefficient downstream analysis.

Purpose of the Study:

  • To develop an improved computational pipeline for subclonal reconstruction of somatic copy number alternations (SCNAs).
  • To address the limitations of existing methods by reducing the impact of false breakpoints and correcting for RCR bias.
  • To enhance the accuracy and efficiency of inferring subclonal population frequencies and reconstructing SCNA profiles from tumor sequencing data.

Main Methods:

  • Mathematical analysis of SCNA subclonal frequency solution space to develop a computational algorithm for reducing false breakpoints.
  • Construction of a novel probability model incorporating a read count on radio (RCR) bias correction algorithm.
  • Integration of the RCR bias correction and false breakpoint filtering algorithms into a comprehensive SCNA subclonal population reconstruction pipeline.

Main Results:

  • The developed pipeline demonstrates superior performance compared to existing subclonal reconstruction programs.
  • Outperformance was validated on both simulated datasets and real-world The Cancer Genome Atlas (TCGA) data.
  • The pipeline effectively mitigates issues related to RCR bias and false breakpoints, leading to more accurate SCNA subclonal reconstruction.

Conclusions:

  • The proposed computational pipeline offers a significant advancement in SCNA subclonal reconstruction using NGS data.
  • The method provides a more accurate and efficient approach to analyzing tumor heterogeneity and copy number alterations.
  • The publicly available Python package facilitates broader adoption and further research in the field.