Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors

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Preprocessing

Before executing any method in this package, it is necessary to run a pre-processing script, to eliminate any noise from the sequences (e.g., other letters as: N, K …,). To use this script, follow the example below:

Important: This package only accepts sequence files in Fasta format as input to the methods.

To run the tool (Example): $ python3.7 preprocessing/preprocessing.py -i input -o output


Where:

-h = help

-i = Input - Fasta format file, e.g., test.fasta

-o = output - Fasta format file, e.g., output.fasta

Running:

$ python3.7 preprocessing/preprocessing.py -i dataset.fasta -o preprocessing.fasta 

Numerical Mapping

This method generates a numerical mapping of all sequence. Essentially, we provide 7 mappings. The theory can be consulted in this article. Nevertheless, this method will generate a vector with the size of the largest sequence. We developed a code that applies everything automatically. Therefore, it is necessary to pass all the classes/labels that will form the dataset. Thereby. to use this model, follow the example below:

To run the code (Example): $ python3.7 methods/MappingClass.py -n number of datasets/labels -o output -r representation


Where:

-h = help

-n = number of datasets/labels

-o = output - CSV format file, e.g., test.csv

-r = representation/mappings, e.g., 1 = Binary, 2 = Z-curve, 3 = Real, 4 = Integer, 5 = EIIP, 6 = Complex Number, 7 = Atomic Number.

Running:

$ python3.7 methods/MappingClass.py -n 2 -o dataset.csv -r 2

Note Input sequences for feature extraction must be in fasta format.

Note This example will generate a csv file with the extracted features.