Generating simulated SNP array and sequencing data to assess genomic segmentation algorithms

Mark R. Zucker, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We developed a tool, implemented in an R package called true and accurate clone generator (TACG), to simulate ‘ground truth’ and realistic SNP array and single nucleotide variant (SNV) data. We present TACG and use it to assess several different approaches to segmentation of copy number data from SNP arrays, with a particular interest in detecting copy number variations (CNVs) in cancer samples. We demonstrate that DNAcopy, an algorithm using circular binary segmentation, generally performs best, which is in agreement with previous research. We determine the conditions under which it and other methods break down. In particular, we assess how characteristics like clonal heterogeneity, presence of nested CNVs, and the type of aberration affect algorithm accuracy. The simulations we generated proved to be useful in determining not just the comparative overall accuracy of different algorithms, but also in determining how their efficacy is affected by the biological characteristics of samples from which the data was generated.

Original languageEnglish (US)
Pages (from-to)438-453
Number of pages16
JournalInternational Journal of Computational Biology and Drug Design
Issue number5-6
StatePublished - 2020
Externally publishedYes


  • Algorithms
  • Cancer
  • Circular binary segmentation
  • Copy number variation
  • Genomics
  • Hidden Markov Models
  • Segmentation
  • Simulations
  • SNP arrays
  • Whole exome sequencing

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications


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