GAVGA

GAVGA: A Genetic Algorithm for Viral Genome Assembly

GAVGA is developed in Python and can be used in the assembly of viral genomes.

Please cite:

Oliveira, R. R., Damasceno, F., Souza, R., Santos, R., Lima, M., Kawasaki, R., & Sales, C. (2017, September). GAVGA: A Genetic Algorithm for Viral Genome Assembly. In EPIA Conference on Artificial Intelligence (pp. 395-407). Springer, Cham.

https://link.springer.com/chapter/10.1007/978-3-319-65340-2_33

Usage

python gavga.py reads.fastq config.init

Parameters:

reads.fastq: fastq file with single reads to be assembled.
config.init: file containing all the parameters necessary to running GAVGA.

Before running GAVGA, the user can change some default parameters in the config file:

• pop_size = Population size. Number of chromosomes that must be created;
• mut_rate = Mutation rate
• cross_rate = Crossover rate
• min_overlap = Minimum overlap length
• min_simil = Percentage of minimum similarity
• num_generation = Number of generations
• tourn_ring = Tournament ring. Total of chromosomes that must compete in the tournament.
• num_run = Number of executions that GAVGA will run. Recommended minimum of 2.
• phred = Phred offset for quality code (33 or 64)
• output = dir where to save the output files

All parameters are mandatory.

Project Members:

• Renato Oliveira (admin)