Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 130

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Differential preservation of endogenous human and microbial DNA in dental calculus and dentin

Mann, Allison E., Sabin, Susanna, Ziesemer, Kirsten, Vågene, Åshild J., Schroeder, Hannes, Ozga, Andrew T., Sankaranarayanan, Krithivasan, Hofman, Courtney A., Fellows Yates, James A., Salazar-García, Domingo C., Frohlich, Bruno, Aldenderfer, Mark, Hoogland, Menno, Read, Christopher, Milner, George R., Stone, Anne C., Lewis, Cecil M., Krause, Johannes, Hofman, Corinne, Bos, Kirsten I., Warinner, Christina·2018·Scientific Reports
DOI 10.1038/s41598-018-28091-9PMC PMC6026117
Open this paper in CoChat — free account

Abstract

Dental calculus (calcified dental plaque) is prevalent in archaeological skeletal collections and is a rich source of oral microbiome and host-derived ancient biomolecules. Recently, it has been proposed that dental calculus may provide a more robust environment for DNA preservation than other skeletal remains, but this has not been systematically tested. In this study, shotgun-sequenced data from paired dental calculus and dentin samples from 48 globally distributed individuals are compared using a metagenomic approach. Overall, we find DNA from dental calculus is consistently more abundant and less contaminated than DNA from dentin. The majority of DNA in dental calculus is microbial and originates from the oral microbiome; however, a small but consistent proportion of DNA (mean 0.08 ± 0.08%, range 0.007–0.47%) derives from the host genome. Host DNA content within dentin is variable (mean 13.70 ± 18.62%, range 0.003–70.14%), and for a subset of dentin samples (15.21%), oral bacteria contribute > 20% of total DNA. Human DNA in dental calculus is highly fragmented, and is consistently shorter than both microbial DNA in dental calculus and human DNA in paired dentin samples. Finally, we find that microbial DNA fragmentation patterns are associated with guanine-cytosine (GC) content, but not aspects of cellular structure.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-018-28091-9

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Geographic locations and temporal periods of archaeological teeth included in this study. (a) Camino del Molino, Iberia; (b) Arbulag Soum, Khövsgöl, Mongolia; (c) Samdzong, Nepal; (d) Kilteasheen, Ireland; (e) Anse à la Gourde, Guadeloupe; (f) Norris Farms, Illinois, USA; (g) Middenbeemster, the Netherlands. For each site, representative teeth with dental calculus deposits are shown in boxes. The number of teeth (dentin-calculus pairs) analyzed per site is provided within the indicated circles, and corresponding letters on the timeline indicate the time period represented by each site.
Figure 1 Geographic locations and temporal periods of archaeological teeth included in this study. (a) Camino del Molino, Iberia; (b) Arbulag Soum, Khövsgöl, Mongolia; (c) Samdzong, Nepal; (d) Kilteasheen, Ireland; (e) Anse à la Gourde, Guadeloupe; (f) Norris Farms, Illinois, USA; (g) Middenbeemster, the Netherlands. For each site, representative teeth with dental calculus deposits are shown in boxes. The number of teeth (dentin-calculus pairs) analyzed per site is provided within the indicated circles, and corresponding letters on the timeline indicate the time period represented by each site.CC BY · PubMed Central
Figure 2 Total DNA content of dental calculus is higher than dentin as measured by both fluorescence and quantitative PCR (qPCR) techniques. (a) Normalized DNA yield (log transformed nanograms DNA per milligram starting material) of DNA extracts obtained from dental calculus and dentin as measured by a Qubit fluorometer using a High Sensitivity Assay (p = 3.911e-11, Wilcoxon signed-rank paired test). (b) Linear correlation between normalized DNA yield (log(ng/mg)) and normalized DNA yield as measured by qPCR after library preparation. The average number of copies per milligram was calculated from the mean of four technical replicates, and these values were compared between dentin and calculus (p = 6.941e-07, Wilcoxon signed-rank paired test). In both quantification metrics dental calculus has a higher DNA yield than dentin.
