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 

Accumulated Nucleotide Frequency

To use this model, follow the example below:

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


Where:

-h = help

-n = number of datasets/labels

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

-r = approach, e.g., 1 = Accumulated Nucleotide Frequency, 2 = Accumulated Nucleotide Frequency with Fourier.

Running:

$ python3.7 methods/AccumulatedNucleotideFrequency.py -n 2 -o dataset.csv -r 1

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

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