Exploring Antimicrobial Resistance Prediction Using Post-hoc Interpretable Methods

2019 ◽  
pp. 93-107
Author(s):  
Bernardo Cánovas-Segura ◽  
Antonio Morales ◽  
Antonio López Martínez-Carrasco ◽  
Manuel Campos ◽  
Jose M. Juarez ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Resistance Prediction ◽  
Post Hoc

scholarly journals Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

2022 ◽  
Author(s):  
Caroline Weis ◽  
Aline Cuénod ◽  
Bastian Rieck ◽  
Olivier Dubuis ◽  
Susanne Graf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Mass Spectra ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
Resistance Prediction

scholarly journals Genomic Prediction of Antimicrobial Resistance: Ready or Not, Here It Comes!

2020 ◽  
Vol 66 (10) ◽  
pp. 1278-1289
Author(s):  
Eric M Ransom ◽  
Robert F Potter ◽  
Gautam Dantas ◽  
Carey-Ann D Burnham
Keyword(s):  
Antimicrobial Resistance ◽  
Diagnostic Testing ◽  
Turnaround Time ◽  
Added Value ◽  
Clinical Settings ◽  
Bioinformatic Pipeline ◽  
Current State ◽  
Resistance Prediction ◽  
Diagnostic Microbiology ◽  
Next Generation Sequencing Ngs

Abstract Background Next-generation sequencing (NGS) technologies are being used to predict antimicrobial resistance. The field is evolving rapidly and transitioning out of the research setting into clinical use. Clinical laboratories are evaluating the accuracy and utility of genomic resistance prediction, including methods for NGS, downstream bioinformatic pipeline components, and the clinical settings in which this type of testing should be offered. Content We describe genomic sequencing as it pertains to predicting antimicrobial resistance in clinical isolates and samples. We elaborate on current methodologies and workflows to perform this testing and summarize the current state of genomic resistance prediction in clinical settings. To highlight this aspect, we include 3 medically relevant microorganism exemplars: Mycobacterium tuberculosis, Staphylococcus aureus, and Neisseria gonorrhoeae. Last, we discuss the future of genomic-based resistance detection in clinical microbiology laboratories. Summary Antimicrobial resistance prediction by genomic approaches is in its infancy for routine patient care. Genomic approaches have already added value to the current diagnostic testing landscape in specific circumstances and will play an increasingly important role in diagnostic microbiology. Future advancements will shorten turnaround time, reduce costs, and improve our analysis and interpretation of clinically actionable results.

scholarly journals Antimicrobial Resistance Prediction in PATRIC and RAST

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
James J. Davis ◽  
Sébastien Boisvert ◽  
Thomas Brettin ◽  
Ronald W. Kenyon ◽  
Chunhong Mao ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Resistance Prediction

scholarly journals BugSplit: highly accurate taxonomic binning of metagenomic assemblies enables genome-resolved metagenomics

2021 ◽  
Author(s):  
Induja Chandrakumar ◽  
Nick PG Gauthier ◽  
Cassidy Nelson ◽  
Michael B Bonsall ◽  
Kerstin Locher ◽  
...  
Keyword(s):  
Microbial Community ◽  
Antimicrobial Resistance ◽  
Klebsiella Pneumoniae ◽  
In Silico ◽  
Reference Database ◽  
Specific Detection ◽  
Novel Method ◽  
Resistance Prediction ◽  
Novel Coronavirus ◽  
Taxonomic Binning

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.

scholarly journals Antimicrobial resistance prediction and phylogenetic analysis of Neisseria gonorrhoeae isolates using the Oxford Nanopore MinION sequencer

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Daniel Golparian ◽  
Valentina Donà ◽  
Leonor Sánchez-Busó ◽  
Sunniva Foerster ◽  
Simon Harris ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Antimicrobial Resistance ◽  
Neisseria Gonorrhoeae ◽  
Oxford Nanopore ◽  
Resistance Prediction

scholarly journals Whole Genome Sequence based Capsular Typing and Antimicrobial Resistance Prediction of Group B Streptococcal Isolates from Colonized Pregnant Women in Nigeria

2021 ◽  
Author(s):  
Mienye Bob-Manuel ◽  
Lesley McGee ◽  
Jeremiah Igunma ◽  
Mary Alex-Wele ◽  
Orikomaba Obunge ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Pregnant Women ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Group B Streptococcus ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Resistance Prediction ◽  
Group B ◽  
Capsular Typing

Abstract Background: Streptococcus agalactiae (Group B Streptococcus, GBS) is one of the major bacterial pathogens responsible for neonatal sepsis. Whole genome sequencing has, in recent years, emerged as a reliable tool for capsular typing and antimicrobial resistance prediction. This study characterized vaginal and rectal isolates of Group B Streptococcus obtained from pregnant women in Port Harcourt, Nigeria using a whole-genome sequence-based approach.Results: Capsular types Ia, Ib, II, III, IV and V were detected among the 43 isolates sequenced. Twelve sequence types (STs) were identified, with ST19 (n=9, 27.3%) and ST486 (n=5, 15.2%) the most frequent among non-duplicated isolates. Of the alpha-like proteins (alp) identified, Alp1 was the most prevalent in 11 (33.3%) isolates. Macrolide and lincosamide resistance determinants were present in 15 (45.5%) isolates; ermB was detected in 1 (3%) and ermTR in 7 (21.2%) isolates. The lnu gene, was detected in 6 (18.2%) and mef was identified in 3 (9.1%) isolates. Resistance of GBS to erythromycin and clindamycin was found to be 30.3% and 24.2%, respectively. All isolates were resistant to tetracycline with only the tetM gene identified. Fluoroquinolone-resistance conferring substitutions in gyrA + parC were detected in 9 (27.3%) isolates and chloramphenicol resistance was predicted in 11 (33.3%).Conclusion: The data available from the whole genome sequencing of these isolates offers a small but insightful description of common serotypes and resistance features within colonizing GBS in Nigeria.

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