  Background: Several sources of noise obfuscate the identification of single
nucleotide variation (SNV) in next generation sequencing data. For instance,
errors may be introduced during library construction and sequencing steps. In
addition, the reference genome and the algorithms used for the alignment of the
reads are further critical factors determining the efficacy of variant calling
methods. It is crucial to account for these factors in individual sequencing
experiments.
  Results: We introduce a simple data-adaptive model for variant calling. This
model automatically adjusts to specific factors such as alignment errors. To
achieve this, several characteristics are sampled from sites with low mismatch
rates, and these are used 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 our simple proposed model is
competitive with frequently used much more complex SNV calling algorithms in
terms of sensitivity and specificity. It performs specifically well in cases
with low allele frequencies. 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.
