Efficient filtration for similarity search with spaced k-mer neighbors

In DNA and protein sequence similarity search, seeding (or filtration) has been widely used to trade search sensitivity with search speed. In this paper, a new seeding method, called spaced k-mer neighbors, is introduced to provide a more efficient tradeoff between the speed and sensitivity in protein similarity search. The new method pre-selects a set of spaced k-mers as neighbors, and uses the neighbors to detect hits between the query and database sequences. An efficient heuristic algorithm is proposed for the neighbor selection. We demonstrate that the method can improve the tradeoff efficiency over existing seeding methods.