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The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50437

Mundlos,  S.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;
Institute for Medical and Human Genetics, Charité Universitätsmedizin Berlin;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50496

Robinson,  P. N.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;
Institute for Medical and Human Genetics, Charité Universitätsmedizin Berlin;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50196

Hecht,  J.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;
Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité Universitätsmedizin Berlin;

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Heinrich.pdf
(Publisher version), 391KB

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Citation

Heinrich, V., Stange, J., Dickhaus, T., Imkeller, P., Kruger, U., Bauer, S., et al. (2012). The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process. Nucleic Acids Research (London), 40(6), 2426-2431. doi:10.1093/nar/gkr1073.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-F26A-D
Abstract
With the availability of next-generation sequencing (NGS) technology, it is expected that sequence variants may be called on a genomic scale. Here, we demonstrate that a deeper understanding of the distribution of the variant call frequencies at heterozygous loci in NGS data sets is a prerequisite for sensitive variant detection. We model the crucial steps in an NGS protocol as a stochastic branching process and derive a mathematical framework for the expected distribution of alleles at heterozygous loci before measurement that is sequencing. We confirm our theoretical results by analyzing technical replicates of human exome data and demonstrate that the variance of allele frequencies at heterozygous loci is higher than expected by a simple binomial distribution. Due to this high variance, mutation callers relying on binomial distributed priors are less sensitive for heterozygous variants that deviate strongly from the expected mean frequency. Our results also indicate that error rates can be reduced to a greater degree by technical replicates than by increasing sequencing depth.