Optimal Seed Solver: Optimizing Seed Selection in Read Mapping

TL;DR

The paper introduces OSS, a dynamic programming algorithm that optimizes seed selection in read mapping by minimizing seed frequency, resulting in faster and more sensitive mapping.

Contribution

It presents the first efficient algorithm to jointly optimize seed length and placement, reducing seed frequency significantly compared to existing methods.

Findings

OSS achieves a 3-fold reduction in seed frequency.

OSS operates in polynomial time with respect to read length and seed count.

Compared to state-of-the-art, OSS improves seed selection quality.

Abstract

Motivation: Optimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the mapper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the potential of the mapper to select less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds. Results: We present the Optimal Seed Solver (OSS), a dynamic programming algorithm that discovers the least frequently-occurring set of x seeds in an L-bp read in $O(x \times L)$ operations on average and in $O(x \times L^{2})$ operations in the worst case. We compared OSS against four state-of-the-art seed selection schemes and observed that OSS provides a 3-fold reduction of average seed frequency over the best previous seed selection optimizations.