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

TL;DR

This paper introduces a general automaton-based framework for computing seed sensitivity, enabling the design of more effective subset seeds for similarity search, outperforming traditional spaced seeds.

Contribution

It presents a unified automaton approach for seed sensitivity and introduces a novel subset seed concept with efficient automaton construction.

Findings

Sensitive subset seeds can be efficiently designed using the proposed approach.

Subset seeds outperform ordinary spaced seeds in similarity search.

The framework is adaptable to various seed definitions.

Abstract

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions 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 specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds.