Additional file 1 of Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics
Creators: name: Lu, Congyu
name: Zhang, Zheng
name: Cai, Zena
name: Zhu, Zhaozhong
name: Qiu, Ye
name: Wu, Aiping
name: Jiang, Taijiao
name: Zheng, Heping
name: Peng, Yousong
Publisher: name: figshare
Year: 2021
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Evolutionary Biology
subject: Ecology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Plant Biology
subject: Virology

description: Additional file 1: Table S1. The host prediction accuracies of GM, VHM and WIsH at taxonomic levels from genus to domain versus the number of top N predictions used when using the consensus method. The highest accuracies among three methods were highlighted in bold. Table S2. The host prediction accuracies of GM, VHM and WIsH at taxonomic levels from genus to domain versus the recall rate when using the threshold method. The highest accuracies among three methods were highlighted in bold. Table S3. Comparison of the time consumed in building models and prediction of virus hosts of PHP and WIsH on the VHM dataset. Figure S1. The comparisons of the taxonomic distribution of both viruses and hosts in the VHM and test datasets used in the manuscript. Figure S2. The host prediction accuracy of the Gaussian mixture model in the ten-fold cross-validations on the K-means clustering of the VHM dataset versus the length of k-mer (A) and the number of components in the Gaussian mixture model(B). Figure S3. The comparison between GM and other common machine learning algorithms on host prediction accuracy. All models took the k-mer frequency (k = 4) as features, and were trained with the default parameters. Negative samples are needed for building computational models with these machine-learning algorithms, and were obtained as follows: for each virus, a non-host prokaryote was randomly selected, which resulted in the same number of negative samples as the positive samples, i.e., virus-host interactions. The testing strategy mentioned in Fig. 1 was used to evaluate the prediction ability of these machine-learning algorithms. During the testing process, only the positive samples, i.e., virus-host interactions, were clustered since we aimed to predict viral hosts. The random forest (RF) algorithm was selected for further optimization since it was observed to perform best among these machine-learning algorithms. Two parameters, i.e., the number of decision trees (n_estimators) and the number of features to consider when looking for the best split (max_features), have a key impact on the performance of the RF algorithm. Besides, the length of k-mers used in the modeling may also have an influence on the performance of the RF algorithm. Therefore, the n_estimators, max_features, and the length of k-mers were further tuned to improve the RF algorithm. The RF algorithm with the “n_estimators” set to be 2000, “max_features” set to be “auto”, and k-mer length set to be 6 was found to perform best (see “Random forest with best parameters” in the figure). The modeling with these machine-learning algorithms was computed with the “scikit-learn” package in python. Figure S4. The distribution of k-mer frequency differences between virus and hosts genomes (blue), and those between virus and non-host prokaryotic genomes (orange). (A-H) represents the randomly selected k-mers of AGTT, AAAA,TTGC, CACG, AATT, TAGA, CGGG, TTCT, respectively. Figure S5. The taxonomic distribution of viruses in the VHM dataset.