A Coverage Criterion for Spaced Seeds and its Applications to Support Vector Machine String Kernels and k-Mer Distances

TL;DR

This paper introduces a coverage criterion for spaced seeds, demonstrating its effectiveness in improving alignment detection, phylogenetic distance measurement, and SVM classification accuracy through automaton-based measurement and comparison with other criteria.

Contribution

It proposes a novel coverage criterion for spaced seeds, with methods for direct measurement and application to alignment-free distances, enhancing seed pattern design.

Findings

Coverage criterion correlates well with classification accuracy

Automaton-based approach enables efficient measurement of seed efficiency

Coverage extension improves alignment-free distance estimation

Abstract

Spaced seeds have been recently shown to not only detect more alignments, but also to give a more accurate measure of phylogenetic distances (Boden et al., 2013, Horwege et al., 2014, Leimeister et al., 2014), and to provide a lower misclassification rate when used with Support Vector Machines (SVMs) (On-odera and Shibuya, 2013), We confirm by independent experiments these two results, and propose in this article to use a coverage criterion (Benson and Mak, 2008, Martin, 2013, Martin and No{\'e}, 2014), to measure the seed efficiency in both cases in order to design better seed patterns. We show first how this coverage criterion can be directly measured by a full automaton-based approach. We then illustrate how this criterion performs when compared with two other criteria frequently used, namely the single-hit and multiple-hit criteria, through correlation coefficients with the correct classification/the true distance. At the end, for alignment-free distances, we propose an extension by adopting the coverage criterion, show how it performs, and indicate how it can be efficiently computed.