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Abstract

Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.

Funding
This study was supported by the:
  • Start-up funds from the First Affiliated Hospital of Wenzhou Medical University (Award 2018QD014)
    • Principle Award Recipient: JianchaoYing
  • Science & Technology Project of Inner Mongolia Autonomous Region, China (Award 201802125)
    • Principle Award Recipient: TengXu
  • Fundamental Research Funds for the Zhejiang Provincial Universities (Award KYYW201919)
    • Principle Award Recipient: JianchaoYing
  • Natural Science Foundation of Zhejiang Province (Award LQ20H150004)
    • Principle Award Recipient: JianchaoYing
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
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2021-07-26
2021-10-24
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