Escherichia coli rifampicin resistance mutation prediction method based on naive Bayes model

A Bayesian model, E. coli technology, used in prediction, genomics, instrumentation, etc.

Active Publication Date: 2021-05-04
JINAN UNIVERSITY
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Problems solved by technology

However, current research can only detect and analyze the rpoB gene mutation characteristics of isolated rifampicin-resistant strains through DNA sequencing and other molecular biology methods, and cannot predict the bases at specific sites and the corresponding amino acid changes caused by them in advance. Predict whether it will produce a mutant phenotype

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  • Escherichia coli rifampicin resistance mutation prediction method based on naive Bayes model
  • Escherichia coli rifampicin resistance mutation prediction method based on naive Bayes model
  • Escherichia coli rifampicin resistance mutation prediction method based on naive Bayes model

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Embodiment

[0037] A method for predicting Escherichia coli rifampicin resistance mutations based on naive Bayesian model, comprising the following steps:

[0038] (1) Collect literature (Garibyan, L., Use of the rpoB gene to determine the specificity of base substitution mutations on the Escherichia colichromosome. DNA Repair, 2003.2(5): p.593-608.) Reports that can cause rifampicin Amino acid mutation sites and mutations in Escherichia coli RNA polymerase beta subunit (RpoB) of resistance results (positive mutations); figure 1 It is the statistical result of the mutation times of each amino acid in the Escherichia coli RNA polymerase β subunit in the above-mentioned literature; Table 1 is the mutation site and result of each amino acid of RpoB in the above-mentioned literature (67 kinds in total):

[0039] Table 1: Mutation sites and results of each amino acid of RpoB in the literature

[0040]

[0041] Table 2

[0042]

[0043] (2) On the NCBI website, search for Protein with R...

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Abstract

The invention discloses an Escherichia coli rifampicin resistance mutation prediction method based on a naive Bayes model. The method comprises the following steps: collecting Escherichia coli RNA polymerase beta subunit rifampicin resistance positive mutants in an existing report; collecting mutants of a bacterial RNA polymerase beta subunit in an existing report, removing sites consistent with positive mutation of escherichia coli, and taking the mutants as negative mutants; predicting a protein property parameter data set of the obtained positive mutant and negative mutant; and training a naive Bayesian model by using the obtained protein property parameter data set, and predicting a mutation phenotype after amino acid mutation at each position in a rifampicin resistance interval in escherichia coli RpoB by using the model. The model constructed by the invention has good prediction performance on escherichia coli rifampicin resistance mutation phenotypes, and the AUC value under an ROC curve in model evaluation is 0.77; and the accuracy is high and can reach 75.4%.

Description

technical field [0001] The invention belongs to the field of bacterial resistance mutation detection, in particular to a method for predicting Escherichia coli rifampicin resistance mutation based on a naive Bayesian model. Background technique [0002] Antimicrobial resistance is one of the greatest threats to global health, food security and development today. Among them, bacterial resistance to the broad-spectrum antibiotic rifampicin is becoming common. Studies have shown that the resistance of bacteria to such drugs is mainly due to changes in the β subunit of RNA polymerase encoded by the rpoB gene. The acquisition of resistance genes by bacteria through new mutations greatly accelerates the development and spread of bacterial resistance. Once the genes change, they can be transferred horizontally to offspring or spread among adjacent bacterial cells to enhance resistance. [0003] At present, a large number of studies have reported that base substitutions and deleti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G16B20/50G06Q10/10
CPCG06Q10/04G16B20/50G06Q10/103
Inventor 王大力宁清吴凡游静
Owner JINAN UNIVERSITY
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