Figure 2 Total DNA content of dental calculus is higher than dentin as measured by both fluorescence and quantitative PCR (qPCR) techniques. (a) Normalized DNA yield (log transformed nanograms DNA per milligram starting material) of DNA extracts obtained from dental calculus and dentin as measured by a Qubit fluorometer using a High Sensitivity Assay (p = 3.911e-11, Wilcoxon signed-rank paired test). (b) Linear correlation between normalized DNA yield (log(ng/mg)) and normalized DNA yield as measured by qPCR after library preparation. The average number of copies per milligram was calculated from the mean of four technical replicates, and these values were compared between dentin and calculus (p = 6.941e-07, Wilcoxon signed-rank paired test). In both quantification metrics dental calculus has a higher DNA yield than dentin.CC BY · PubMed Central
Figure 3 Microbial communities represented in archaeological dental calculus and dentin are distinct. (a) Principal Coordinates Analysis (PCoA) of Bray-Curtis distances of all bacterial and archaeal species-level assignments from dental calculus and dentin. Color indicates material type (dental calculus or dentin) and membership in global or regional dataset. Microbial taxonomy was assigned using MALT with the full NCBI nucleotide database. Dentin samples marked with an asterisk belong to individuals NF47 and NF217 in the global dataset, and may represent teeth with carious lesions (Supplementary Figure S2). (b) A subset of four sample pairs from the global (S41, H24) and regional (KT14, KT13) sample sets were selected to further demonstrate differences in dental calculus and dentin microbial communities. Species represented in the MALT results were sorted into a layered classification scheme (see legend in gray box), and the proportions of reads assigned to each taxon were used to generate Krona plots. (c) Stacked bar plots of Bayesian SourceTracker results for the four selected pairs display estimated proportions of source contribution at the genus level, using modern plaque, skin, and soil datasets as model sources. Both approaches show overwhelming abundance of environmental bacteria within dentin samples, while most microbial DNA within the paired calculus samples are associated with, and most likely derive from, the human oral microbiome. The robustness of the human microbiome signal to the environmental signal is driving the separation we see in the PCoA.
Figure 3 Microbial communities represented in archaeological dental calculus and dentin are distinct. (a) Principal Coordinates Analysis (PCoA) of Bray-Curtis distances of all bacterial and archaeal species-level assignments from dental calculus and dentin. Color indicates material type (dental calculus or dentin) and membership in global or regional dataset. Microbial taxonomy was assigned using MALT with the full NCBI nucleotide database. Dentin samples marked with an asterisk belong to individuals NF47 and NF217 in the global dataset, and may represent teeth with carious lesions (Supplementary Figure S2). (b) A subset of four sample pairs from the global (S41, H24) and regional (KT14, KT13) sample sets were selected to further demonstrate differences in dental calculus and dentin microbial communities. Species represented in the MALT results were sorted into a layered classification scheme (see legend in gray box), and the proportions of reads assigned to each taxon were used to generate Krona plots. (c) Stacked bar plots of Bayesian SourceTracker results for the four selected pairs display estimated proportions of source contribution at the genus level, using modern plaque, skin, and soil datasets as model sources. Both approaches show overwhelming abundance of environmental bacteria within dentin samples, while most microbial DNA within the paired calculus samples are associated with, and most likely derive from, the human oral microbiome. The robustness of the human microbiome signal to the environmental signal is driving the separation we see in the PCoA.CC BY · PubMed Central
Figure 4 Human DNA in dental calculus shows consistent patterns of low relative abundance and high fragmentation. (a) Relative percentage of human DNA in all paired calculus and dentin samples calculated from de-duplicated reads mapped to the hg19 human reference genome using BWA. While the majority of dentin samples have an overall higher percentage of human DNA, this value varies substantially by sample. Calculus is comparatively consistent between samples albeit on average lower than their paired dentin sample. Sample pairs corresponding to those in Fig. 3 are indicated by numbered triangles: (1) S41, (2) H24, (3) KT14, (4) KT13. (b) Median fragment length of merged reads mapping to the human genome compared to all merged reads in both dental calculus and dentin. Human assigned reads in dental calculus are shorter than expected independent of age, laboratory processing protocol, or sample preservation. Human mapped reads in dental calculus and dentin were further verified for authenticity using strict mapping parameters (Supplementary Figure S5, Supplementary Table S9).
