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Entropy predicts sensitivity of pseudorandom seeds.
Benjamin Dominik Maier1, Kristoffer Sahlin2
1Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden.
Genome Research
|May 22, 2023
Summary
New strobemer seeds improve sequence similarity searches, even with high error rates and indels. A new model links seed entropy to sensitivity, aiding the design of more effective seeds for read mapping and ANI estimation.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Seed design is crucial for sequence similarity searches like read mapping and average nucleotide identity (ANI) estimation.
- Traditional seeds (k-mers, spaced k-mers) show reduced sensitivity at high error rates, especially with indels.
- Strobemers, a pseudorandom seeding construct, demonstrated high sensitivity at high indel rates, but the underlying reasons were not fully understood.
Purpose of the Study:
- To develop a model for estimating seed entropy and understand its relationship with match sensitivity.
- To design novel strobemer seed constructs with improved performance.
- To evaluate the effectiveness of new strobemer constructs in read mapping and ANI estimation.
Main Methods:
- Proposed a model to estimate the entropy of a seed.
- Introduced three new strobemer seed constructs: mixedstrobes, altstrobes, and multistrobes.
- Utilized simulated and biological data for performance evaluation.
Main Results:
- Established a positive correlation between seed entropy and match sensitivity.
- Demonstrated that new strobemer constructs (mixedstrobes, altstrobes, multistrobes) outperform existing strobemers in sequence matching sensitivity.
- Showcased improved performance in read mapping (30% faster, 0.2% higher accuracy with minimap2) and ANI estimation (higher rank correlation with true ANI for high entropy seeds).
Conclusions:
- Seed entropy is a key factor in determining match sensitivity, providing a framework for designing superior seeds.
- The novel strobemer constructs offer enhanced sensitivity for sequence similarity searches.
- Strobemers represent a significant advancement for applications like read mapping and ANI estimation, particularly in the presence of high error rates and indels.
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