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Classification of bacterial plasmid and chromosome derived sequences using machine learning.

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Machine learning accurately distinguishes bacterial plasmids from chromosomes in draft genomes. A neural network model effectively identifies plasmid DNA sequences, aiding antimicrobial resistance and virulence studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Plasmids are crucial genetic elements driving horizontal gene transfer, contributing to bacterial virulence and antimicrobial resistance.
  • Draft bacterial genome sequences often contain numerous contigs, complicating the distinction between chromosomal and plasmid DNA.
  • Accurate identification of plasmid sequences is essential for understanding bacterial evolution and pathogen dynamics.

Purpose of the Study:

  • To develop and evaluate machine learning models for classifying bacterial chromosomal and plasmid sequences from draft genomes.
  • To identify the most effective machine learning approach for distinguishing plasmid contigs.
  • To provide a computational tool for analyzing short-read bacterial assemblies.

Main Methods:

  • Utilized a training dataset of 10,584 chromosomes and 10,654 plasmids from the PATRIC database.
  • Evaluated machine learning models including random forest, logistic regression, XGBoost, and a neural network.
  • Employed nucleotide k-mers, specifically 6-mers, as features for sequence classification using 5kb subsequences.

Main Results:

  • A neural network model using nucleotide 6-mers achieved the highest performance with an average accuracy of 89.38% ± 2.16% via 10-fold cross-validation.
  • Model accuracy improved to 92.08% when employing a voting strategy for classifying holdout sequences.
  • Sequences involved in horizontal gene transfer, such as hypothetical proteins and mobile elements, were most frequently misclassified.

Conclusions:

  • A neural network model offers a robust and accurate method for identifying plasmid sequences in draft bacterial genomes.
  • This approach bypasses the need for traditional sequence alignment tools, simplifying plasmid identification.
  • The developed model aids in understanding the spread of antimicrobial resistance and virulence factors encoded on plasmids.