Classification of bacterial plasmid and chromosome derived sequences using machine learning

Journal Article · Fri Dec 16 00:00:00 EST 2022 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0279280· OSTI ID:2320222

Zou, Xiaohui ^[1]; Nguyen, Marcus ^[2]; Overbeek, Jamie ^[2]; Cao, Bin ^[1]; Davis, James J. ^[2]

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1. China-Japan Friendship Hospital, Beijing (China)
2. Argonne National Laboratory (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer—including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements—were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 2320222
• Journal Information: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 12 Vol. 17; ISSN 1932-6203
• Publisher: Public Library of Science
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
antimicrobial resistance
bacterial genomics
bacterial virulence
bacteriophages
horizontal gene transfer
machine learning
mobile genetic elements
neural networks
plasmids
sequence databases
transposable elements