A unifying framework for seed sensitivity and its application to subset seeds (Extended abstract)

We propose a general approach to compute the seed sensitivity, that can be applied to di erent de nitions of seeds. It treats separately three components of the seed sensitivity problem { a set of target alignments, an associated probability distribution, and a seed model { that are speci ed by distinct nite automata. The approach is then applied to a new concept of subset seeds for which we propose an e cient automaton construction. Experimental results con rm that sensitive subset seeds can be e ciently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds.