Cited by 141Paleopathology and ancient diseasesGenomics and Phylogenetic StudiesForensic and Genetic Research

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

HOPS: automated detection and authentication of pathogen DNA in archaeological remains

Open access

Published version of a preprint

Abstract

High-throughput DNA sequencing enables large-scale metagenomic analyses of complex biological systems. Such analyses are not restricted to present-day samples and can also be applied to molecular data from archaeological remains. Investigations of ancient microbes can provide valuable information on past bacterial commensals and pathogens, but their molecular detection remains a challenge. Here, we present HOPS (Heuristic Operations for Pathogen Screening), an automated bacterial screening pipeline for ancient DNA sequences that provides detailed information on species identification and authenticity. HOPS is a versatile tool for high-throughput screening of DNA from archaeological material to identify candidates for genome-level analyses.

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 · 10.1186/s13059-019-1903-0

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

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

Flagged first, then by citations

Show all 9 references

Research works printed but not linked: 43

  1. Turnbaugh PJ, et al. The human microbiome project: exploring the microbial part of ourselves in a changing world. Nature. 2007;449:804. · DOI · Paper page
  2. Karsenti E, et al. A holistic approach to marine eco-systems biology. PLoS Biol. 2011;9:e1001177. https://doi.org/10.1371/journal.pbio.1001177. · DOI · Paper page
  3. Gilbert JA, Jansson JK, Knight R. The earth microbiome project: successes and aspirations. BMC Biol. 2014;12:69. https://doi.org/10.1186/s12915-014-0069-1. · DOI
  4. Schlebusch CM, et al. Genomic variation in seven Khoe-san groups reveals adaptation and complex African history. Science. 2012;338:374–9. https://doi.org/10.1126/science.1227721. · DOI
  5. Skoglund P, et al. Reconstructing Prehistoric African Population Structure. Cell. 2017;171:59–71 e21. https://doi.org/10.1016/j.cell.2017.08.049. · DOI
  6. Warinner C, et al. A robust framework for microbial archaeology. Annu Rev Genomics Hum Genet. 2017;18:321–56. https://doi.org/10.1146/annurev-genom-091416-035526. · DOI
  7. Key FM, Posth C, Krause J, Herbig A, Bos KI. Mining metagenomic data sets for ancient DNA: recommended protocols for authentication. Trends Genet. 2017;33:508–20. https://doi.org/10.1016/j.tig.2017.05.005. · DOI · Paper page
  8. Bos KI, et al. A draft genome of Yersinia pestis from victims of the black death. Nature. 2011;478:506–10. https://doi.org/10.1038/nature10549. · DOI · Paper page
  9. Wagner DM, et al. Yersinia pestis and the plague of Justinian 541-543 AD: a genomic analysis. Lancet Infect Dis. 2014;14:319–26. https://doi.org/10.1016/S1473-3099(13)70323-2. · DOI
  10. Rasmussen S, et al. Early divergent strains of Yersinia pestis in Eurasia 5,000 years ago. Cell. 2015;163:571–82. https://doi.org/10.1016/j.cell.2015.10.009. · DOI
  11. Feldman M, et al. A high-coverage Yersinia pestis genome from a sixth-century Justinianic plague victim. Mol Biol Evol. 2016;33:2911–23. https://doi.org/10.1093/molbev/msw170. · DOI
  12. Bos KI, et al. Eighteenth century Yersinia pestis genomes reveal the long-term persistence of an historical plague focus. Elife. 2016;5:e12994. https://doi.org/10.7554/eLife.12994. · DOI
  13. Spyrou MA, et al. Historical Y. pestis genomes reveal the European black death as the source of ancient and modern plague pandemics. Cell Host Microbe. 2016;19:874–81. https://doi.org/10.1016/j.chom.2016.05.012. · DOI
  14. Valtueña AA, et al. The Stone Age plague and its persistence in Eurasia. Current Biol. 2017;27:3683–91. e3688. · DOI
  15. Spyrou MA, et al. Analysis of 3800-year-old Yersinia pestis genomes suggests bronze age origin for bubonic plague. Nat Commun. 2018;9:2234. https://doi.org/10.1038/s41467-018-04550-9. · DOI
  16. Schuenemann VJ, et al. Genome-wide comparison of medieval and modern Mycobacterium leprae. Science. 2013;341:179–83. https://doi.org/10.1126/science.1238286. · DOI · Paper page
  17. Schuenemann VJ, et al. Ancient genomes reveal a high diversity of Mycobacterium leprae in medieval Europe. PLoS Pathog. 2018;14:e1006997. https://doi.org/10.1371/journal.ppat.1006997. · DOI
  18. Bos KI, et al. Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis. Nature. 2014;514:494–7. https://doi.org/10.1038/nature13591. · DOI · Paper page
  19. Kay GL, et al. Eighteenth-century genomes show that mixed infections were common at time of peak tuberculosis in Europe. Nat Commun. 2015;6:6717. https://doi.org/10.1038/ncomms7717. · DOI
  20. D'Anastasio R, Staniscia T, Milia ML, Manzoli L, Capasso L. Origin, evolution and paleoepidemiology of brucellosis. Epidemiol Infect. 2011;139:149–56. https://doi.org/10.1017/S095026881000097X. · DOI
  21. Kay GL, et al. Recovery of a medieval Brucella melitensis genome using shotgun metagenomics. MBio. 2014;5:e01337–14. https://doi.org/10.1128/mBio.01337-14. · DOI
  22. Vagene AJ, et al. Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nat Ecol Evol. 2018;2:520–8. https://doi.org/10.1038/s41559-017-0446-6. · DOI
  23. Zhou Z, et al. Pan-genome Analysis of Ancient and Modern Salmonella enterica Demonstrates Genomic Stability of the Invasive Para C Lineage for Millennia. Curr Biol. 2018;28:2420–8 e2410. https://doi.org/10.1016/j.cub.2018.05.058. · DOI
  24. Maixner F, et al. The 5300-year-old helicobacter pylori genome of the iceman. Science. 2016;351:162–5. https://doi.org/10.1126/science.aad2545. · DOI · Paper page
  25. Caporaso JG, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6. https://doi.org/10.1038/nmeth.f.303. · DOI · Paper page
  26. Velsko IM, Frantz LAF, Herbig A, Larson G, Warinner C. Selection of Appropriate Metagenome Taxonomic Classifiers for Ancient Microbiome Research. mSystems. 2018;3. https://doi.org/10.1128/mSystems.00080-18. · DOI · Paper page
  27. Briggs AW, et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc Natl Acad Sci U S A. 2007;104:14616–21. https://doi.org/10.1073/pnas.0704665104. · DOI · Paper page
  28. Herbig A, et al. MALT: Fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman. BioRxiv. 2016:050559. https://doi.org/10.1101/050559. · DOI
  29. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17:377–86. https://doi.org/10.1101/gr.5969107. · DOI
  30. Droge J, McHardy AC. Taxonomic binning of metagenome samples generated by next-generation sequencing technologies. Brief Bioinform. 2012;13:646–55. https://doi.org/10.1093/bib/bbs031. · DOI
  31. Wootton JC, Federhen S. Statistics of Local Complexity in Amino-Acid-Sequences and Sequence Databases. Comput Chem. 1993;17:149–63. https://doi.org/10.1016/0097-8485(93)85006-X. · DOI
  32. Huson DH, et al. MEGAN Community edition - interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol. 2016;12:e1004957. https://doi.org/10.1371/journal.pcbi.1004957. · DOI · Paper page
  33. Kircher M. Analysis of high-throughput ancient DNA sequencing data. Methods Mol Biol. 2012;840:197–228. https://doi.org/10.1007/978-1-61779-516-9_23. · DOI · Paper page
  34. Mann AE, et al. Differential preservation of endogenous human and microbial DNA in dental calculus and dentin. Sci Rep. 2018;8:9822. https://doi.org/10.1038/s41598-018-28091-9. · DOI
  35. Olalde I, et al. The beaker phenomenon and the genomic transformation of Northwest Europe. Nature. 2018;555:190. · DOI · Paper page
  36. Wang C, Reinhold S, Kalmykov A, et al. Ancient human genome-wide data from a 3000-year interval in the Caucasus corresponds with eco-geographic regions. Nat Commun. 2019;10:590. https://doi.org/10.1038/s41467-018-08220-8. · DOI
  37. Louvel G, Der Sarkissian C, Hanghoj K, Orlando L. metaBIT, an integrative and automated metagenomic pipeline for analysing microbial profiles from high-throughput sequencing shotgun data. Mol Ecol Resour. 2016;16:1415–27. https://doi.org/10.1111/1755-0998.12546. · DOI · Paper page
  38. Zhou Z, Luhmann N, Alikhan NF, Quince C, Achtman M. Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes. In: Raphael B. (eds) Research in Computational Molecular Biology. RECOMB 2018. Lecture Notes in Computer Science, vol 10812. Cham: Springer; 2018. https://doi.org/10.1007/978-3-319-89929-9_15. · DOI
  39. Warinner C, Speller C, Collins MJ. A new era in palaeomicrobiology: prospects for ancient dental calculus as a long-term record of the human oral microbiome. Philos Trans R Soc Lond Ser B Biol Sci. 2015;370:20130376. https://doi.org/10.1098/rstb.2013.0376. · DOI
  40. Warinner C, Speller C, Collins MJ, Lewis CM Jr. Ancient human microbiomes. J Hum Evol. 2015;79:125–36. https://doi.org/10.1016/j.jhevol.2014.10.016. · DOI
  41. Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007;35:D61–5. https://doi.org/10.1093/nar/gkl842. · DOI
  42. Renaud G, Hanghøj K. Willerslev, E. & Orlando, L. gargammel: a sequence simulator for ancient DNA. Bioinformatics. 2016;33:577–9. · DOI · Paper page
  43. Peltzer A, et al. EAGER: efficient ancient genome reconstruction. Genome Biol. 2016;17:60. https://doi.org/10.1186/s13059-016-0918-z. · DOI · Paper page

