Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors

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Other techniques

MathFeature also provides other techniques known in the literature: k-mer (for protein), Amino acid composition (AAC), Dipeptide composition (DPC), Tripeptide composition (TPC).

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

Customizable k-mer, AAC, DPC, TPC

To use this model, follow the example below:

To run the code (Example): $ python3.7 methods/ExtractionTechniques-Protein.py -i input -o output -l label -t technique


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, DNA, 0, 1

-t = type of Feature Extraction - e.g., AAC or DPC or TPC or kmer or kstep

Running:

$ python3.7 methods/ExtractionTechniques-Protein.py -i protein.fasta -o dataset.csv -l DNA -t AAC

Note: Kstep = Customizable k-mer - You can configure sliding window, step and k-mer.

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

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