Statistical assignment of DNA sequences using Bayesian phylogenetics

Kasper Munch Terkelsen; Wouter Krogh Boomsma; John P. Huelsenbeck; Eske Willerslev; Rasmus Nielsen

doi:10.1080/10635150802422316

Statistical assignment of DNA sequences using Bayesian phylogenetics

Kasper Munch Terkelsen, Wouter Krogh Boomsma, John P. Huelsenbeck, Eske Willerslev, Rasmus Nielsen

138 Citations (Scopus)

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.

Original language	English
Journal	Systematic Biology
Volume	57
Issue number	5
Pages (from-to)	750-757
Number of pages	8
ISSN	1063-5157
DOIs	https://doi.org/10.1080/10635150802422316
Publication status	Published - 2008

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title = "Statistical assignment of DNA sequences using Bayesian phylogenetics",

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.",

author = "Terkelsen, {Kasper Munch} and Boomsma, {Wouter Krogh} and Huelsenbeck, {John P.} and Eske Willerslev and Rasmus Nielsen",

note = "Keywords: Animals; Base Sequence; Bayes Theorem; Computer Simulation; DNA; Insects; Models, Genetic; Models, Statistical; Phylogeny; Plants",

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T1 - Statistical assignment of DNA sequences using Bayesian phylogenetics

AU - Terkelsen, Kasper Munch

AU - Boomsma, Wouter Krogh

AU - Huelsenbeck, John P.

AU - Willerslev, Eske

AU - Nielsen, Rasmus

N1 - Keywords: Animals; Base Sequence; Bayes Theorem; Computer Simulation; DNA; Insects; Models, Genetic; Models, Statistical; Phylogeny; Plants

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

U2 - 10.1080/10635150802422316

DO - 10.1080/10635150802422316

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SN - 1063-5157

VL - 57

SP - 750

EP - 757

JO - Systematic Biology

JF - Systematic Biology

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Statistical assignment of DNA sequences using Bayesian phylogenetics

Abstract

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