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PureCN: copy number calling and SNV classification using targeted short read sequencing.

Markus Riester1, Angad P Singh1, A Rose Brannon1

  • 1Novartis Institutes for BioMedical Research, Cambridge, MA USA.

Source Code for Biology and Medicine
|December 22, 2016
PubMed
Summary

A new algorithm accurately classifies genetic variants from tumor samples without normal tissue. This method estimates tumor purity and copy number, aiding in variant classification for cancer research.

Keywords:
Cell linesCopy numberHeterogeneityHybrid captureLoss of heterozygosityPloidyPurityWhole exome sequencing

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Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Computational Biology

Background:

  • Distinguishing somatic from germline variants is crucial for cancer diagnostics.
  • Current molecular diagnostic assays often sequence tumors only, lacking matched normal tissue.
  • A reliable algorithm is needed to classify variants in unmatched tumor sequencing data for retrospective analyses.

Purpose of the Study:

  • To develop and validate a computational method for analyzing unmatched tumor sequencing data.
  • To accurately estimate tumor characteristics like purity, ploidy, and copy number.
  • To classify single nucleotide variants (SNVs) as somatic or germline.

Main Methods:

  • Developed an R package named PureCN for analyzing targeted short-read sequencing data.
  • The methodology adjusts for local copy number to infer genotypes from allelic fractions.
  • The software supports both matched and unmatched tumor samples and integrates with existing pipelines.

Main Results:

  • PureCN accurately estimates tumor purity, copy number, loss of heterozygosity (LOH), and contamination.
  • The algorithm successfully classifies single nucleotide variants (SNVs) by somatic status and clonality.
  • Accuracy was validated using both simulated and real whole-exome sequencing data.

Conclusions:

  • The PureCN algorithm provides accurate tumor purity and ploidy estimates without matched normal samples.
  • This enables reliable classification of SNVs, significantly aiding cancer variant interpretation.
  • The open-source R/Bioconductor package PureCN is available for broad research use.