Figure 4 Human DNA in dental calculus shows consistent patterns of low relative abundance and high fragmentation. (a) Relative percentage of human DNA in all paired calculus and dentin samples calculated from de-duplicated reads mapped to the hg19 human reference genome using BWA. While the majority of dentin samples have an overall higher percentage of human DNA, this value varies substantially by sample. Calculus is comparatively consistent between samples albeit on average lower than their paired dentin sample. Sample pairs corresponding to those in Fig. 3 are indicated by numbered triangles: (1) S41, (2) H24, (3) KT14, (4) KT13. (b) Median fragment length of merged reads mapping to the human genome compared to all merged reads in both dental calculus and dentin. Human assigned reads in dental calculus are shorter than expected independent of age, laboratory processing protocol, or sample preservation. Human mapped reads in dental calculus and dentin were further verified for authenticity using strict mapping parameters (Supplementary Figure S5, Supplementary Table S9).CC BY · PubMed Central
Figure 5 Fragment length and damage rates among bacterial taxa within calculus. (a) Fragment length distribution of 50 high frequency species-level bacteria among all dental calculus grouped into three metadata categories: gram status, the presence or absence of a surface layer (S-layer), and the overall genomic GC content of the organism as documented from the reference genome in the NCBI database. Input was normalized to 400 randomly chosen reads per sample per species to mitigate the impact of sample specific read length profiles. No detectable differences in the median are apparent for the presence of an S-layer or gram status. Genomic GC content, however, does impact the overall fragment length profile of reads. (b) Deviation of the median fragment length from overall sample median fragment length of a subset of 20 oral bacteria in dental calculus colored by dataset origin. Again, genomic GC content appears to impact the fragment length profiles of individual taxa where high GC content species have shorter overall fragment length profiles. (c) Terminal cytosine damage rates (C to T substitution ratio at the first position of the 5′ end of the molecule) among 50 bacterial species in dental calculus grouped by gram status, presence or absence of an S-layer, and overall genomic GC content. No detectable differences in damage rates could be detected in any of the three chosen metadata categories. (d) Terminal cytosine damage rates (C to T substitution ratio at the first position of the 5′ end of the molecule) among 20 oral bacteria. While some differences in damage patterns can be detected, it is unclear if this is random or due to some process not examined in this study.
Figure 5 Fragment length and damage rates among bacterial taxa within calculus. (a) Fragment length distribution of 50 high frequency species-level bacteria among all dental calculus grouped into three metadata categories: gram status, the presence or absence of a surface layer (S-layer), and the overall genomic GC content of the organism as documented from the reference genome in the NCBI database. Input was normalized to 400 randomly chosen reads per sample per species to mitigate the impact of sample specific read length profiles. No detectable differences in the median are apparent for the presence of an S-layer or gram status. Genomic GC content, however, does impact the overall fragment length profile of reads. (b) Deviation of the median fragment length from overall sample median fragment length of a subset of 20 oral bacteria in dental calculus colored by dataset origin. Again, genomic GC content appears to impact the fragment length profiles of individual taxa where high GC content species have shorter overall fragment length profiles. (c) Terminal cytosine damage rates (C to T substitution ratio at the first position of the 5′ end of the molecule) among 50 bacterial species in dental calculus grouped by gram status, presence or absence of an S-layer, and overall genomic GC content. No detectable differences in damage rates could be detected in any of the three chosen metadata categories. (d) Terminal cytosine damage rates (C to T substitution ratio at the first position of the 5′ end of the molecule) among 20 oral bacteria. While some differences in damage patterns can be detected, it is unclear if this is random or due to some process not examined in this study.CC BY · PubMed Central
Figure 6 Relationship of GC content to fragment length in five prevalent oral genera and one soil genus (Streptomyces). (a) Two low expected GC content genera, Methanobrevibacter and Fusobacterium binned by read lengths wherein each dot represents the mean GC content for that bin. (b) Two moderate expected GC content general, Tannerella and Porphyromonas binned by read lengths wherein each dot represents the mean GC content for that bin. (c) One high expected GC content oral genus (Actinomyces) and one high expected GC content soil genus (Streptomyces) binned by read lengths wherein each dot represents the mean GC content for that bin. Heat bar indicated the number of reads representing each length bin. In all cases except for Streptomyces only reads from dental calculus samples were included. Reads for Streptomyces were extracted from dentin samples to contrast GC shifts in a potentially modern soil contaminate with typical members of the human oral microbiome. In all cases except for Streptomyces the GC content of shorter reads is skewed higher than the overall mean value. This effect greater in low to moderate expected GC content genera than those with higher GC content (Supplementary Figure S8).
