This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
To compare multiple genome sequences, we transform each primary genome sequence into corresponding k-mer-based vectors. According to the principle of independent component analysis (ICA), the operation can be regarded as mixing multiple source genomic signals via several sensors, through which we can obtain the mixed vectors with equal-length from the corresponding genome sequences with different length. However, this mixing operation is performed by counting all the k-mer-based frequencies, instead of using real hardware of sensors. Thus, we name this preprocessing operation as virtual mixer (VM).Using ICA-based transformation, we projected all the vectors upon their independent components to capture the coefficients-based feature vector through the projection extractor (PE), which has been proved to have a property of distance preserving. Then, we used the proposed VMPE model upon three representative real datasets of genome sequence to test the efficiency for the model. The contrastive analysis results indicate that the proposed VMPE model performs well in similarity analysis.