A Simple Data-Adaptive Probabilistic Variant Calling Model
Steve Hoffmann, Peter F. Stadler, Korbinian Strimmer
(Submitted on 20 May 2014)
Background: Several sources of noise obfuscate the identification of single nucleotide variation in next generation sequencing data. Not only errors introduced during library construction and sequencing steps but also the quality of the reference genome and the algorithms used for the alignment of the reads play an influential role. It is not trivial to estimate the influence these factors for individual sequencing experiments.
Results: We introduce a simple data-adaptive model for variant calling. Several characteristics are sampled from sites with low mismatch rates and uses to estimate empirical log-likelihoods. These likelihoods are then combined to a score that typically gives rise to a mixture distribution. From these we determine a decision threshold to separate potentially variant sites from the noisy background.
Conclusions: In simulations we show that the proposed model is at par with frequently used SNV calling algorithms in terms of sensitivity and specificity. The application to next-generation sequencing data reveals stark differences of the score distributions indicating a strong influence of data specific sources of noise. The proposed model is specifically designed to adjust to these differences.