wspc 0.0.6

Protein-content-based bacterial pathogenicity classifier

WSPC

Installation and dependencies

WSPC package can be installed via one of the following options:

1. Using pip install (via the command line):

pip install wspc

2. Using conda (on a conda environment):

conda install -c zivukelsongroup wspc

Dependencies:

• Python >=3.6
• Packages: pandas, numpy, scikit-learn, scipy

Command Line

In windows: make sure that the python "Scripts\" directory is added to PATH, so that the package can be executed as a command

Usage:

usage: wspc [-h] [-m {predict,fit}] -i I [-o OUTPUT] [-l LABELS_PATH] [--model_path MODEL_PATH] [-k K] [-t T]

optional arguments:
  -h, --help            show this help message and exit
  -m {predict,fit}, --mode {predict,fit}
  -i I                  input directory with genome *.txt files or a merged input *.fasta file
  -o OUTPUT, --output OUTPUT
                        output directory, default current directory
  -l LABELS_PATH, --labels_path LABELS_PATH
                        path to *.csv file with labels
  --model_path MODEL_PATH
                        path to a saved model in a *.pkl file. If not provided, saved pre-trained model will be used
  -k K                  parameter for training - selecting k-best features using chi2
  -t T                  parameter for training - clustering threshold

Predict:

You can predict the pathogenicity potentials of group of genomes using a saved model in a *.pkl file. If a path is not provided, saved pre-trained model will be used. The WSPC pre-trained model can be found in https://github.com/shakedna1/wspc_rep/blob/main/src/wspc/model/WSPC_model.pkl.

wspc -m predict -i path_to_input_genomes

Train:

Train a new model using the fit command.

You can train a new model using the same k (selecting k-best features using chi2) and t (clustering threshold) values of WSPC (450 and 0.18 respectively) or using a different values of your choice.

wspc -m fit -i path_to_input_genomes -l path_to_labels -k 450 -t 0.18

Reconstruction of Training and Prediction on the dataset from the paper

1. Download and extract the WSPC dataset (WSPC train set & WSPC test set) from https://github.com/shakedna1/wspc_rep/raw/main/Data/train_test_datasets.zip In Ubuntu:

      wget https://github.com/shakedna1/wspc_rep/raw/main/Data/train_test_datasets.zip
      unzip train_test_datasets.zip
   

2. Train:

   wspc -m fit -i train_genomes.fasta -l train_genomes_info.csv -k 450 -t 0.18
   

   The file trained_model.pkl will be saved in the same directory (or in the directory provided through the -o argument)

3. Test:

   wspc -m predict -i test_genomes.fasta --model_path trained_model.pkl
   

   The file predictions.csv will contain the predictions

Running WSPC as a python module

Below are a detailed running examples of WSPC as a python module:

1. Train a new model and predict genomes pathogenicity using the new model:

Imports:

import wspc

Train a new model:

X_train = wspc.read_genomes(path_to_genomes)
y = wspc.read_labels(path_to_labels, X_train)

model = wspc.fit(X_train, y, k=450, t=0.18)

Predict pathogenicity:

X_test = wspc.read_genomes(path_to_genomes)
predictions = wspc.predict(X_test, model)

2. Predict genomes pathogenicity using an exiting model:

Imports:

import wspc

Load a pre-trained model:

model_path - path to a saved model in a *.pkl file. If not provided, saved pre-trained model will be used

model = wspc.load_model(model_path)

Predict pathogenicity:

X_test = wspc.read_genomes(path_to_genomes)
predictions = wspc.predict(X_test, model)

WSPC input:

WSPC handle different types of input:

1. Input directory with genome *.tab and\or *.txt files:

   *.tab file - Public genomes on PATRIC database are available through a genomes directory. Each genome directory includes a .features.tab file, which provides all genomic features and related information in tab-delimited format, including PGFams information. For features.tab file example, look at the file: https://github.com/shakedna1/wspc_rep/blob/main/Data/Bacpacs/patric_files/1041522.28.PATRIC.features.tab

   *.txt file - Output file of the PATRIC annotation service for new genome. For more detailes on the file and the annotation service, see explanation at the section: "Obtain PATRIC Global Protein Families (PGFams) annotations for new sequenced genome" below.

2. Merged input *.fasta file: A merged file in a fasta format that contains concatenation of the PGFams information, which can be extracted from a *.tab file using the field: pgfam_id and from a *.txt file using the fiels: "pgfam".

   For the merged file exact format, look at the file: https://github.com/shakedna1/wspc_rep/blob/main/Data/train_genomes.fasta

   Example of the fasta file content:

   >1346.123
   PGF_10048015
   PGF_00062045
   PGF_00409415
   PGF_00766022
   PGF_02011026
   X
   X
   X
   PGF_07480521
   PGF_01162199
   PGF_03475877
   PGF_00876106
   PGF_06473395
   PGF_06429692
   PGF_00007012
   PGF_04788810
   

   • 1346.123 - genome name, the lines below the genome name represent the genome sequence of PGFam annotations. X represents a missing/un-annotated gene.

   Note that any protein family annotation IDs can be used, e.g., COGs, eggNOGs etc.

Obtain PATRIC Global Protein Families (PGFams) annotations for a newly sequenced genome:

PATRIC Provides Global Protein Families (PGFams) annotations service for new genomes. In order to generate PGFams annotations file for a new sequenced genome:

1. Use PATRIC's Genome Annotation Service: https://patricbrc.org/app/Annotation.

   For detailed instructions, Follow the instructions under the PATRIC genome annotations service documentation: https://docs.patricbrc.org/user_guides/services/genome_annotation_service.html

2. Download the resulting "Taxonomy name + label".txt file (click on view, then download. "Taxonomy name + label" is the genome name).

3. If you wish to create a merged *.fasta file for number of genomes, the column "pgfam" will be used for pgfam extraction.