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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Related Experiment Video

Updated: Sep 3, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing.

Hana Rozhoňová1,2,3, Daniel Danciu1,4, Stefan Stark1,3,4

  • 1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland.

Bioinformatics (Oxford, England)
|July 28, 2022
PubMed
Summary
This summary is machine-generated.

We developed SECEDO, a novel method for clustering tumor cells using single-nucleotide variants from ultra-low coverage DNA sequencing. SECEDO accurately reconstructs tumor subclone composition and enhances variant detection sensitivity.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Single-cell DNA sequencing technologies allow whole-genome analysis of thousands of cells.
  • Ultra-low coverage (<0.05× per cell) data primarily enables identification of large copy number alterations.
  • Single-nucleotide variant (SNV) detection is crucial for understanding intra-tumor heterogeneity in non-copy number-driven tumors.

Purpose of the Study:

  • To develop a method for clustering tumor cells based on SNVs from ultra-low coverage single-cell DNA sequencing data.
  • To enable more comprehensive analysis of intra-tumor heterogeneity by focusing on SNVs.
  • To improve the sensitivity and confidence of SNV detection within tumor subclones.

Main Methods:

  • Developed Single Cell Data Tumor Clusterer (SECEDO), a Bayesian filtering approach.
  • SECEDO exploits read overlap and phasing for efficient clustering of tumor cells based on SNVs.
  • Applied SECEDO to both synthetic and real single-cell sequencing datasets.

Main Results:

  • SECEDO accurately reconstructed clonal composition in a synthetic dataset of 7250 cells, detecting 92.11% of somatic SNVs.
  • Applied to real breast cancer data (≈2000 cells/dataset), SECEDO achieved an Adjusted Rand Index (ARI) of ≈0.6 at 0.03× coverage.
  • SECEDO outperformed state-of-the-art methods, which required significantly increased coverage and data pooling to achieve comparable results.
  • Variant calling on SECEDO-identified clusters more than doubled SNV detection sensitivity and increased variant confidence.

Conclusions:

  • SECEDO enables robust tumor subclone detection and characterization using SNVs from ultra-low coverage single-cell DNA sequencing.
  • The method significantly enhances the sensitivity and confidence of SNV identification within distinct tumor subclones.
  • SECEDO provides a powerful tool for a more comprehensive understanding of intra-tumor heterogeneity.