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This article is part of the supplement: Selected articles from the 9th Annual Biotechnology and Bioinformatics Symposium (BIOT 2012)

Open Access Research

VarBin, a novel method for classifying true and false positive variants in NGS data

Jacob Durtschi1*, Rebecca L Margraf1, Emily M Coonrod1, Kalyan C Mallempati1 and Karl V Voelkerding12

Author Affiliations

1 ARUP Institute for Clinical & Experimental Pathology®, Salt Lake City, Utah, USA

2 Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah, USA

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BMC Bioinformatics 2013, 14(Suppl 13):S2  doi:10.1186/1471-2105-14-S13-S2

Published: 1 October 2013

Abstract

Background

Variant discovery for rare genetic diseases using Illumina genome or exome sequencing involves screening of up to millions of variants to find only the one or few causative variant(s). Sequencing or alignment errors create "false positive" variants, which are often retained in the variant screening process. Methods to remove false positive variants often retain many false positive variants. This report presents VarBin, a method to prioritize variants based on a false positive variant likelihood prediction.

Methods

VarBin uses the Genome Analysis Toolkit variant calling software to calculate the variant-to-wild type genotype likelihood ratio at each variant change and position divided by read depth. The resulting Phred-scaled, likelihood-ratio by depth (PLRD) was used to segregate variants into 4 Bins with Bin 1 variants most likely true and Bin 4 most likely false positive. PLRD values were calculated for a proband of interest and 41 additional Illumina HiSeq, exome and whole genome samples (proband's family or unrelated samples). At variant sites without apparent sequencing or alignment error, wild type/non-variant calls cluster near -3 PLRD and variant calls typically cluster above 10 PLRD. Sites with systematic variant calling problems (evident by variant quality scores and biases as well as displayed on the iGV viewer) tend to have higher and more variable wild type/non-variant PLRD values. Depending on the separation of a proband's variant PLRD value from the cluster of wild type/non-variant PLRD values for background samples at the same variant change and position, the VarBin method's classification is assigned to each proband variant (Bin 1 to Bin 4).

Results

To assess VarBin performance, Sanger sequencing was performed on 98 variants in the proband and background samples. True variants were confirmed in 97% of Bin 1 variants, 30% of Bin 2, and 0% of Bin 3/Bin 4.

Conclusions

These data indicate that VarBin correctly classifies the majority of true variants as Bin 1 and Bin 3/4 contained only false positive variants. The "uncertain" Bin 2 contained both true and false positive variants. Future work will further differentiate the variants in Bin 2.