Cited by

Sorted by citations

  • Ancient DNA analysis

    Orlando, L., Allaby, R., Skoglund, P., Der Sarkissian, C., Stockhammer, P. W., Ávila-Arcos, M. C., Fu, Q., Krause, J., Willerslev, E., Stone, A. C., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00011-0 · Cited by 411

    Self-citation

See all 141

Metadata

Authors
Hübler, R., Key, F. M., Warinner, C., Bos, K. I., Krause, J., & Herbig, A.
Affiliations
Max Planck Institute of Geoanthropology (DE)
Venue
Genome Biology
Published
2019-12
Volume
20
Issue
1
Pages
280-280
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
OpenAlex
W2996069160
Cited by
141
References
9
Citations by year
2026: 25 · 2025: 18 · 2024: 27 · 2023: 17 · 2022: 18 · 2021: 24 · 2020: 9 · 2019: 3
FWCI
78.67
Type
journal article
Language
en
Open access
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Funders
  • Max-Planck-Gesellschaft
  • Deutsche Forschungsgemeinschaft (KE 2408/1-1)
Topics
  • Social Sciences › Arts and Humanities › Archeology (arts and humanities) › Paleopathology and ancient diseases
  • 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
Keywords
ancient DNA, pathogen detection, metagenomics, species identification, Humulus lupulus, archaeological finds
MeSH terms
Archaeology, DNA, Bacterial, Genetic Techniques, Software, Metagenomics

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