Figure 6 Relationship of GC content to fragment length in five prevalent oral genera and one soil genus (Streptomyces). (a) Two low expected GC content genera, Methanobrevibacter and Fusobacterium binned by read lengths wherein each dot represents the mean GC content for that bin. (b) Two moderate expected GC content general, Tannerella and Porphyromonas binned by read lengths wherein each dot represents the mean GC content for that bin. (c) One high expected GC content oral genus (Actinomyces) and one high expected GC content soil genus (Streptomyces) binned by read lengths wherein each dot represents the mean GC content for that bin. Heat bar indicated the number of reads representing each length bin. In all cases except for Streptomyces only reads from dental calculus samples were included. Reads for Streptomyces were extracted from dentin samples to contrast GC shifts in a potentially modern soil contaminate with typical members of the human oral microbiome. In all cases except for Streptomyces the GC content of shorter reads is skewed higher than the overall mean value. This effect greater in low to moderate expected GC content genera than those with higher GC content (Supplementary Figure S8).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 69

  1. White, D. J. Processes contributing to the formation of dental calculus. Biofouling 4, 209–218 (1991). · DOI
  2. Marsh, P. D. Dental plaque as a biofilm and microbial community - implications for health and disease. BMC Oral Health 6, https://doi.org/10.1186/1472-6831-6-S1-S14 (2006). · DOI · Paper page
  3. Warinner, C., Speller, C. & Collins, M. J. A new era in palaeomicrobiology: prospects for ancient dental calculus as a long-term record of the human oral microbiome. Phil. Trans. R. Soc. B 370, 20130376 (2015). · DOI
  4. Marsh, P. D. & Bradshaw, D. J. Dental plaque as a biofilm. Journal of Industrial Microbiology 15, 169–175, https://doi.org/10.1007/bf01569822 (1995). · DOI
  5. Power, R. C., Salazar-García, D. C., Wittig, R. M., Freiberger, M. & Henry, A. G. Dental calculus evidence of plant diet and life history transitions in the chimpanzees of the Taï Forest. Scientific Reports 5, 15161 (2015). · DOI
  6. Power, R. C., Salazar-García, D. C., Wittig, R. M. & Henry, A. G. Assesing use and suitability of scanning electron microscopy in the analysis of micro remains in dental calculus. Journal of Archaeological Science 49, 160–169 (2014). · DOI
  7. Armitage, P. L. The extraction and identification of opal phytoliths from the teeth of ungulates. Journal of Archaeological Science 2, 187–197, https://doi.org/10.1016/0305-4403(75)90056-4 (1975). · DOI
  8. Fox, C. L., Pérez-Pérez, A. & Juan, J. Dietary Information through the Examination of Plant Phytoliths on the Enamel Surface of Human Dentition. Journal of Archaeological Science 21, 29–34, https://doi.org/10.1006/jasc.1994.1005 (1994). · DOI
  9. Henry, A. G., Brooks, A. S. & Piperno, D. R. Microfossils in calculus demonstrate consumption of plants and cooked foods in Neanderthal diets (Shanidar III, Iraq; Spy I and II, Belgium). Proceedings of the National Academy of Sciences 108, 486 (2011). · DOI
  10. Dudgeon John, V. & Tromp, M. Diet, Geography and Drinking Water in Polynesia: Microfossil Research from Archaeological Human Dental Calculus, Rapa Nui (Easter Island). International Journal of Osteoarchaeology 24, 634–648, https://doi.org/10.1002/oa.2249 (2012). · DOI
