Prokaryotic virus Host Predictor (PHP)

Prokaryotic virus Host Predictor (PHP) is a computational tool for host prediction of prokaryotic viruses based on Gaussian Model (GM). PHP takes the complete or partial genomic sequences of prokaryotic viruses as inputs. For each virus sequence, PHP automatically calculates the probability of host for 60,105 prokaryotic genomes, and takes the prokaryotic genome with the largest probability as the predicted host. PHP would output the name of prokaryotic genome with the largest score, and output the host score of all prokaryotic genomes.

Python packages pandas,numpy,sklearn are needed to be installed before Installation of phageHostPredictor. The program needs to run on the Linux operating system

Dependencies:

Python 3, numpy, pandas, scikit-learn

pip install pandas
pip install numpy
pip install -U scikit-learn

Usage:

Step 1: calculate the K-mer frequency of the host

python3 countKmer.py --fastaFileDir  ./exampleHostGenome --kmerFileDir ./exampleOutput --kmerName HostKmer  --coreNum -1

Or use the simplify command

python3 countKmer.py -f ./exampleHostGenome -d ./exampleOutput -n HostKmer -c -1

--fastaFileDir or -f: The fasta file of prokaryotic genome sequences, one genome per file.

--kmerFileDir or -d: The path of prokaryotic K-mer file.

--kmerName or -n: The name of prokaryotic K-mer file.

--coreNum or -c: The number of cores used in k-mer calculation. -1 represents the use of all cores.

*K-mer file of 60,105 prokaryotic genomes is saved in current folder and named hostKmer_60105_kmer4.tar.gz

*Users can directly use the K-mer file to run the step 2 after decompression.

*The taxonomy information of the 60,105 genomes is saved in /interactionTable/MARGE_PAIR_TAX.xls

Step 2: predict the infection relationship between the virus and the host

python3 PHP.py --virusFastaFileDir ./exampleVirusGenome  --outFileDir ./exampleOutput  --bacteriaKmerDir ./exampleOutput  --bacteriaKmerName HostKmer

Or use the simplify command

python3 PHP.py -v ./exampleVirusGenome  -o ./exampleOutput  -d ./exampleOutput  -n HostKmer

--virusFastaFileDir or -v: The fasta file of query virus sequences, one virus genome per file.

--outFileDir or -o: The path of temp files and result files.

--bacteriaKmerDir or -d: The path of prokaryotic K-mer file.

--bacteriaKmerName or -n: The name of prokaryotic K-mer file.

Interpretation of Result

After running the prediction program, you will see the output files Prediction_Maxhost and Prediction_Allhost in the output folder.

In document Prediction_Maxhost.tsv, The first column is the input virus，The third column is the highest score host and the second is the score of this host.

In document Prediction_Allhost.csv, query viruses and scores for all prokaryotic genomes are given.

The corresponding accuracy of different scoring thresholds is as follows.

Users use their own data to customize the model

Users can use their own data to train customized models, /exampleTrainingData/ provides sample data, each folder containing one pair of viruses and host genomes in exampleTrainingData.
The virus needs to be saved in the /phage/ folder, and the host needs to be saved in the /host/ folder.
Then run the following command to automatically train to get the model

python3 PHP_UserTrain.py --trainDataDir ./exampleTrainingData --outFileDir ./exampleOutput --modelName UserModel.m

--trainDataDir The training data folder, in which each pair of training data is saved in its own folder

--outFileDir The path to save the trained model

--modelName The name of the trained model

Users need to rename and replace the original PHP model to use the customized model

predict short virus query contig

If your input sequences contain sequences shorter than 12500 bp, the program will automatically identify these short segments and use the corresponding model to predict.

if 12500bp< length，The sequence will be predicted using a full length model;

if 7500bp < length <= 12500bp, The sequence will be predicted using a 10000bp model;

if 4000bp < length <= 7500bp, The sequence will be predicted using a 5000bp model;

if 2000 < length <= 4000bp, The sequence will be predicted using a 3000bp model;

if length<=2000bp, The sequence will be predicted using a 1000bp model;