Titel
A simple data-adaptive probabilistic variant calling model
Autor*in
Steve Hoffmann
Junior Research Group Transcriptome Bioinformatics, University Leipzig
Autor*in
Korbinian Strimmer
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig
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. The likelihoods are then combined to a score that typically gives rise to a mixture distribution. From this we determine a decision threshold to separate potentially variant sites from the noisy background. Conclusions In simulations we show that our simple 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.
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:446245
Erschienen in
Titel
Algorithms for Molecular Biology
Band
10
Ausgabe
10
Seitenanfang
10
Verlag
Springer Science + Business Media
Erscheinungsdatum
2015
Zugänglichkeit

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