
Customized databases and computational pipelines can effectively remove human reads and accurately classify Mycobacterium tuberculosis reads from simulated metagenomic samples, while maintaining low computational resource requirements.


coremsg

Background Culture-free real-time sequencing of clinical metagenomic samples promises both rapid pathogen detection and antimicrobial resistance profiling. However, this approach introduces the risk of patient DNA leakage. To mitigate this risk, we need near-comprehensive removal of human DNA sequence at the point of sequencing, typically involving use of resource-constrained devices. Existing benchmarks have largely focused on use of standardised databases and largely ignored the computational requirements of depletion pipelines as well as the impact of human genome diversity.

Results We benchmarked host removal pipelines on simulated Illumina and Nanopore metagenomic samples. We found that construction of a custom kraken database containing diverse human genomes results in the best balance of accuracy and computational resource usage. In addition, we benchmarked pipelines using kraken and minimap2 for taxonomic classification of Mycobacterium reads using standard and custom databases. With a database representative of the Mycobacterium genus, both tools obtained near-perfect precision and recall for classification of Mycobacterium tuberculosis . Computational efficiency of these custom databases was again superior to most standard approaches, allowing them to be executed on a laptop device.

Conclusions Nanopore sequencing and a custom kraken human database with a diversity of genomes leads to superior host read removal from simulated metagenomic samples while being executable on a laptop. In addition, constructing a taxon-specific database provides excellent taxonomic read assignment while keeping runtime and memory low. We make all customised databases and pipelines freely available.

### Competing Interest Statement

The authors have declared no competing interest.

*   FN
    :   false negative
    FP
    :   false positive
    HERV-K
    :   Human endogenous retrovirus K
    HLA
    :   human leukocyte antigen
    HPRC
    :   Human Pangenome Reference Consortium
    HRRT
    :   human read removal tool
    MTB
    :   Mycobacterium tuberculosis 
    MTBC
    :   Mycobacterium tuberculosis Complex
    NTM
    :   non-tuberculous mycobacteria
    TP
    :   true positive
    WGS
    :   whole-genome sequencing

Pangenome databases provide superior host removal and mycobacteria classification from clinical metagenomic data

efficient-computational-methods-for-host-removal-and-mycobacterial-classification-from-clinical-metagenomic-data


Efficient Computational Methods for Host Removal and Mycobacterial Classification from Clinical Metagenomic Data
