Publication date: Dec 02, 2025
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.
| Concepts | Keywords |
|---|---|
| Decades | genome |
| Efficient | natural vector |
| Genome | sequence classification |
| Poliovirus | virus |
| Viral |
Semantics
| Type | Source | Name |
|---|---|---|
| drug | DRUGBANK | Tropicamide |