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Benchmarking Ploidy Estimation Methods for Bulk and Single-Cell Whole Genome Sequencing.

Yawei Song1, Zilv Mei1, Qijie Zheng1

  • 1Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|September 8, 2025
PubMed
Summary

This study benchmarks computational tools for estimating cellular ploidy from whole-genome sequencing (WGS) data. PURPLE excels for bulk WGS with high tumor purity, while SeCNV leads for single-cell WGS ploidy detection.

Keywords:
aneuploidyploidy estimationsingle‐cell DNA sequencingtumor puritywhole genome sequencing

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Cellular ploidy is crucial for physiological processes, but alterations (aneuploidy, polyploidy) are linked to development, regeneration, and tumor evolution.
  • Numerous computational tools exist for ploidy estimation from whole-genome sequencing (WGS) data at bulk and single-cell levels.
  • A systematic comparison of these ploidy estimation tools is lacking.

Purpose of the Study:

  • To benchmark the performance of existing computational tools for estimating cellular ploidy from bulk and single-cell WGS data.
  • To evaluate factors influencing tool performance, including tumor purity, sequencing platform, and preprocessing steps.
  • To identify the most accurate tools for ploidy analysis in different WGS contexts.

Main Methods:

  • Conducted a benchmarking study comparing 11 bulk WGS and 8 single-cell WGS ploidy estimation methods.
  • Utilized experimental and simulated datasets comprising diploid cells mixed with aneuploid or polyploid cells.
  • Assessed tool performance based on accuracy in estimating tumor purity, ploidy, and copy number variations.

Main Results:

  • For bulk WGS, PURPLE demonstrated superior performance when tumor purity exceeded 30%, irrespective of sequencing depth or platform.
  • Existing tools showed limitations with euploid samples and long-read sequencing data.
  • For single-cell WGS, SeCNV emerged as the top-performing method for ploidy detection accuracy.

Conclusions:

  • The study provides a comprehensive performance evaluation of WGS ploidy estimation tools.
  • Findings offer guidance for selecting appropriate tools for bulk and single-cell ploidy analysis.
  • Highlights the need for further development of computational tools, particularly for euploid samples and long-read sequencing data.