KEVOLVE

• KEVOLVE v1.2 Help file
• K-mers based feature identifier for viral genomic classification
• Copyright (C) 2023 Dylan Lebatteux and Abdoulaye Banire Diallo
• Author : Dylan Lebatteux
• Contact : [email protected]

Description

KEVOLVE, an approach based on a Genetic Algorithm including a Machine-Learning kernel to extract a bag of minimal subsets of k-mers features maximizing a given classification score threshold.

Required softwares

• python
• scikit-learn
• numpy
• biopython
• joblib

Parameters

List of parameters requiring adjustment in the configuration_file.ini :

• k : Length of k-mers
• model_path : Path of the prediction models directory
• training_fasta : Training fasta file path
• testing_fasta: Testing fasta file path
• reference_sequence : Reference sequence file path (file to format GenBank)
• k_mers_path : Path of the directory to save the extracted sets of k-mers
• prediction_path : Path of the directory to save the predictions
• analysis_report_path : Path of the directory to save the the analysis reports
• k_mers_to_analyze_path : Path of the file of k-mers to analyze
• n_iterations : Maximum number of generations of the genetic algorithm
• n_solutions : Maximum number of solutions to identify
• n_chromosomes : Number of feature subset generated at each iteration
• n_genes : Number of k-mers in the initial population
• objective_score : Score to be achieved by the objective function (default = 0.99)
• mutation_rate : Initial mutation rate (default = 0.1)
• crossover_rate : Percentage of crossover in each generation (default = 0.2)
• evaluation_mode : Evaluation mode during the prediction (True/False), require labelled sequences for the predicted set

Utilization

1. Specify the parameters of the previous section in the configuration_file.ini.
2. Run the following command :

$ python main.py configuration_file.ini

3. Select an option:

• 1.Extract k-mers | Required parameters: k, training_fasta, k_mers_path, n_iterations, n_solution, n_chromosomes, n_genes, objective_score, mutation_rate and crossover_rate
• 2.Fit a model | Required parameters: model_path, training_fasta and k_mers_path
• 3.Predict a sequences | Required parameters: testing_fasta, model_path, prediction_path and evaluation_mode
• 4.Motif analyzer | Required parameters: training_fasta, k_mers_to_analyze_path and reference_sequence and analysis_report_path
• 5.Exit/Quit

Fasta file format example for n sequences:

>id_sequence_1|target_sequence_1 
CTCAACTCAGTTCCACCAGGCTCTGTTGGATCCGAGGGTAAGGGCTCTGTATTTTCCTGC 
>id_sequence_2|target_sequence_2						
CTCAACTCAGTTCCACCAGGCTCTGTTGGATCCGAGGGTAAGGGCTCTGTATTTTCCTGC
...
...
...
>>id_sequence_n-1|target_sequence_n-1									 
CTCAACTCAGTTCCACCAGGCTCTGTTGGATCCGAGGGTAAGGGCTCTGTATTTTCCTGC 
>id_sequence_n|target_sequence_n													 
CTCAACTCAGTTCCACCAGGCTCTGTTGGATCCGAGGGTAAGGGCTCTGTATTTTCCTGC 

• The character "|" is used to separate the sequence ID from its target.
• The target must be specified in the fasta file for a prediction with evaluation_mode = True.
• For more detailed examples see the data sets in the input folder

Output

• k_mers.fasta: File of the extracted k-mers list
• model.pkl : Prediction model generated by KEVOLVE
• Prediction.csv : Results file of the prediction of unknown genomic sequences
• Signature.xlsx : Analysis report associated with a discriminative k-mers

Reference to cite KEVOLVE

• Lebatteux, Dylan, and Abdoulaye Baniré Diallo. "Combining a genetic algorithm and ensemble method to improve the classification of viruses." 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021.

Reference to cite KANALYZER (Option 4: Motif analyzer)

• Lebatteux, Dylan, et al. "KANALYZER: a method to identify variations of discriminative k-mers in genomic sequences." 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE Computer Society, 2022.