AVPIden

A prediction scheme for identification and functional characterization of antiviral peptides.

Requirements

We have already integrate the environment in env.yaml. execute conda create -f env.yaml to install packages required in a new created AVPIden conda env.

Enter the enviornment with conda activate AVPIden before further executions.

Frameworks for establishment of AVPIden

• feature_extract.py: modules and executions to extract features from the sequences loacted at Fasta directory. The result feature representations of all sequences are loacted at the data directory. You should run this before making classification.
• Args.py: Parameters of how to perform classification. You can set the parameter stage as one of the string from ['Entire', 'ByFamily', 'ByVirus'] to perform classifier establishment and evaluation of first stage indentification, second stage classificatoin (by virus families), or second stage classification (by specific virus), respectively.
• classify.py: Make classification for different antiviral peptide identification and functional characterization tasks. The evaluation of classification are also included.
• utils.py utilities of the framework
• If you want to illustrtes the features or classification results, funtions located in feature_description.py or plot_utils.py may help you.
• Fasta original fasta sequences collection of construction/evaluation of AVPIden.

Cite us

@article{10.1093/bib/bbab263,
    author = {Pang, Yuxuan and Yao, Lantian and Jhong, Jhih-Hua and Wang, Zhuo and Lee, Tzong-Yi},
    title = "{AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches}",
    journal = {Briefings in Bioinformatics},
    year = {2021},
    month = {07},
    issn = {1477-4054},
    doi = {10.1093/bib/bbab263},
    url = {https://doi.org/10.1093/bib/bbab263},
    note = {bbab263},
}