A synthetic-diploid benchmark for accurate variant-calling evaluation

Existing benchmark datasets for use in evaluating variant-calling accuracy are constructed from a consensus of known short-variant callers, and they are thus biased toward easy regions that are accessible by these algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two fully homozygous human cell lines, which provides a relatively more accurate and less biased estimate of small-variant-calling error rates in a realistic context. The synthetic-diploid (Syndip) benchmark dataset, constructed from two fully homozygous long-read assemblies, provides more accurate assessments of error rates in small-variant-calling algorithms than existing benchmarks.