The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process
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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.
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Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
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Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684 · see citing works
SOAP: Short oligonucleotide alignment program
Li, R., Li, Y., Kristiansen, K., & Wang, J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn025 · Cited by 3,597 · see citing works
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Ng, S. B., Buckingham, K. J., Lee, C., Bigham, A. W., Tabor, H. K., Dent, K. M., Huff, C. D., Shannon, P. T., Jabs, E. W., Nickerson, D. A., et al. (2010) · Nature Genetics · doi:10.1038/ng.499 · Cited by 1,957 · see citing works
Athreya, K. B., & Ney, P. E. (1972) · Springer eBooks · doi:10.1007/978-3-642-65371-1 · Cited by 1,676 · see citing works
Genetic diagnosis by whole exome capture and massively parallel DNA sequencing
Choi, M., Scholl, U. I., Ji, W., Liu, T., Tikhonova, I. R., Zumbo, P., Nayır, A., Bakkaloğlu, A., Özen, S., Sanjad, S. A., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0910672106 · Cited by 1,327 · see citing works
Problems and Theorems in Analysis
Pólya, G., & Szegő, G. (1972) · Springer eBooks · doi:10.1007/978-1-4757-1640-5 · Cited by 1,317 · see citing works
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Bell, C. J., Dinwiddie, D. L., Miller, N. A., Hateley, S. L., Ganusova, E. E., Mudge, J., Langley, R. J., Zhang, L., Lee, C. C., Schilkey, F. D., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001756 · Cited by 706 · cited 2× · see citing works
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Harismendy, O., Ng, P. C., Strausberg, R. L., Wang, X., Stockwell, T. B., Beeson, K. Y., Schork, N. J., Murray, S. S., Topol, E. J., Levy, S., et al. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r32 · Cited by 614 · see citing works
Pruitt, K. D., Harrow, J., Harte, R., Wallin, C., Diekhans, M., Maglott, D. R., Searle, S. M. J., Farrell, C. M., Loveland, J. E., Ruef, B. J., et al. (2009) · Genome Research · doi:10.1101/gr.080531.108 · Cited by 558 · see citing works
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Degner, J. F., Marioni, J. C., Pai, A. A., Pickrell, J. K., Nkadori, E., Gilad, Y., & Pritchard, J. K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp579 · Cited by 502 · see citing works
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Goya, R., Sun, M., Morin, R. D., Leung, G., Ha, G., Wiegand, K. C., Senz, J., Crisan, A., Marra, M. A., Hirst, M., et al. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq040 · Cited by 210 · see citing works
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Krawitz, P. M., Rödelsperger, C., Jäger, M., Jostins, L., Bauer, S., & Robinson, P. N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq027 · Cited by 107 · see citing works
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Timp, W., Mirsaidov, U. M., Wang, D., et al. (2010) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2010.2044418 · Cited by 103 · see citing works
Strategies for exome and genome sequence data analysis in disease‐gene discovery projects
Robinson, P. N., Krawitz, P. M., & Mundlos, S. (2011) · Clinical Genetics · doi:10.1111/j.1399-0004.2011.01713.x · Cited by 98 · see citing works
Self-citationRelative frequencies in multitype branching processes
Yakovlev, A. Y., & Yanev, N. M. (2009) · The Annals of Applied Probability · doi:10.1214/08-aap539 · Cited by 42 · see citing works
Technology-specific error signatures in the 1000 Genomes Project data
Nothnagel, M., Herrmann, A., Wolf, A., Schreiber, S., Platzer, M., Siebert, R., Krawczak, M., & Hampe, J. (2011) · Human Genetics · doi:10.1007/s00439-011-0971-3 · Cited by 38 · see citing works
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- Authors
- Heinrich, V., Stange, J., Dickhaus, T., Imkeller, P., Krüger, U., Bauer, S., Mundlos, S., Robinson, P. N., Hecht, J., & Krawitz, P. M.
- Affiliations
- Charité – Universitätsmedizin Berlin (DE)
- Humboldt-Universität zu Berlin (DE)
- Max Planck Society (DE)
- Corresponding author
- Krawitz, Peter M.
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-11-29
- Volume
- 40
- Issue
- 6
- Pages
- 2426-2431
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3315291
- OpenAlex
- W2144523701
- Cited by
- 43
- References
- 19
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 4 · 2021: 6 · 2020: 3 · 2019: 6 · 2018: 1 · 2017: 3 · 2016: 1 · 2015: 2 · 2014: 6 · 2013: 7 · 2012: 1
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (KR 3985/1-1, DFG KR 3985/1-1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- allele frequency distribution, branching processes, heterozygous mutation, variant calling, sequencing error rate, technical replicates, sequencing depth, human exome, variant detection sensitivity
- MeSH terms
- Alleles, Gene Frequency, Heterozygote, Humans, Stochastic Processes, Sequence Analysis, DNA, High-Throughput Nucleotide Sequencing, Exome
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