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Comparative Study
, 20 (1), 280

HOPS: Automated Detection and Authentication of Pathogen DNA in Archaeological Remains

Affiliations
Comparative Study

HOPS: Automated Detection and Authentication of Pathogen DNA in Archaeological Remains

Ron Hübler et al. Genome Biol.

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.

Keywords: Ancient DNA; Ancient bacteria; Archaeogenetics; Metagenomics; Microbial archaeology; Paleopathology; Pathogen detection.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
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. 2
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. 3
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. 4
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. 5
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

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