mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters
Open accessAbstract
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.com Supplementary information: Supplementary data are available at Bioinformatics online.
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By characterizing damage patterns, users can distinguish authentic aDNA from modern contaminants, thereby ensuring the integrity of the dataset.
“By characterizing damage patterns, users can distinguish authentic aDNA from modern contaminants, thereby ensuring the integrity of the dataset (Jónsson et al. 2013, Skoglund et al. 2014).”
From Unravelling reference bias in ancient DNA datasets (2024) · cites this paper as Jónsson et al. 2013
The Abstract says damage-related misincorporation patterns are instrumental in authenticating ancient sequences versus modern contaminants, and the Introduction says mapDamage identifies these patterns. The Conclusion describes reducing errors in downstream analyses, but the paper does not establish that characterizing damage patterns ensures the integrity of a dataset. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAuthentic ancient DNA exhibits characteristic damage including short DNA fragment lengths, a high rate of cytosine deamination at the termini of DNA fragments, and an enrichment of purine bases at DNA breakage sites.
“Briefly, authentic ancient DNA is known to exhibit characteristic damage that includes short DNA fragment lengths, a high rate of cytosine deamination at the termini of DNA fragments, and an enrichment of purine bases at DNA breakage sites.”
From Successful enrichment and recovery of whole mitochondrial genomes from ancient human dental calculus (2016) · cites this paper as Jonsson et al., 2013
The Introduction reports elevated cytosine deamination at 5′ overhangs and damage-related substitutions near read ends, supporting the terminal-deamination part of the claim. It also mentions strand breaks, but our copy does not state that ancient DNA fragments are characteristically short or that purines are enriched at breakage sites. (LLM, conf=0.87)
Experimental — accuracy not yet measuredReads mapped with vg continue to show terminal deamination damage, which is used as a standard diagnostic for the presence of true ancient DNA, as seen in mapDamage plots, although levels are slightly reduced.
“Finally, reads mapped with vg continue to show terminal deamination damage, which is used as a standard diagnostic for the presence of true ancient DNA, as seen in mapDamage [28] plots, although levels are slightly reduced (Additional file 1: Fig. S12).”
From Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph (2020) · cites this paper as [28]
The authenticity of the human reads obtained from archaeological dental calculus and dentine was assessed by examining damage patterns typical of ancient DNA.
“The authenticity of the human reads obtained from archaeological dental calculus and dentine was assessed by examining damage patterns typical of ancient DNA (Briggs et al., 2007; Ginolhac et al., 2011; Jonsson et al., 2013).”
From Successful enrichment and recovery of whole mitochondrial genomes from ancient human dental calculus (2016) · cites this paper as Jonsson et al., 2013
Ancient DNA damage patterns were assessed on unmerged reads using MapDamage 2.0, and read quality scores were modified with the rescale option to account for post-mortem damage.
“Ancient DNA damage patterns were assessed on unmerged reads using MapDamage 2.0 (Jonsson et al., 2013), and read quality scores were modified with the rescale option to account for post‐mortem damage.”
From Successful enrichment and recovery of whole mitochondrial genomes from ancient human dental calculus (2016) · cites this paper as Jonsson et al., 2013
Levels of DNA deamination damage in the mapped reads were estimated using mapDamage (v2.0) and compared to expected values in ancient skeletal element.
“(ii) Levels of DNA deamination damage in the mapped reads were estimated using mapDamage (v2.0) (39) and compared to the expected values in ancient skeletal element.”
From Ancient DNA sheds light on the genetic origins of early Iron Age Philistines (2019) · cites this paper as [39]
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Zhu, Y., Hou, X., Zhao, J., Xiao, B., Song, S., Zou, X., Liu, S., Hofreiter, M., & Lai, X. (2025) · Genes · doi:10.3390/genes16060639 · Cited by 2 · see citing works
- Authors
- Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F., & Orlando, L.
- Affiliations
- University of Copenhagen (DK)
- Emory University (US)
- Corresponding author
- Jónsson, Hákon
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-04-23
- Volume
- 29
- Issue
- 13
- Pages
- 1682-1684
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3694634
- OpenAlex
- W2150163641
- Cited by
- 1,669
- References
- 26
- Citations by year
- 2026: 185 · 2025: 207 · 2024: 178 · 2023: 187 · 2022: 156 · 2021: 196 · 2020: 137 · 2019: 103 · 2018: 119 · 2017: 81 · 2016: 57 · 2015: 29 · 2014: 28 · 2013: 4
- FWCI
- 17.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion)
- Funders
- European Commission (299176, 293845)
- Danmarks Frie Forskningsfond
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- ancient DNA, Bayesian model, cytosine deamination, overhang length, contamination detection
- MeSH terms
- Bayes Theorem, Cytosine, Deamination, DNA Damage, Fossils, Humans, Software, Sequence Analysis, DNA, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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