Drug resistance prediction for Mycobacterium tuberculosis with reference graphs
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.05.04.539481v1?rss=1

Authors: Hall, M. B., Lima, L., Coin, L. J. M., Iqbal, Z.

Abstract: The dominant paradigm for analysing genetic variation relies on a central idea: all genomes in a species can be described as minor differences from a single reference genome. However, this approach can be problematic or inadequate for bacteria, where there can be significant sequence divergence within a species. Reference graphs are an emerging solution to the reference bias issues implicit in the "single-reference" model. Such a graph represents variation at multiple scales within a population - e.g., nucleotide- and locus-level. The genetic causes of drug resistance in bacteria have proven comparatively easy to decode compared with studies of human diseases. For example, it is possible to predict resistance to numerous anti-tuberculosis drugs by simply testing for the presence of a list of single nucleotide polymorphisms and insertion/deletions, commonly referred to as a catalogue. We developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph, a process that can be replicated for other species. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the pileup-based approach of TBProfiler using 44,709 and 138 publicly available Illumina and Nanopore datasets with associated phenotypes. We find DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe on Illumina data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance.

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