  11. King, D. J., Searcy, M. T., Yost, C. L. & Waller, K. Corn, Beer, and Marine Resources at Casas Grandes, Mexico: An Analysis of Prehistoric Diets Using Microfossils Recovered from Dental Calculus. Journal of Archaeological Science: Reports 16, 365–379, https://doi.org/10.1016/j.jasrep.2017.10.013 (2017). · DOI
  12. de la Fuente, C., Flores, S. & Moraga, M. DNA from human ancient bacteria: A novel source of genetic evidence from archaeological dental calculus. Archaeometry 55, 767–778 (2013). · DOI · Paper page
  13. Warinner, C. et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 46, 336–344 (2014). · DOI · Paper page
  14. Weyrich, L. S. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357 (2017). · DOI
  15. Price, S. D. R., Keenleyside, A. & Schwarcz, H. P. Testing the validity of stable isotope analyses of dental calculus as a proxy in paleodietary studies. Journal of Archaeological Science 91, 92–103 (2018). · DOI
  16. Paabo, S. et al. Genetic analyses from ancient DNA. Annual review of genetics 38, 645–679, https://doi.org/10.1146/annurev.genet.37.110801.143214 (2004). · DOI · Paper page
  17. Dabney, J., Meyer, M. & Pääbo, S. Ancient DNA damage. Cold Spring Harbor perspectives in biology 5, a012567 (2013). · DOI · Paper page
  18. Damgaard, P. B. et al. Improving access to endogenous DNA in ancient bones and teeth. Scientific Reports 5, 11184 (2015). · DOI · Paper page
  19. Pinhasi, R. et al. Optimal ancient DNA yields from the inner ear part of the human petrous bone. PloS one 10, e0129102 (2015). · DOI · Paper page
  20. Hansen, H. B. et al. Comparing ancient DNA preservation in petrous bone and tooth cementum. PLoS One 12, e0170940 (2017). · DOI
  21. Ziesemer, K. A. et al. Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification. Sci Rep 5, 16498, https://doi.org/10.1038/srep16498 (2015). · DOI · Paper page
  22. Kemp, B. M., Winters, M., Monroe, C. & Barta, J. L. How much DNA is lost? Measuring DNA loss of short-tandem-repeat length fragments targeted by the PowerPlex 16 System using the Qiagen MinElute Purification Kit. Human Biology 86, 1–18 (2014). · DOI
  23. Vågene, Å. J. et al. Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nature Ecology & Evolution, https://doi.org/10.1038/s41559-017-0446-6 (2018). · DOI
  24. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome biology 12, R60 (2011). · DOI · Paper page
  25. Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics 12, 385 (2011). · DOI
  26. Coordinators, N. R. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 46, D8–D13, https://doi.org/10.1093/nar/gkx1095 (2018). · DOI
  27. Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat Meth 8, 761–763, http://www.nature.com/nmeth/journal/v8/n9/abs/nmeth.1650.html - supplementary-information (2011). · DOI
  28. Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207 (2012). · DOI · Paper page
  29. Johnston, E. R. et al. Metagenomics reveals pervasive bacterial populations and reduced community diversity across the Alaska tundra ecosystem. Frontiers in microbiology 7, 579 (2016). · DOI
  30. Belda-Ferre, P. et al. The oral metagenome in health and disease. The ISME journal 6, 46 (2012). · DOI
  31. Jiang, W. et al. Pyrosequencing analysis of oral microbiota shifting in various caries states in childhood. Microbial ecology 67, 962–969 (2014). · DOI
