PHIAF

Code and Datasets for "PHIAF: Prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion"

Developers

Menglu Li ([email protected]) and Wen Zhang ([email protected]) from College of Informatics, Huazhong Agricultural University.

Datasets

• data/phage_dna_norm_features is a set of files with features encoded by DNA sequences corresponding to all phages.
• data/host_dna_norm_features is a set of files with features encoded by DNA sequences corresponding to all hosts.
• data/phage_protein_normfeatures is a set of files with features encoded by protein sequences corresponding to all phages.
• data/host_protein_normfeatures is a set of files with features encoded by protein sequences corresponding to all hosts.
• data/data_pos_neg.txt is the dataset used to train and test prediction model, which contain 312 positive and 312 negative samples (304 phages and 235 hosts).
• data/alldata.txt is phage-host interactions extracted from databases, which contain 5399 interactions between 5331 phages and 235 hosts.

Code

Environment Requirement

The code has been tested running under Python 3.7.9. The required packages are as follows:

• numpy == 1.19.1
• pandas == 1.1.3
• biopython == 1.78
• torch == 1.4.0+cpu
• keras == 2.3.1
• scikit-learn == 0.23.2
• tensorflow == 1.15.0

Usage

git clone https://github.com/mengluli-web/PHIAF
cd PHIAF/code
python generate_data.py   ####using GAN to generate pseudo positive samples
python main.py

Users can use their own data to train prediction models.

For new host/phage, users can download the DNA and protein sequences from the NCBI database, and use code/compute_dna_features.py and code/compute_protein_features.py to compute the features derived from DNA and protein sequences.

Note:

In code/compute_dna_features.py, users need to install the iLearn tool [https://ilearn.erc.monash.edu/ or https://github.com/Superzchen/iLearn] and prepare .fasta file, this file is DNA sequences of all phages/hosts. (when you use iLearn to compute the DNA features, you should set the parameters k of Kmer and RCKmer as 3, not 2.)

In code/compute_protein_features.py, users need to prepare a .gb file of every phage/host.

Then users use generate_data.py and main.py to predict PHI.

Contact

Please feel free to contact us if you need any help.