title: Reliably Detecting Clinically Important Variants Requires Both Combined Variant Calls and Optimized Filtering Strategies.
abstract: A diversity of tools is available for identification of variants from genome sequence data. Given the current complexity of incorporating external software into a genome analysis infrastructure, a tendency exists to rely on the results from a single tool alone. The quality of the output variant calls is highly variable however, depending on factors such as sequence library quality as well as the choice of short-read aligner, variant caller, and variant caller filtering strategy. Here we present a two-part study first using the high quality 'genome in a bottle' reference set to demonstrate the significant impact the choice of aligner, variant caller, and variant caller filtering strategy has on overall variant call quality and further how certain variant callers outperform others with increased sample contamination, an important consideration when analyzing sequenced cancer samples. This analysis confirms previous work showing that combining variant calls of multiple tools results in the best quality resultant variant set, for either specificity or sensitivity, depending on whether the intersection or union, of all variant calls is used respectively. Second, we analyze a melanoma cell line derived from a control lymphocyte sample to determine whether software choices affect the detection of clinically important melanoma risk-factor variants finding that only one of the three such variants is unanimously detected under all conditions. Finally, we describe a cogent strategy for implementing a clinical variant detection pipeline; a strategy that requires careful software selection, variant caller filtering optimizing, and combined variant calls in order to effectively minimize false negative variants. While implementing such features represents an increase in complexity and computation the results offer indisputable improvements in data quality.
authors.affiliation: Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.|National Computational Infrastructure, Australian National University, Canberra, ACT, Australia.
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Field
authors.affiliation: Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.|Australian Phenomics Facility, Australian National University, Canberra, ACT, Australia.
authors.forename: Vicky
authors.initials: V
authors.lastname: Cho
authors.affiliation: Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.|National Computational Infrastructure, Australian National University, Canberra, ACT, Australia.
authors.forename: T Daniel
authors.initials: TD
authors.lastname: Andrews
authors.affiliation: Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.|Immunogenomics Group, Immunology Research Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
authors.forename: Chris C
authors.initials: CC
authors.lastname: Goodnow
mesh_terms: D045744:Cell Line, Tumor; D003198:Computer Simulation; D005260:Female; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012372:ROC Curve; D012307:Risk Factors; D012984:Software