Efficient seeding techniques for protein similarity search

TL;DR

This paper explores the design of efficient subset seed alphabets for protein similarity search, achieving comparable or better sensitivity/selectivity trade-offs than standard methods like Blastp.

Contribution

It introduces new seed design methods using subset seed formalism, demonstrating improved performance over traditional seeding techniques in protein sequence analysis.

Findings

Seeds outperform Blastp on Bernoulli models with BLOSUM62 matrix.

Subset seed formalism is less expressive but more efficient.

Proposed seeds achieve optimal sensitivity/selectivity trade-offs.

Abstract

We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets.We then perform an analysis of seeds built over those alphabet and compare them with the standard Blastp seeding method [2,3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seed is less expressive (but less costly to implement) than the accumulative principle used in Blastp and vector seeds, our seeds show a similar or even better performance than Blastp on Bernoulli models of proteins compatible with the common BLOSUM62 matrix.