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An Exact Nonparametric Method for Inferring Mosaic Structure in Sequence Triplets

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Boni, Maciej F, Posada, David, Feldman, Marcus W·2007·Genetics

Abstract

Statistical tests for detecting mosaic structure or recombination among nucleotide sequences usually rely on identifying a pattern or a signal that would be unlikely to appear under clonal reproduction. Dozens of such tests have been described, but many are hampered by long running times, confounding of selection and recombination, and/or inability to isolate the mosaic-producing event. We introduce a test that is exact, nonparametric, rapidly computable, free of the infinite-sites assumption, able to distinguish between recombination and variation in mutation/fixation rates, and able to identify the breakpoints and sequences involved in the mosaic-producing event. Our test considers three sequences at a time: two parent sequences that may have recombined, with one or two breakpoints, to form the third sequence (the child sequence). Excess similarity of the child sequence to a candidate recombinant of the parents is a sign of recombination; we take the maximum value of this excess similarity as our test statistic Δm,n,b. We present a method for rapidly calculating the distribution of Δm,n,b and demonstrate that it has comparable power to and a much improved running time over previous methods, especially in detecting recombination in large data sets.

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Metadata

Authors
Boni, M. F., Posada, D., & Feldman, M. W.
Venue
Genetics
Published
2007-06-01
Volume
176
Issue
2
Pages
1035-1047
ISSN
  • 0016-6731 (ISSN-L)
  • 1943-2631
OpenAlex
W2095886196
Cited by
768
References
4
FWCI
9.82
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: An Exact Nonparametric Method for Inferring Mosaic Structure in Sequence Triplets. https://app.cochat.ai/papers/10.1534/genetics.106.068874
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Boni, M. F., Posada, D., & Feldman, M. W. (2007). An Exact Nonparametric Method for Inferring Mosaic Structure in Sequence Triplets. Genetics. https://doi.org/10.1534/genetics.106.068874

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