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Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines

John G. Cleary, Ross Braithwaite, Kurt Gaastra, Brian S. Hilbush, Stuart Inglis, Sean A. Irvine, Alan Jackson, Richard Littin, Mehul Rathod, David Ware, Justin M. Zook, Len Trigg, View ORCID ProfileFrancisco M. De La Vega
doi: https://doi.org/10.1101/023754
John G. Cleary
1Real Time Genomics, Hamilton 3240, New Zealand
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Ross Braithwaite
1Real Time Genomics, Hamilton 3240, New Zealand
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Kurt Gaastra
1Real Time Genomics, Hamilton 3240, New Zealand
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Brian S. Hilbush
1Real Time Genomics, Hamilton 3240, New Zealand
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Stuart Inglis
2NetValue Ltd, Hamilton 3240, New Zealand.
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Sean A. Irvine
1Real Time Genomics, Hamilton 3240, New Zealand
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Alan Jackson
1Real Time Genomics, Hamilton 3240, New Zealand
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Richard Littin
2NetValue Ltd, Hamilton 3240, New Zealand.
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Mehul Rathod
1Real Time Genomics, Hamilton 3240, New Zealand
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David Ware
1Real Time Genomics, Hamilton 3240, New Zealand
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Justin M. Zook
3National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
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Len Trigg
1Real Time Genomics, Hamilton 3240, New Zealand
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Francisco M. De La Vega
1Real Time Genomics, Hamilton 3240, New Zealand
5Department of Genetics, Stanford University, Stanford, CA 94305, USA
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  • ORCID record for Francisco M. De La Vega
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ABSTRACT

Summary To evaluate and compare the performance of variant calling methods and their confidence scores, comparisons between a test call set and a “gold standard” need to be carried out. Unfortunately, these comparisons are not straightforward with the current Variant Call Files (VCF), which are the standard output of most variant calling algorithms for high-throughput sequencing data. Comparisons of VCFs are often confounded by the different representations of indels, MNPs, and combinations thereof with SNVs in complex regions of the genome, resulting in misleading results. A variant caller is inherently a classification method designed to score putative variants with confidence scores that could permit controlling the rate of false positives (FP) or false negatives (FN) for a given application. Receiver operator curves (ROC) and the area under the ROC (AUC) are efficient metrics to evaluate a test call set versus a gold standard. However, in the case of VCF data this also requires a special accounting to deal with discrepant representations. We developed a novel algorithm for comparing variant call sets that deals with complex call representation discrepancies and through a dynamic programing method that minimizes false positives and negatives globally across the entire call sets for accurate performance evaluation of VCFs.

Availability RTG Tools is implemented as a multithreaded Java application and source code is available under BSD license at: https://github.com/RealTimeGenomics/rtg-tools

Contact len{at}realtimegenomics.com

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted August 03, 2015.
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Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines
John G. Cleary, Ross Braithwaite, Kurt Gaastra, Brian S. Hilbush, Stuart Inglis, Sean A. Irvine, Alan Jackson, Richard Littin, Mehul Rathod, David Ware, Justin M. Zook, Len Trigg, Francisco M. De La Vega
bioRxiv 023754; doi: https://doi.org/10.1101/023754
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Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines
John G. Cleary, Ross Braithwaite, Kurt Gaastra, Brian S. Hilbush, Stuart Inglis, Sean A. Irvine, Alan Jackson, Richard Littin, Mehul Rathod, David Ware, Justin M. Zook, Len Trigg, Francisco M. De La Vega
bioRxiv 023754; doi: https://doi.org/10.1101/023754

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