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Indexed variation graphs for efficient and accurate resistome profiling

View ORCID ProfileWill P.M. Rowe, View ORCID ProfileMartyn D Winn
doi: https://doi.org/10.1101/270835
Will P.M. Rowe
1Institute of Integrative Biology, The University of Liverpool,
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  • For correspondence: will.rowe@stfc.ac.uk
Martyn D Winn
2Scientific Computing Department and The Hartree Centre, STFC Daresbury Laboratory,
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  • For correspondence: martyn.winn@stfc.ac.uk
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Abstract

Background Antimicrobial resistance remains a major threat to global health. Profiling the collective antimicrobial resistance genes within a metagenome (the “resistome”) facilitates greater understanding of antimicrobial resistance gene diversity and dynamics. In turn, this can allow for gene surveillance, individualised treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools.

Results Our method combines a variation graph representation of gene sets with an LSH Forest indexing scheme to allow for fast classification of metagenomic sequence reads using similarity-search queries. Subsequent hierarchical local alignment of classified reads against graph traversals enables accurate reconstruction of full-length gene sequences using a scoring scheme. We provide our implementation, GROOT, and show it to be both faster and more accurate than a current reference-dependent tool for resistome profiling. GROOT runs on a laptop and can process a typical 2 gigabyte metagenome in 2 minutes using a single CPU.

Conclusion We present a method for resistome profiling that utilises a novel index and search strategy to accurately type resistance genes in metagenomic samples. The use of variation graphs yields several advantages over other methods using linear reference sequences. Our method is not restricted to resistome profiling and has the potential to improve current metagenomic workflows. The implementation is written in Go and is available at https://github.com/will-rowe/groot (MIT license).

  • List of abbreviations

    AMR
    Antimicrobial Resistance
    ARG
    Antimicrobial Resistance Gene
    DAG
    Directed Acyclical Graph
    DFS
    Depth-First Search
    GROOT
    Graphing Resistance Out Of meTagenomes
    MSA
    Multiple Sequence Alignment
    PCR
    Polymerase Chain Reaction
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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    Posted February 28, 2018.
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    Indexed variation graphs for efficient and accurate resistome profiling
    Will P.M. Rowe, Martyn D Winn
    bioRxiv 270835; doi: https://doi.org/10.1101/270835
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    Indexed variation graphs for efficient and accurate resistome profiling
    Will P.M. Rowe, Martyn D Winn
    bioRxiv 270835; doi: https://doi.org/10.1101/270835

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