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

Efficient seeding techniques for protein similarity search