phagetb 1.6
A multilevel prediction method for predicting interactions between bacteriophages and pathogenic bacterial hosts
Requires Python: >=3.6


###PhageTB

PhageTB is a multilevel prediction method for predicting interactions between bacteriophages and pathogenic bacterial hosts. This study develops a novel host prediction method for predicting hosts of query phages by their genome sequences utilizing alignment-based and alignment-free features.

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###nstallation

To install the package, type the following command:

pip install phagetb

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###Getting Started

This project is hosted on [PhageTB](https://webs.iiitd.edu.in/raghava/phagetb/) and can be accessed by clicking on the link above or can be used as a standalone application by downloading the source code from this GitHub repository.

There are 3 prediction methods available in this model. 

1. Predict The Bacterial Host For A Query Phage (model.py (phagetb))

This Module Allows Users To Predict The Bacterial Hosts Corresponding To The Query Phages using the genome sequence of the phage. 

2. Predict The Lytic Phage For Query Bacteria (model_bacteria.py (phagetb_1))

This Module Allow Users To Predict The Target Phage Likely To Infect Query Bacteria.

3. Predict Interaction Of Query Phage-Bacteria Pair (model_phage_host_pair.py (phagetb_2))

This Module Allows Users To Predict Whether Given Phage And Bacterial Hosts Are Likely To Interact With One Another. The prediction from this module for the phage is used as a 
query for the BLAST task (blastn) against the query bacterial host. The BLAST task is performed using the NCBI BLAST+ tool. The BLAST output is parsed, and if the predicted host and query host have a similarity higher than the threshold, then the phage-host pair is predicted to interact.

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###Minimum Usage

Following is the complete list of all options (with default values) that can be used to run the model. you may get these options by " phagetb -h" (and similarly for other modules).

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1. model.py (phagetb)

usage: phagetb [-h] -i INPUT [-o OUTPUT] [-l LEVELS [LEVELS ...]]

Please provide the following arguments

optional arguments:

  -h, --help            show this help message and exit

  -i INPUT, --input INPUT
 
                        Input: genome sequence of the phage in FASTA format or
 
                        single sequence per line in single letter code
 
  -o OUTPUT, --output OUTPUT
 
                        Output: File for saving results by default outfile.csv
 
  -l LEVELS [LEVELS ...], --levels LEVELS [LEVELS ...]
 
                        Levels: 1: Blast against phage reference DB, 2: Blast
                        against host reference DB, 3: Integrated model, 4:
                        CRISPR by default level is 1
                        
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2. model_bacteria.py (phagetb_1)

usage: phagetb_1 [-h] -i INPUT [-o OUTPUT] [-l LEVELS [LEVELS ...]]
                         [-n NUM_OF_REF_HOSTS] [-t THRESHOLD] [--only_blast]

Please provide the following arguments

optional arguments:

  -h, --help            show this help message and exit

  -i INPUT, --input INPUT

                        Input: genome sequence of the bacteria in FASTA format
                        or single sequence per line in single letter code

  -o OUTPUT, --output OUTPUT

                        Output: File for saving results by default outfile.csv

  -l LEVELS [LEVELS ...], --levels LEVELS [LEVELS ...]

                        Levels: 1: Blast against phage reference DB, 2: Blast
                        against host reference DB, 3: Integrated model, 4:
                        CRISPR by default level is 1

  -n NUM_OF_REF_HOSTS, --num_of_ref_hosts NUM_OF_REF_HOSTS

                        Number of reference hosts to consider by default number is 1

  -t THRESHOLD, --threshold THRESHOLD

                        Threshold: evalue threshold for similarity score by default e-value threshold is 0.01

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3. model_phage_host_pair.py (phagetb_2)

usage: phagetb_2  [-h] -v INPUT_PHAGE -b INTPUT_BACTERIA
                                [-o OUTPUT] [-l LEVELS [LEVELS ...]]
                                [-t THRESHOLD]

Please provide the following arguments

optional arguments:

  -h, --help            show this help message and exit

  -v INPUT_PHAGE, --input_phage INPUT_PHAGE

                        Input: genome sequence of the phage in FASTA format or

                        single sequence per line in single letter code

  -b INTPUT_BACTERIA, --intput_bacteria INTPUT_BACTERIA

                        Input: genome sequence of the bacteria in FASTA format
                        or single sequence per line in single letter code

  -o OUTPUT, --output OUTPUT

                        Output: File for saving results by default outfile.csv

  -l LEVELS [LEVELS ...], --levels LEVELS [LEVELS ...]

                        Levels: 1: Blast against phage reference DB, 2: Blast
                        against host reference DB, 3: Integrated model, 4:
                        CRISPR by default level is 1

  -t THRESHOLD, --threshold THRESHOLD

                        Threshold: e-value threshold for similarity score by default e-value threshold is 0.01

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###File descriptions

1. blastdb: The database of the hosts, phages and CRISPR sequences.
2. blast_binaries: The directory contains blastn to execute the similarity search for different OS.
3. base: The directory containing the extra files and pretrained model required for predictions.
4. model.py: The Python script for predicting hosts.
5. model_bacteria.py: The Python script for predicting target phages for a bacteria.
6. model_phage_host_pair.py: The Python script for predicting interaction for a phage and host pair.
7. genome_data: The directory containing the genome data of reference hosts

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###Address for contact

In case of any queries, feel free to reach out to us at 

Prof. G. P. S. Raghava, Head Department of Computational Biology,Indraprastha Institute of Information Technology (IIIT), 
Okhla Phase III, New Delhi 110020 ; Phone:+91-11-26907444; Email: raghava@iiitd.ac.in  Web: http://webs.iiitd.edu.in/raghava/

<b>Reference</b>: Aggarwal et al. (2023) An ensemble method for prediction of phage-based therapy against bacterial infections. Front. Microbiol., DOI: https://doi.org/10.3389/fmicb.2023.1148579