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Comparison of variant calling pipelines using Illumina CanineHD BeadChip array as the truth dataset

ABSTRACT: Next generation sequencing platforms have become essential tools for understanding DNA in a wide range of contexts. Their success heavily relies on the accuracy, sensitivity and specificity of methods used to discern differences between the reference genome and genomes under investigation. Here we compare the relative performances of five popular single nucleotide variant callers with and without their associated recommended hard filtering criteria. We compare: FreeBayes; the Genome Analysis Toolkit’s Haplotype Caller and Unified Genotyper; SAMtools; and VarScan. We tailor this comparison to suit smaller projects with modest sample numbers (n = 10) and coverage (~10X) to fill a current gap in the literature. Other comparison studies are generally applicable only to larger projects in model species, where there is access to large amounts of sequencing data and curated callsets for base and variant quality score recalibration. We estimated the accuracy, sensitivity and specificity of each pipeline according to the genotype concordance rate and number with the “truth” dataset for 10 canine samples. The truth dataset was defined as genotypes obtained from the CanineHD BeadChip array. Whole genome sequencing data was performed on the Illumina HiSeq2000 or HiSeq2500 platform as 100-101 base pair, paired end reads to an average sample coverage of 10.3X. Apart from GATK Haplotype Caller, applying recommended hard filters did not improve the performance of genotyping concordance at the tested levels of minimum coverage. The default VarScan pipeline with no additional filters applied (VarScan uses SAMtools mpileup, without base alignment quality computation) generally outperformed other callers in terms of accuracy, sensitivity and specificity. The results of this study demonstrate that hard filtering of variant calls from low-powered genome studies can impair accuracy, sensitivity and specificity of callsets and provides some benchmark performance metrics on a range of low coverage levels.

ORGANISM(S): Canis lupus familiaris

PROVIDER: GSE117010 | GEO | 2018/08/31

REPOSITORIES: GEO

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Project description:The accurate mapping of recurring DNA copy number aberrations (CNAs), a hallmark feature of the cancer genome, has facilitated the discovery of tumor suppressor genes and oncogenes. Microarray-based assays designed to detect these chromosomal copy number alterations on a genome-wide and high-resolution scale have emerged as a cornerstone technology in the genomic era. The diversity of commercially-available platforms prompted a systematic comparison of five copy number profiling assays for their ability to detect 2-fold copy number gain and loss (4n or 1n, respectively) as well as focal high-amplitude CNAs. Here, using a collection of established human melanoma cell lines, we defined the reproducibility, absolute signals, signal:noise, false-positive and false-negative rates for each of the five assays against ground-truth defined by Spectral Karyotyping (SKY), in addition to comparing the concordance of CNAs detection by two high-resolution Agilent and Affymetrix microarray platforms. Our analyses concluded that the Agilentâs 60mer oligo-microarray with probe design optimized for genomic hybridization offers the highest sensitivity and specificity [area under Receiver Operator Characteristic (ROC) curve >0.99] while Affymetrixâs SNP microarray appears to offer better detection of CNAs in gene-poor region. Availability of these comparison results should guide study design decisions and facilitate further computational development. Keywords: comparative genomic hybridization We generated copy number profiles of a defined set of tumor cell lines on five oligo-microarray-based assays of three platforms (Agilent, Affymetrix and NimbleGen) and determined the reproducibility, signal and noise as well as sensitivity and specificity of each in detecting two-fold signals based on SKY-defined aberrations as ground-truth for comparison. In addition, high-density microarray assays from Agilent and Affymetrix platforms were further compared for definition of CNAs in an independent dataset using published analytical approaches. All arrays were run from the same DNA harvest from respective cell lines. Each array was run according to the protocol of the manufacturer.

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Comparison of variant calling pipelines using Illumina CanineHD BeadChip array as the truth dataset

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