Similarity-weighted entropy for quantifying genetic diversity in viral quasispecies

Viral quasispecies are dynamic populations of genetically diverse viruses, often exhibiting high mutation rates. Understanding the genetic diversity within these quasispecies is critical for analyzing viral evolution, adaptation, and treatment resistance. Entropy and normalized Shannon entropy are widely used metrics to quantify this diversity. However, these metrics ignore genetic similarities between sequences, potentially underestimating the true diversity. In this paper, we introduce two methods for similarity-weighted normalized entropy that account for sequence similarities and provide more accurate measures of genetic diversity. By applying these methods to two hypothetical viral quasispecies populations, we compare the traditional entropy, normalized entropy, and the proposed similarity-weighted measures. Our results demonstrate that the similarity-weighted entropies better capture the true genetic diversity in highly related viral populations, while retaining the simplicity of the original entropy calculations. We discuss the advantages and limitations of both similarity-weighted measures and propose their application in viral quasispecies studies.