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Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data.

Shifu Chen1, Ming Liu2, Yanqing Zhou2

  • 1HaploX Biotechnology, Nanshan District, Shenzhen, Guangdong, China. chen@haplox.com.

Methods in Molecular Biology (Clifton, N.J.)
|March 15, 2018
PubMed
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Analyzing cell-free tumor DNA (ctDNA) using next-generation sequencing (NGS) presents challenges in detecting low mutated allele frequency (MAF) variations. This chapter reviews ctDNA analysis difficulties and introduces novel bioinformatics methods to improve NGS data interpretation.

Keywords:
CNVCirculating tumor DNAGene fusionLiquid biopsyMutation visualizationOpenGenectDNA

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Area of Science:

  • Biomolecular analysis
  • Genomics
  • Bioinformatics

Background:

  • Cell-free tumor DNA (ctDNA) is a key liquid biopsy biomarker, offering comprehensive tumor genetic information and overcoming tissue biopsy limitations.
  • Next-generation sequencing (NGS) is a prevalent technology for ctDNA analysis, but detecting low mutated allele frequency (MAF) variants in noisy data remains difficult.

Purpose of the Study:

  • To elucidate the challenges inherent in analyzing ctDNA sequencing data.
  • To review existing technologies relevant to ctDNA analysis.
  • To present novel bioinformatics methodologies for enhanced ctDNA NGS data interpretation.

Main Methods:

  • Review of current ctDNA processing technologies.
  • Explanation of difficulties in low MAF variant detection from noisy sequencing data.
  • Introduction of novel bioinformatics approaches for ctDNA NGS data analysis.

Main Results:

  • Identification of key challenges in ctDNA NGS data analysis, particularly for low MAF variants.
  • Overview of established and emerging technologies for ctDNA sample processing.
  • Presentation of new bioinformatics tools designed to improve the accuracy and sensitivity of ctDNA variant detection.

Conclusions:

  • Despite mature ctDNA processing technologies, accurate detection of low MAF variants from noisy NGS data remains a significant challenge.
  • Novel bioinformatics methods are crucial for overcoming these challenges and improving the utility of ctDNA in clinical applications.
  • Enhanced analysis of ctDNA NGS data holds promise for advancing liquid biopsy and personalized medicine.