Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors

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Shannon Entropy

Fundamentally, we applid entropy, because we can reach a single value that quantifies the information contained in different observation periods (e.g., our case: k-mer - see our pipeline in this article). To use this model, follow the example below:

To run the tool (Example): $ python3.7 methods/EntropyClass.py -i input -o output -l label -k kmer -e Entropy


Where:

-h = help

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

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

-l = Label - Dataset Label, e.g., lncRNA, mRNA, sncRNA

-k = Range of k-mer, e.g., 1-mer (1) or 2-mer (1, 2)

-e = Type of Entropy, E.g., Shannon

Running:

$ python3.7 methods/EntropyClass.py -i sequences.fasta -o sequences.csv -l mRNA -k 10 -e Shannon

Tsallis Entropy

To run the feature extraction tool (Example): $ python3.7 methods/TsallisEntropy.py -i input -o output -l label -k kmer -q entropic parameter

Where:

-h = help

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

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

-l = Label - Dataset Label, e.g., lncRNA, mRNA, sncRNA

-k = kmer - Range of k-mer, E.g., 1-mer (1) or 2-mer (1, 2) ...

-q = Tsallis - q entropic parameter

Running:

$ python3.7 methods/TsallisEntropy.py -i sequences.fasta -o sequences.csv -l mRNA -k 10 -q 2.3

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

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