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Inferring Variation in Copy Number Using High Throughput Sequencing Data in R.

Brian J Knaus1, Niklaus J Grünwald1

  • 1Horticultural Crops Research Unit, United States Department of Agriculture-Agricultural Research Service, Corvallis, OR, United States.

Frontiers in Genetics
|May 1, 2018
PubMed
Summary

We developed a new method to infer copy number variation using variant call format (VCF) data. This approach, implemented in the R package vcfR, does not require prior knowledge of genome ploidy.

Keywords:
PhytophthoraR packagebioinformaticscomputational biologycopy number variation (CNV)high throughput sequencing (HTS)ploidy

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Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Inferring copy number variation (CNV) is challenging as existing methods often require pre-defined copy numbers.
  • Variant callers typically need a known genome or genomic region copy number to function.
  • This limitation hinders accurate CNV analysis in diverse organisms.

Purpose of the Study:

  • To present a novel method for inferring copy number variation directly from variant call format (VCF) data.
  • To implement this method within the R package vcfR for broader accessibility.
  • To provide a flexible tool for investigating CNV without assuming base ploidy.

Main Methods:

  • The method utilizes the relative frequencies of alleles sequenced at heterozygous positions across the genome.
  • Allele frequencies are summarized using arbitrarily sized windows and binned to identify the most abundant frequency.
  • This non-parametric approach is applicable to reference genomes with chromosomes or contigs.

Main Results:

  • The method successfully infers copy number without relying on prior ploidy assumptions.
  • Validation was performed using Saccharomyces cerevisiae and Phytophthora infestans, organisms known for CNV.
  • The functionality is integrated into the vcfR R package.

Conclusions:

  • The developed method offers a robust and flexible approach to inferring copy number variation from VCF data.
  • Its ability to infer ploidy makes it a valuable tool for genomic projects involving organisms with variable copy numbers.
  • The integration into vcfR enhances its utility for researchers in genomics and bioinformatics.