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bioRxiv · 10.1101/461798

Comparison of three variant callers for human whole genome sequencing

Supernat, A.·Vidarsson, O. V.·Steen, V. M.·Stokowy, T.
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Abstract

Testing of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30x, 15x and 10x WGS data of the well-known NA12878 DNA reference sample.\n\nAccording to our comparison, the performance on SNV calling was almost similar in 30x data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively.\n\nWe conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.

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Supernat, A., Vidarsson, O. V., Steen, V. M., Stokowy, T.. 2018-11-05. Comparison of three variant callers for human whole genome sequencing. https://doi.org/10.1101/461798

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