resistance prediction
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BMC Genomics ◽  
2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Wouter Deelder ◽  
Gary Napier ◽  
Susana Campino ◽  
Luigi Palla ◽  
Jody Phelan ◽  
...  

Abstract Background Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. Results We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). Conclusion Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.


2022 ◽  
Author(s):  
Caroline Weis ◽  
Aline Cuénod ◽  
Bastian Rieck ◽  
Olivier Dubuis ◽  
Susanne Graf ◽  
...  

2022 ◽  
Vol 170 ◽  
pp. 108592
Author(s):  
Felipe Piana Vendramell Ferreira ◽  
Rabee Shamass ◽  
Vireen Limbachiya ◽  
Konstantinos Daniel Tsavdaridis ◽  
Carlos Humberto Martins

2021 ◽  
Vol 155 (A3) ◽  
Author(s):  
Lt(N) A Carter ◽  
E Muk-Pavic ◽  
T McDonald

Due to the novel hull form design, at present no standard series or full-scale data is publicly available to predict Tri- SWACH resistance during the preliminary ship design process. This work investigates the viability of using an Artificial Neural Network (ANN) to quickly predict total resistance for preliminary Tri-SWACH design. An ANN was trained using total resistance experimental data obtained from model tests, which varied side hull arrangements. The results highlight strong correlation for model resistance prediction. A Tri-SWACH case study was then developed which had side hull geometric properties different to any previously used to train the ANN. The results, validated against CFD predictions, mimicked the resistance pattern generated by other model experimental data, providing confidence in the ANN’s ability to function as a resistance prediction tool. This work demonstrates the viability of ANN to assess Tri-SWACH resistance as part of a preliminary design process. These results suggest that ANNs can be effective tools for assessing performance given relevant training data.


2021 ◽  
Vol Volume 14 ◽  
pp. 1575-1582
Author(s):  
Eric Rytkin ◽  
Karin Mirzaev ◽  
Irina Bure ◽  
Kristina Akmalova ◽  
Sherzod Abdullaev ◽  
...  

2021 ◽  
Vol 7 (11) ◽  
Author(s):  
Nilay Peker ◽  
Leonard Schuele ◽  
Nienke Kok ◽  
Miguel Terrazos ◽  
Stefan M. Neuenschwander ◽  
...  

Whole-genome sequencing (WGS) of Mycobacterium tuberculosis (MTB) isolates can be used to get an accurate diagnosis, to guide clinical decision making, to control tuberculosis (TB) and for outbreak investigations. We evaluated the performance of long-read (LR) and/or short-read (SR) sequencing for anti-TB drug-resistance prediction using the TBProfiler and Mykrobe tools, the fraction of genome recovery, assembly accuracies and the robustness of two typing approaches based on core-genome SNP (cgSNP) typing and core-genome multi-locus sequence typing (cgMLST). Most of the discrepancies between phenotypic drug-susceptibility testing (DST) and drug-resistance prediction were observed for the first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol, mainly with LR sequence data. Resistance prediction to second-line drugs made by both TBProfiler and Mykrobe tools with SR- and LR-sequence data were in complete agreement with phenotypic DST except for one isolate. The SR assemblies were more accurate than the LR assemblies, having significantly (P<0.05) fewer indels and mismatches per 100 kbp. However, the hybrid and LR assemblies had slightly higher genome fractions. For LR assemblies, Canu followed by Racon, and Medaka polishing was the most accurate approach. The cgSNP approach, based on either reads or assemblies, was more robust than the cgMLST approach, especially for LR sequence data. In conclusion, anti-TB drug-resistance prediction, particularly with only LR sequence data, remains challenging, especially for first-line drugs. In addition, SR assemblies appear more accurate than LR ones, and reproducible phylogeny can be achieved using cgSNP approaches.


2021 ◽  
Author(s):  
Induja Chandrakumar ◽  
Nick PG Gauthier ◽  
Cassidy Nelson ◽  
Michael B Bonsall ◽  
Kerstin Locher ◽  
...  

A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplit's taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases of Klebsiella pneumoniae bacteremia and Neisseria gonorrhoeae infection, BugSplit's taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available at https://bugseq.com/academic.


2021 ◽  
Author(s):  
Avika Dixit ◽  
Luca Freschi ◽  
Roger Vargas ◽  
Matthias I Groeschel ◽  
Sabira Tahseen ◽  
...  

Background: Global tuberculosis (TB) drug resistance (DR) surveillance is largely focused on the drug rifampicin. We leveraged public and surveillance M. tuberculosis (Mtb) whole genome sequencing (WGS) data, to generate more comprehensive country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction. Methods: We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to twelve drugs and bias-corrected for model performance, outbreak sampling, and resistance oversampling. We validated our estimates using a national DR survey conducted in South Africa. Results: Mtb isolates from 29 countries (n=19,149) met sequence quality criteria. Marginal genotypic resistance estimates overlapped with the South African DR survey for all drugs except isoniazid and second-line injectables that were underestimated (n=3,134); among multi-drug resistant (MDR) TB, estimates overlapped for pyrazinamide and the fluoroquinolones. Globally, mono-resistance to isoniazid was estimated at 10.9% (95% CI: 10.2-11.7%, n = 14,012. Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% [0.1-11%], n=111 and India 2.8% [0.08-9.4%], n=114). Rates of resistance discordance between isoniazid and ethionamide were high with 74.4% (IQR: 64.5-79.7%) of isoniazid resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3,964). Conclusions: This is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics. Our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug susceptible TB in South Asia and suggest that ethionamide is an under-utilized drug in MDR treatment.


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