  32. Schuenemann, V. J. et al. Genome-Wide Comparison of Medieval and Modern Mycobacterium leprae. Science 341, 179 (2013). · DOI · Paper page
  33. Socransky, S. S. & Haffajee, A. D. Dental biofilms: difficult therapeutic targets. Periodontology 2000 28, 2–55 (2002). · DOI · Paper page
  34. Marshall, G. W., Marshall, S. J., Kinney, J. H. & Balooch, M. The dentin substrate: structure and properties related to bonding. Journal of Dentistry 25, 441–458, https://doi.org/10.1016/S0300-5712(96)00065-6 (1997). · DOI
  35. Palmer, L., Chapple, I., Wright, H., Roberts, A. & Cooper, P. Extracellular deoxyribonuclease production by periodontal bacteria. Journal of periodontal research 47, 439–445 (2012). · DOI · Paper page
  36. Warinner, C., Speller, C., Collins, M. J. & C. M., L. Jr. Ancient human microbiomes. Journal of Human Evolution 79, 125–136 (2015). · DOI
  37. Watson, P. et al. Penetration of fluoride into natural plaque biofilms. Journal of dental research 84, 451–455 (2005). · DOI
  38. Velsko, I. M. et al. The dental calculus metabolome in modern and historic samples. Metabolomics 13 (2017). · DOI
  39. Linde, A. & Goldberg, M. Dentinogenesis. Critical Reviews in Oral Biology and Medicine 4, 679–728 (1993). · DOI
  40. Lekic, P., Rojas, J., Birek, C., Tenenbaum, H. & McCulloch, C. A. G. Phenotypic comparison of periodontal ligament cells in vivo and in vitro. Journal of Periodontal Research 36, 71–79 (2001). · DOI
  41. Provenza, D. V. The Blood Vascular Supply of the Dental Pulp with Emphasis on Capillary Circulation. Circulation Research 6, 213 (1958). · DOI
  42. Philips, A. et al. Comprehensive analysis of microorganisms accompanying human archaeological remains. GigaScience (2017). · DOI
  43. Seper, A. et al. Vibrio cholerae evades neutrophil extracellular traps by the activity of two extracellular nucleases. PLoS pathogens 9, e1003614 (2013). · DOI
  44. Ryder, M. I. Comparison of neutrophil functions in aggressive and chronic periodontitis. Periodontology 2000 53, 124–137 (2010). · DOI · Paper page
  45. Branzk, N. et al. Neutrophils sense microbe size and selectively release neutrophil extracellular traps in response to large pathogens. Nature immunology 15, 1017 (2014). · DOI · Paper page
  46. von Köckritz-Blickwede, M., Blodkamp, S. & Nizet, V. Interaction of bacterial exotoxins with neutrophil extracellular traps: impact for the infected host. Frontiers in microbiology 7, 402 (2016). · DOI
  47. Bhongir, R. K. et al. DNA-fragmentation is a source of bactericidal activity against Pseudomonas aeruginosa. Biochemical Journal 474, 411–425 (2017). · DOI
  48. Majewski, P. et al. Inhibitors of serine proteases in regulating the production and function of neutrophil extracellular traps. Frontiers in immunology 7, 261 (2016). · DOI
  49. Brinkmann, V. & Zychlinsky, A. Neutrophil extracellular traps: is immunity the second function of chromatin? J Cell Biol 198, 773–783 (2012). · DOI · Paper page
  50. Palmer, L. J., Damgaard, C., Holmstrup, P. & Nielsen, C. H. Influence of complement on neutrophil extracellular trap release induced by bacteria. Journal of periodontal research 51, 70–76 (2016). · DOI
  51. Dang, G. et al. Characterization of Rv0888, a novel extracellular nuclease from Mycobacterium tuberculosis. Scientific reports 6, 19033 (2016). · DOI
