Entropy predicts sensitivity of pseudo-random seeds.

Seed design is important for sequence similarity search applications such as read mapping and average nucleotide identity (ANI) estimation. While k -mers and spaced k -mers are likely the most well-known and used seeds, sensitivity suffers at high error rates, particularly when indels are present. Recently, we developed a pseudo-random seeding construct, strobemers, which were empirically demonstrated to have high sensitivity also at high indel rates. However, the study lacked a deeper understanding of why. In this study, we propose a model to estimate the entropy of a seed and find that seeds with high entropy, according to our model, in most cases have high match sensitivity. Our discovered seed randomness-sensitivity relationship explains why some seeds perform better than others, and the relationship provides a framework for designing even more sensitive seeds. We also present three new strobemer seed constructs, mixedstrobes, altstrobes, and multistrobes. We use both simulated and biological data to demonstrate that our new seed constructs improve sequence-matching sensitivity to other strobemers. We show that the three new seed constructs are useful for read mapping and ANI estimation. For read mapping, we implement strobemers into minimap2 and observe 30% faster alignment time and 0.2% higher accuracy than using k -mers when mapping reads at high error rates. As for ANI-estimation, we find that higher entropy seeds have a higher rank correlation between estimated and true ANI.