RT Journal Article SR Electronic(1) A1 Wang, Qian A1 Ye, Jun A1 Xu, Teng A1 Zhou, Ning A1 Lu, Zhongqiu A1 Ying, JianchaoYR 2021 T1 Prediction of prokaryotic transposases from protein features with machine learning approaches JF Microbial Genomics, VO 7 IS 7 OP SP 000611 DO https://doi.org/10.1099/mgen.0.000611 PB Microbiology Society, SN 2057-5858, AB 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., UL https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000611