mykrobe-parser
mykrobe-parser parses Mykrobe predictor output to report species identification and antibiotic resistance profiles for Staphylococcus aureus and Mycobacterium tuberculosis from sequence data.
Key Features:
- Parser implementation: Implemented as an R script that parses and summarizes output from the Mykrobe predictor.
- de Bruijn graph analysis: Uses de Bruijn graph representation of bacterial diversity to identify species and resistance-associated variants.
- Rapid analysis: Can generate comprehensive resistance profiles in approximately three minutes.
- Reported performance: For Staphylococcus aureus, achieves 99.1% sensitivity and 99.6% specificity across 12 antibiotics (validation n=470), and for Mycobacterium tuberculosis, 82.6% sensitivity and 98.5% specificity (validation n=1,609).
- Minor allele detection: Incorporates minor alleles to enhance detection of extremely drug-resistant strains.
- Nanopore compatibility: Compatible with single-molecule nanopore sequencing (Oxford Nanopore Technologies).
- Updated mutation catalogue: Includes an updated mutation catalogue that improves detection of pyrazinamide resistance.
- Customizable resistance catalogues: Allows definition of custom resistance catalogues for tailored analyses.
- Enhanced species identification: Improved algorithms for identification of non-tuberculous mycobacterial species.
- Statistical model updates: Includes an updated statistical model optimized for Oxford Nanopore Technologies sequencing data.
Scientific Applications:
- Global surveillance: Provides drug susceptibility profiles to support surveillance of antibiotic resistance.
- Therapeutic regimen design: Supplies resistance predictions with high concordance to phenotypic drug susceptibility testing (DST) to inform personalized treatment regimens.
- M. tuberculosis resistance management: Supports analysis and management of complex drug resistance patterns in Mycobacterium tuberculosis.
Methodology:
Parses Mykrobe predictor output (R script) using de Bruijn graph representation, integrates minor-allele detection, applies an updated mutation catalogue and a statistical model optimized for Oxford Nanopore Technologies, and is compatible with single-molecule nanopore sequencing.
Topics
Details
- License:
- Apache-2.0
- Maturity:
- Mature
- Tool Type:
- command-line tool
- Programming Languages:
- R
- Added:
- 3/12/2024
- Last Updated:
- 11/7/2024
Operations
Data Inputs & Outputs
Analysis
Publications
Bradley P, Gordon NC, Walker TM, Dunn L, Heys S, Huang B, Earle S, Pankhurst LJ, Anson L, de Cesare M, Piazza P, Votintseva AA, Golubchik T, Wilson DJ, Wyllie DH, Diel R, Niemann S, Feuerriegel S, Kohl TA, Ismail N, Omar SV, Smith EG, Buck D, McVean G, Walker AS, Peto TEA, Crook DW, Iqbal Z. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nature Communications. 2015;6(1). doi:10.1038/ncomms10063. PMID:26686880. PMCID:PMC4703848.
Hunt M, Bradley P, Lapierre SG, Heys S, Thomsit M, Hall MB, Malone KM, Wintringer P, Walker TM, Cirillo DM, Comas I, Farhat MR, Fowler P, Gardy J, Ismail N, Kohl TA, Mathys V, Merker M, Niemann S, Omar SV, Sintchenko V, Smith G, Soolingen Dv, Supply P, Tahseen S, Wilcox M, Arandjelovic I, Peto TEA, Crook DW, Iqbal Z. Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe. Wellcome Open Research. 2019;4:191. doi:10.12688/wellcomeopenres.15603.1. PMID:32055708. PMCID:PMC7004237.