Optimizing Multiple Spaced Seeds for Homology Search

Optimized spaced seeds improve sensitivity and specificity in local homology search. Several authors have shown that multiple seeds can have better sensitivity and specificity than single seeds. We describe a linear programming-based algorithm to optimize a set of seeds. Theoretically, our algorithm offers a performance guarantee: the sensitivity of a chosen seed set is at least 70% of what can be achieved, in most reasonable models of homologous sequences. In practice, our algorithm generates a solution which is at least 90% of the optimal. Our method not only achieves performance better than or comparable to that of a greedy algorithm, but also gives this area a mathematical foundation.

• Type: Conference
• Year: 2004
• Where: CPM
• Authors: Jinbo Xu, Daniel G. Brown, Ming Li, Bin Ma
• Keywords: Algorithm, CPM 2004, Linear Programming-based Algorithm, Seeds Improve Sensitivity