  52. Warinner, C. et al. A Robust Framework for Microbial Archaeology. Annual Review of Genomics and Human Genetics 18, 321–356, https://doi.org/10.1146/annurev-genom-091416-035526 (2017). · DOI
  53. Panjkovich, A., Norambuena, T. & Melo, F. dnaMATE: a consensus melting temperature prediction server for short DNA sequences. Nucleic Acids Research 33, W570–W572, https://doi.org/10.1093/nar/gki379 (2005). · DOI
  54. Foerstner, K. U., Von Mering, C., Hooper, S. D. & Bork, P. Environments shape the nucleotide composition of genomes. EMBO reports 6, 1208–1213 (2005). · DOI
  55. Gansauge, M.-T. et al. Single-stranded DNA library preparation from highly degraded DNA using T4 DNA ligase. Nucleic Acids Research 45, e79–e79, https://doi.org/10.1093/nar/gkx033 (2017). · DOI
  56. Waters-Rist, A. L. & Hoogland, M. L. P. Osteological evidence of short-limbed dwarfism in a nineteenth century Dutch family: Achondroplasia or hypochondroplasia. Int J Pathology 3, 243–256 (2013).
  57. Maurandi, J. L., Martinex, M. L., Martinez, F. R. & Fernandez, A. A. The collective Chalcolithic burial of Camino del Molino (Caravaca de la Cruz, Murcia, Spain). Methodology and the first results of an exceptional archaeological site. Trabajos Prehist 66, 143–159 (2009). · DOI
  58. Aldenderfer, M. Variation in mortuary practice on the early Tibetan plateau and the high Himalayas. Journal of the International Association for Bon Research 1, 293–318 (2013).
  59. Jeong, C. et al. Long-term genetic stability and a high-altitude East Asian origin for the peoples of the high valleys of the Himalayan arc. Proc Natl Acad Sci USA 113, 7485–7490, https://doi.org/10.1073/pnas.1520844113 (2016). · DOI
  60. Littleton, J. et al. Taphonomic analysis of Bronze Age burials in Mongolian khirigsuurs. Journal of Archaeological Science 39, 3361–3370 (2012). · DOI
  61. Read, C. In Medieval Lough Cé: history, archaeology, and landscape (ed. Thomas Finan) (Four Courts Press, 2010).
  62. Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harbor Protocols 2010, pdb. prot5448 (2010). · DOI
  63. Peltzer, A. et al. EAGER: efficient ancient genome reconstruction. Genome biology 17, 60 (2016). · DOI · Paper page
  64. Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, 2009). · DOI · Paper page
  65. Sarkar, D. Lattice: multivariate data visualization with R. (Springer Science & Business Media, 2008). · DOI
  66. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics 25, 1754–1760 (2009). · DOI · Paper page
  67. Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004). · DOI · Paper page
  68. Sabin, S. Ancient DNA analysis of dental remains from Kilteasheen: a case study in metagenomics and an exploration of dental calculus M.Sc. thesis, Eberhard-Karls-Universität Tübingen, (2016).
  69. Tange, O. In The USENIX Magazine 42-47 (2011). · DOI

Cited by

See all 130

Metadata

Authors
Mann, A. E., Sabin, S., Ziesemer, K., Vågene, Å. J., Schroeder, H., Ozga, A. T., Sankaranarayanan, K., Hofman, C. A., Fellows Yates, J. A., Salazar-García, D. C., Frohlich, B., Aldenderfer, M., Hoogland, M., Read, C., Milner, G. R., Stone, A. C., Lewis, C. M., Krause, J., Hofman, C., Bos, K. I., & Warinner, C.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2018-06-29
Volume
8
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-018-28091-9
PMC
PMC6026117
Cited by
130
References
15
Language
en
Licence

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review