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

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

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Hübler, Ron, Key, Felix M., Warinner, Christina, Bos, Kirsten I., Krause, Johannes, Herbig, Alexander·2019·Genome Biology
DOI 10.1186/s13059-019-1903-0PMC PMC6913047

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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.

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Fig. 1 Schematic depiction of HOPS workflow. First, MALT aligns the metagenomic data against its reference database and has an optional mode for processing aDNA reads. MaltExtract then processes the MALT output with various filters and produces various statistics. Finally, post-processing procedures provide a comprehensive visualization of the output that can be evaluated to identify putatively positive hits
Fig. 1 Schematic depiction of HOPS workflow. First, MALT aligns the metagenomic data against its reference database and has an optional mode for processing aDNA reads. MaltExtract then processes the MALT output with various filters and produces various statistics. Finally, post-processing procedures provide a comprehensive visualization of the output that can be evaluated to identify putatively positive hitsCC BY · PubMed Central
Fig. 2 Post-processing steps in HOPS. Three hierarchical post-processing steps are used in HOPS. a First, the edit distance distribution is required to show a decline. b Second, the alignments are assessed for C>T and G>A mismatches typical for aDNA; by default, any such damage is considered sufficient. c Third, the edit distance distribution of reads showing damage is evaluated
Fig. 2 Post-processing steps in HOPS. Three hierarchical post-processing steps are used in HOPS. a First, the edit distance distribution is required to show a decline. b Second, the alignments are assessed for C>T and G>A mismatches typical for aDNA; by default, any such damage is considered sufficient. c Third, the edit distance distribution of reads showing damage is evaluatedCC BY · PubMed Central
Fig. 3 Assignment of simulated reads to taxonomic levels for 33 bacterial pathogens. The fraction of simulated reads (red gradient) per reference (y-axis) assigned to a specific node across different levels of the taxonomy (x-axis). The levels of taxonomy not defined for a species are shown in gray
Fig. 3 Assignment of simulated reads to taxonomic levels for 33 bacterial pathogens. The fraction of simulated reads (red gradient) per reference (y-axis) assigned to a specific node across different levels of the taxonomy (x-axis). The levels of taxonomy not defined for a species are shown in grayCC BY · PubMed Central
Fig. 4 Comparison of the number of successfully recovered Y. pestis reads using standard (SD) and damage-tolerant (DT) MALT with minimum percent identities of a 99%, b 95%, and c 85%. Shown are the recovered reads from the “default” (all reads) and “ancient” (reads with damage) modes in MALT, with the same 500 reads being spiked into the metagenomic backgrounds. Error bars show the standard error of five independent technical replicates for each analysis
Fig. 4 Comparison of the number of successfully recovered Y. pestis reads using standard (SD) and damage-tolerant (DT) MALT with minimum percent identities of a 99%, b 95%, and c 85%. Shown are the recovered reads from the “default” (all reads) and “ancient” (reads with damage) modes in MALT, with the same 500 reads being spiked into the metagenomic backgrounds. Error bars show the standard error of five independent technical replicates for each analysisCC BY · PubMed Central
Fig. 5 Performance comparison of HOPS, Kraken, SPARSE, metaBIT, and MIDAS. a Number of species that have been correctly identified in the simulated data sets by each of the programs. The bar plot on the upper left shows the percentage of data sets with 50 simulated reads for which the correct species has been identified. The other bar plots show the number of species that have been correctly identified in data sets with 50, 500, and 5000 simulated reads, respectively. b Number of target species identified in the metagenomic background (negative controls) without any spiked-in species-derived data for each of the tested programs
Fig. 5 Performance comparison of HOPS, Kraken, SPARSE, metaBIT, and MIDAS. a Number of species that have been correctly identified in the simulated data sets by each of the programs. The bar plot on the upper left shows the percentage of data sets with 50 simulated reads for which the correct species has been identified. The other bar plots show the number of species that have been correctly identified in data sets with 50, 500, and 5000 simulated reads, respectively. b Number of target species identified in the metagenomic background (negative controls) without any spiked-in species-derived data for each of the tested programsCC BY · PubMed Central

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Cites

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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

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Metadata

Authors
Hübler, R., Key, F. M., Warinner, C., Bos, K. I., Krause, J., & Herbig, A.
Affiliations
Max Planck Institute of Geoanthropology (DE)
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2019-12
Volume
20
Issue
1
Pages
280-280
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-019-1903-0
PMC
PMC6913047
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
Licence
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

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