Dataset Information

A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains.

ABSTRACT:

Motivation

Antimicrobial resistance (AMR) is becoming a huge problem in both developed and developing countries, and identifying strains resistant or susceptible to certain antibiotics is essential in fighting against antibiotic-resistant pathogens. Whole-genome sequences have been collected for different microbial strains in order to identify crucial characteristics that allow certain strains to become resistant to antibiotics; however, a global inspection of the gene content responsible for AMR activities remains to be done.

Results

We propose a pan-genome-based approach to characterize antibiotic-resistant microbial strains and test this approach on the bacterial model organism Escherichia coli. By identifying core and accessory gene clusters and predicting AMR genes for the E. coli pan-genome, we not only showed that certain classes of genes are unevenly distributed between the core and accessory parts of the pan-genome but also demonstrated that only a portion of the identified AMR genes belong to the accessory genome. Application of machine learning algorithms to predict whether specific strains were resistant to antibiotic drugs yielded the best prediction accuracy for the set of AMR genes within the accessory part of the pan-genome, suggesting that these gene clusters were most crucial to AMR activities in E. coli. Selecting subsets of AMR genes for different antibiotic drugs based on a genetic algorithm (GA) achieved better prediction performances than the gene sets established in the literature, hinting that the gene sets selected by the GA may warrant further analysis in investigating more details about how E. coli fight against antibiotics.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Her HL

PROVIDER: S-EPMC6022653 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains.

Her Hsuan-Lin HL Wu Yu-Wei YW

Bioinformatics (Oxford, England) 20180701 13

<h4>Motivation</h4>Antimicrobial resistance (AMR) is becoming a huge problem in both developed and developing countries, and identifying strains resistant or susceptible to certain antibiotics is essential in fighting against antibiotic-resistant pathogens. Whole-genome sequences have been collected for different microbial strains in order to identify crucial characteristics that allow certain strains to become resistant to antibiotics; however, a global inspection of the gene content responsibl ...[more]

PMID: 29949970

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Dataset Information

A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains.

Motivation

Results

Supplementary information

Publications

A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains.

Similar Datasets

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