Abstract
BackgroundAntimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections. It is important to understand AMR’s biological mechanisms for the development of new drugs and more rapid and accurate clinical diagnostics. The increasing availability of whole-genome SNP (single nucleotide polymorphism) information, obtained from whole-genome sequence data, along with AMR profiles provides an opportunity to use feature selection in machine learning to find AMR-associated mutations. This work describes the use of a supervised feature selection approach using deep neural networks to detect AMR-associated genetic factors from whole-genome SNP data.ResultsThe proposed method, DNP-AAP (deep neural pursuit – average activation potential), was tested on a Neisseria gonorrhoeae dataset with paired whole-genome sequence data and resistance profiles to five commonly used antibiotics including penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime. The results show that DNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae, and also provide a list of candidate genomic features (SNPs) that might lead to the discovery of novel AMR determinants. Logistic regression classifiers were built with the identified SNPs and the prediction AUCs (area under the curve) for penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime were 0.974, 0.969, 0.949, 0.994, and 0.976, respectively.ConclusionsDNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae. It also provides a list of candidate genes and intergenic regions that might lead to novel AMR factor discovery. More generally, DNP-AAP can be applied to AMR analysis of any bacterial species with genomic variants and phenotype data. It can serve as a useful screening tool for microbiologists to generate genetic candidates for further lab experiments.
Highlights
Antimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections
By looking into genomic variations between individuals, we can identify contributors to their phenotypic differences. This is why Single nucleotide polymorphism (SNP) are commonly used as markers to study the genetic cause of diseases and antimicrobial resistance, and used in plant and animal breeding programs to select superior varieties
We propose Deep neural pursuit – average activation potential (DNP-activation potential (AAP)) to identify known and discover new potential AMR-associated point mutations from whole-genome SNP data
Summary
Antimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections. In order to make such predictions, the variations between individual genomes are examined and related to phenotypes To this end, a genome-wide association study (GWAS) is commonly performed to detect associations between SNPs and AMR phenotypes [4]. A genome-wide association study (GWAS) is commonly performed to detect associations between SNPs and AMR phenotypes [4] This is one way to address the curse of dimensionality—the feature dimension being much higher than the sample size—in building models to predict phenotypes from genotypes. P-values from GWAS only indicate whether or not SNPs are related to a phenotype, but not how strongly they are related This is one reason why SNPs selected by GWAS are not always good predictors, and why we cannot completely rely on them as features to build predictive models. Machine learning algorithms can serve as an alternative and complementary method to GWAS
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