Multi-Perspective Natural Vector: A Novel Method for Viral Sequence Feature Extraction

Abstract

The rapid expansion of biological data in recent decades has highlighted the need for efficient methods in sequence analysis. Traditional pairwise alignment approaches are both time-consuming and memory-intensive. Alignment-free (AF) methods such as natural vector (NV) and k-mer operate on a one-dimensional framework, interpreting DNA primarily as a linear string of nucleotides. To achieve a more comprehensive interpretation of molecular structure, this study incorporates the three-dimensional architectural features of DNA and introduces a novel AF method named Multi-perspective natural vector (MNV). The MNV method maps genome sequences of varying lengths to points within a unified geometric space, facilitating large-size data processing tasks such as variant classification and clustering. Across datasets of different sizes and types, MNV attains a 100% convex hull separation ratio in lower dimensions compared with widely used methods NV and k-mer methods. In neural network classification, MNV achieves better classification accuracy of 99.55% and 98.78% on SARS-CoV-2 and poliovirus datasets respectively, demonstrating its effectiveness in viral genome analysis while maintaining computational efficiency.