DOI: 10.1101/475947Nov 23, 2018Paper

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BioRxiv : the Preprint Server for Biology
Dimitrios KleftogiannisStefano Lise

Abstract

Background: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). Methods: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. Results: Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and comp...Continue Reading

Related Concepts

DNA
Pharmaceutical Preparations
Genome
Ions
Neoplasms
Nucleotides
Polymerase Chain Reaction
Protein Biosynthesis
Protein Isoforms
Gene Mutant

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