Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe

Abstract

Two billion people are infected with , leading to Mycobacterium tuberculosis 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, , which provided offline species identification and drug Mykrobe predictor resistance predictions for from whole genome sequencing M. tuberculosis (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations.

Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen,Shigella sonnei

“…We demonstrate that the population structure of S. sonnei can be represented by a robust maximum likelihood phylogeny and define within it 137 subtrees on the basis of pairwise divergence and epidemiological coherence, which we designate as hierarchically nested genotypes. Further, we provide a software package implemented within the Mykrobe 31 code base, which can identify both the S. sonnei genotype and AMR determinants direct from short-read sequence files in a few seconds.…”

“…We modified the Mykrobe genotyping software 31 to probe for these marker SNVs and assign S. sonnei genotypes. Probes to detect changes at specific S. sonnei QRDR codons (GyrA-83, GyrA-87, ParC-80) were also included in the S. sonnei panel in Mykrobe 31 (v0.9.0) software available at https://github.com/Mykrobe-tools/mykrobe, using the probe panel stored in Figshare (https://doi.org/10.6084/m9.figshare.13072646). Mykrobe outputs were then parsed using a custom python script at (https://github.com/katholt/sonneityping).…”

“…The validation and discovery datasets were subjected to mapping, SNV calling and phylogenetic analysis as described above, generating a recombination-filtered core-genome alignment of 32,138 SNVs in 3,696 isolates, and a ML phylogeny ( Supplementary Figure 3, interactive tree available in Microreact at https://microreact.org/project/g8BvA2JCXWaZNDyPyjsWXF). All genomes were assigned a genotype using Mykrobe 31 v0.9.0, and these were compared to the ML phylogeny to check that each genotype was monophyletic as expected (using the function is.monophyletic in the ape package 42 for R).…”

“…Here, we describe the global population structure for S. sonnei and (i) propose a hierarchical SNV-based genotyping scheme, which we define using 1,935 globally distributed genomes; (ii) implement the scheme within the free and open-source Mykrobe 31 software alongside detection of genetic determinants that are highly predictive of AMR phenotypes in S. sonnei 27 ; and (iii) validate this approach to genotyping using an additional 2,015 genomes that were sequenced in public health laboratories and deposited in the publicly available GenomeTrakr database. By applying this novel genotyping framework to S. sonnei WGS data generated in public health laboratories on three continents, we demonstrate the utility of the new scheme for identifying, tracking and reporting emerging AMR clones both within and between jurisdictions.…”

RapidMycobacterium tuberculosisspoligotyping from uncorrected long reads using Galru

“…The largest public dataset of Nanopore data for M. tuberculosis contains 5 samples which were sequenced using short and long read sequencing technologies (Hunt et al , 2019). The full list of accession numbers is available in Table 1.…”