Literature DB >> 17395598

Bayesian estimation of sequence damage in ancient DNA.

Simon Y W Ho1, Tim H Heupink, Andrew Rambaut, Beth Shapiro.   

Abstract

DNA extracted from archaeological and paleontological remains is usually damaged by biochemical processes postmortem. Some of these processes lead to changes in the structure of the DNA molecule, which can result in the incorporation of incorrect nucleotides during polymerase chain reaction. These base misincorporations, or miscoding lesions, can lead to the inclusion of spurious additional mutations in ancient DNA (aDNA) data sets. This has the potential to affect the outcome of phylogenetic and population genetic analyses, including estimates of mutation rates and genetic diversity. We present a novel model, termed the delta model, which estimates the amount of damage in DNA data and accounts for its effects in a Bayesian phylogenetic framework. The ability of the delta model to estimate damage is first investigated using a simulation study. The model is then applied to 13 aDNA data sets. The amount of damage in these data sets is shown to be significant but low (about 1 damaged base per 750 nt), suggesting that precautions for limiting the influence of damaged sites, such as cloning and enzymatic treatment, are worthwhile. The results also suggest that relatively high rates of mutation previously estimated from aDNA data are not entirely an artifact of sequence damage and are likely to be due to other factors such as the persistence of transient polymorphisms. The delta model appears to be particularly useful for placing upper credibility limits on the amount of sequence damage in an alignment, and this capacity might be beneficial for future aDNA studies or for the estimation of sequencing errors in modern DNA.

Mesh:

Year:  2007        PMID: 17395598     DOI: 10.1093/molbev/msm062

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  20 in total

1.  Evaluating the impact of post-mortem damage in ancient DNA: a theoretical approach.

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Journal:  J Mol Evol       Date:  2011-11-20       Impact factor: 2.395

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Journal:  Genome Res       Date:  2011-07-29       Impact factor: 9.043

4.  Ancient DNA analyses exclude humans as the driving force behind late Pleistocene musk ox (Ovibos moschatus) population dynamics.

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Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-08       Impact factor: 11.205

5.  Signal processing for metagenomics: extracting information from the soup.

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6.  Intraspecific phylogenetic analysis of Siberian woolly mammoths using complete mitochondrial genomes.

Authors:  M Thomas P Gilbert; Daniela I Drautz; Arthur M Lesk; Simon Y W Ho; Ji Qi; Aakrosh Ratan; Chih-Hao Hsu; Andrei Sher; Love Dalén; Anders Götherström; Lynn P Tomsho; Snjezana Rendulic; Michael Packard; Paula F Campos; Tatyana V Kuznetsova; Fyodor Shidlovskiy; Alexei Tikhonov; Eske Willerslev; Paola Iacumin; Bernard Buigues; Per G P Ericson; Mietje Germonpré; Pavel Kosintsev; Vladimir Nikolaev; Malgosia Nowak-Kemp; James R Knight; Gerard P Irzyk; Clotilde S Perbost; Karin M Fredrikson; Timothy T Harkins; Sharon Sheridan; Webb Miller; Stephan C Schuster
Journal:  Proc Natl Acad Sci U S A       Date:  2008-06-09       Impact factor: 11.205

7.  Ancient DNA and the tropics: a rodent's tale.

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Journal:  Biol Lett       Date:  2014-06       Impact factor: 3.703

8.  Evolutionary analysis of hepatitis C virus gene sequences from 1953.

Authors:  Rebecca R Gray; Yasuhito Tanaka; Yutaka Takebe; Gkikas Magiorkinis; Zelma Buskell; Leonard Seeff; Harvey J Alter; Oliver G Pybus
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2013-08-12       Impact factor: 6.237

Review 9.  Evolutionary Patterns and Processes: Lessons from Ancient DNA.

Authors:  Michela Leonardi; Pablo Librado; Clio Der Sarkissian; Mikkel Schubert; Ahmed H Alfarhan; Saleh A Alquraishi; Khaled A S Al-Rasheid; Cristina Gamba; Eske Willerslev; Ludovic Orlando
Journal:  Syst Biol       Date:  2017-01-01       Impact factor: 9.160

10.  Accurate and fast methods to estimate the population mutation rate from error prone sequences.

Authors:  Bjarne Knudsen; Michael M Miyamoto
Journal:  BMC Bioinformatics       Date:  2009-08-11       Impact factor: 3.169

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