A Simple Data-Adaptive Probabilistic Variant Calling Model

TL;DR

This paper presents a simple, data-adaptive probabilistic model for variant calling in sequencing data that adjusts to experiment-specific noise factors, achieving competitive sensitivity and specificity, especially at low allele frequencies.

Contribution

The paper introduces a novel, straightforward probabilistic model that automatically adapts to sequencing noise factors, improving variant calling accuracy over complex existing methods.

Findings

Model is competitive with complex algorithms in sensitivity and specificity.

Performs well with low allele frequencies.

Effectively captures data-specific noise influences.

Abstract

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.