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mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters

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      Abstract

      Motivation: Ancient DNA (aDNA) molecules in fossilized bones and teeth, coprolites, sediments, mummified specimens and museum collections represent fantastic sources of information for evolutionary biologists, revealing the agents of past epidemics and the dynamics of past populations. However, the analysis of aDNA generally faces two major issues. Firstly, sequences consist of a mixture of endogenous and various exogenous backgrounds, mostly microbial. Secondly, high nucleotide misincorporation rates can be observed as a result of severe post-mortem DNA damage. Such misincorporation patterns are instrumental to authenticate ancient sequences versus modern contaminants. We recently developed the user-friendly mapDamage package that identifies such patterns from next-generation sequencing (NGS) sequence datasets. The absence of formal statistical modeling of the DNA damage process, however, precluded rigorous quantitative comparisons across samples.Results: Here, we describe mapDamage 2.0 that extends the original features of mapDamage by incorporating a statistical model of DNA damage. Assuming that damage events depend only on sequencing position and post-mortem deamination, our Bayesian statistical framework provides estimates of four key features of aDNA molecules: the average length of overhangs (λ), nick frequency (ν) and cytosine deamination rates in both double-stranded regions () and overhangs (). Our model enables rescaling base quality scores according to their probability of being damaged. mapDamage 2.0 handles NGS datasets with ease and is compatible with a wide range of DNA library protocols.Availability: mapDamage 2.0 is available at ginolhac.github.io/mapDamage/ as a Python package and documentation is maintained at the Centre for GeoGenetics Web site (geogenetics.ku.dk/publications/mapdamage2.0/).Contact: jonsson.hakon@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

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      Most cited references 11

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      Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

      A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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        Instability and decay of the primary structure of DNA.

         T Lindahl (1993)
        Although DNA is the carrier of genetic information, it has limited chemical stability. Hydrolysis, oxidation and nonenzymatic methylation of DNA occur at significant rates in vivo, and are counteracted by specific DNA repair processes. The spontaneous decay of DNA is likely to be a major factor in mutagenesis, carcinogenesis and ageing, and also sets limits for the recovery of DNA fragments from fossils.
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          A high-coverage genome sequence from an archaic Denisovan individual.

          We present a DNA library preparation method that has allowed us to reconstruct a high-coverage (30×) genome sequence of a Denisovan, an extinct relative of Neandertals. The quality of this genome allows a direct estimation of Denisovan heterozygosity indicating that genetic diversity in these archaic hominins was extremely low. It also allows tentative dating of the specimen on the basis of "missing evolution" in its genome, detailed measurements of Denisovan and Neandertal admixture into present-day human populations, and the generation of a near-complete catalog of genetic changes that swept to high frequency in modern humans since their divergence from Denisovans.
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            Author and article information

            Affiliations
            1Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 København K, Denmark and 2Department of Biology, Emory University, Atlanta, GA 30322, USA
            Author notes
            *To whom correspondence should be addressed.

            Associate Editor: Michael Brudno

            Journal
            Bioinformatics
            Bioinformatics
            bioinformatics
            bioinfo
            Bioinformatics
            Oxford University Press
            1367-4803
            1367-4811
            1 July 2013
            23 April 2013
            23 April 2013
            : 29
            : 13
            : 1682-1684
            23613487
            3694634
            10.1093/bioinformatics/btt193
            btt193
            © The Author 2013. Published by Oxford University Press.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

            Counts
            Pages: 3
            Categories
            Applications Notes
            Sequence Analysis

            Bioinformatics & Computational biology

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