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We propose a general approach to compute the seed sensitivity, that can beapplied to different definitions of seeds. It treats separately threecomponents of the seed sensitivity problem - a set of target alignments, anassociated probability distribution, and a seed model - that are specified bydistinct finite automata. The approach is then applied to a new concept ofsubset seeds for which we propose an efficient automaton construction.Experimental results confirm that sensitive subset seeds can be efficientlydesigned using our approach, and can then be used in similarity searchproducing better results than ordinary spaced seeds

A Unifying Framework for Seed Sensitivity and Its Application to Subset Seeds