Haploid to diploid alignment for variation calling assessment.

Abstract

Variation calling is the process of detecting differences between donor and consensus DNA via high-throughput sequencing read mapping. When evaluating the performance of different variation calling methods, a typical scenario is to simulate artificial (diploid) genomes and sample reads from those. After variation calling, one can then compute precision and recall statistics. This works reliably on SNPs but on larger indels there is the problem of invariance: a predicted deletion/insertion can differ slightly from the true one, yet both make the same change to the genome. Also exactly correct predictions are rare, especially on larger insertions, so one should consider some notion of approximate predictions for fair comparison. We propose a full genome alignment-based strategy that allows for fair comparison of variation calling predictions: First, we apply the predicted variations to the consensus genome to create as many haploid genomes as are necessary to explain the variations. Second, we align the haploid genomes to the (aligned) artificial diploid genomes allowing arbitrary recombinations. The resulting haploid to diploid alignments tells how much the predictions differ from the true ones, solving the invariance issues in direct variation comparison. In an effort to make the approach scalable to real genomes, we develop a simple variant of the classical edit distance dynamic programming algorithm and apply the diagonal doubling technique to optimise the computation. We experiment with the approach on simulated predictions and also on real prediction data from a variation calling challenge.