Upload Files
This web application is used to predict bacteriocin sequences from an input FASTA file. The user manual is provided in the 'Help' menu. To predict bacteriocin sequences, please select a method and upload a FASTA file with the sequences you want to predict using the 'Choose an input fasta file' upload box, following the file format shown at the bottom of this page.If you wish to add new data to the model (training set), please use the 'Add new bacteriocin sequences to training (FASTA)' upload box for bacteriocin sequences and the 'Add new non-bacteriocin sequences to training (FASTA)' upload box for non-bacteriocin sequences, respectively.
Once the necessary files are uploaded, click the 'Bacteriocin prediction' button. This will automatically navigate you to the 'Predicted Bacteriocin Sequences' page once the classification results are generated. Then, click the 'Probability estimation' button to navigate to the 'Probability Scores' page containing the probability values of the sequences.
Download Results
The prediction and probability results can be downloaded by clicking the 'Prediction results' and 'Probability results' buttons, respectively. Additionally, positive and negative datasets, along with the training/testing datasets for all methods, are available in the 'Data' menu.
FASTA Formatting
An example FASTA file can be obtained by clicking on the 'Download Input Samples' button. To predict new sequences, the example FASTA file should be in the form shown below:
Download Input Samples
>BAC110
GGAPATSANAAGAAAIVGALAGIPGGPLGVVVGAVSAGLTTAIGSTVGSGSASSSAGGGS
>WP_007226314.1
MSSISKTVAKTFGLILFFISIANAEITGIVVSVTDGDTIKVLDENSNQHKVRLTGIDAPE
RGQPFGQASKKYLASMVSGKEVFVESNKKDRYGRVLGKVWVQPADCPSCGKTLDINHAQL
LAGMAWWYRYYAKQQSPEDRGRYESAEDEAKARGWGLWSAASPINPYNWRKGRR
>BAC112
NKWGNAVIGAATGATRGVSWCRGFGPWGMTACALGGAAIGGYLGYKSN
If you find our web application useful, please cite our following paper.
Citation:
Akhter, S. and Miller, J.H., BPAGS: A web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier. Frontiers in Bioinformatics, 3, p.1284705.
Download Files
Positive datasets
Positive datasets
Negative datasets
Negative datasets
Training datasets for BaPreS)
Training datasets for BaPreS
Training datasets for ADTree
Training datasets for ADTree
Training datasets for GA
Training datasets for GA
Training datasets for linear SVC
Training datasets for linear SVC
Training datasets for CVFE
Training datasets for CVFE
Training datasets for HFE
Training datasets for HFE
Testing datasets for BaPreS
Testing datasets for BaPreS
Testing datasets for ADTree
Testing datasets for ADTree
Testing datasets for GA
Testing datasets for GA
Testing datasets for linear SVC
Testing datasets for linear SVC
Testing datasets for CVFE
Testing datasets for CVFE
Testing datasets for HFE
Testing datasets for HFE