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Cited by 195

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Statistical Assignment of DNA Sequences Using Bayesian Phylogenetics

Munch, Kasper, Boomsma, Wouter, Huelsenbeck, John P., Willerslev, Eske, Nielsen, Rasmus·2008·Systematic Biology

Abstract

We provide a new automated statistical method for DNA barcoding based on a Bayesian phylogenetic analysis. The method is based on automated database sequence retrieval, alignment, and phylogenetic analysis using a custom-built program for Bayesian phylogenetic analysis. We show on real data that the method outperforms Blast searches as a measure of confidence and can help eliminate 80% of all false assignment based on best Blast hit. However, the most important advance of the method is that it provides statistically meaningful measures of confidence. We apply the method to a re-analysis of previously published ancient DNA data and show that, with high statistical confidence, most of the published sequences are in fact of Neanderthal origin. However, there are several cases of chimeric sequences that are comprised of a combination of both Neanderthal and modern human DNA.

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Metadata

Authors
Munch, K., Boomsma, W., Huelsenbeck, J. P., Willerslev, E., & Nielsen, R.
Venue
Systematic Biology
Published
2008-10-01
Volume
57
Issue
5
Pages
750-757
ISSN
  • 1063-5157 (ISSN-L)
  • 1076-836X
OpenAlex
W2135691213
Cited by
195
References
5
FWCI
3.23
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Statistical Assignment of DNA Sequences Using Bayesian Phylogenetics. https://app.cochat.ai/papers/10.1080/10635150802422316
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Munch, K., Boomsma, W., Huelsenbeck, J. P., Willerslev, E., & Nielsen, R. (2008). Statistical Assignment of DNA Sequences Using Bayesian Phylogenetics. Systematic Biology. https://doi.org/10.1080/10635150802422316

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.