Alignment-free sequence comparison with spaced k-mers

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

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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, and to provide a lower misclassification rate when used with Support Vector Machines (SVMs) (Onodera 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, 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.

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“…We have presented how the coverage criterion (Benson andMak, 2008, Martin, 2013) can help in measuring the seed efficiency in two recent problems : a classifier based on spaced k-mers (Onodera and Shibuya, 2013), and a k-mer alignment-free distance estimation (Boden et al, 2013. We have also shown how to extend the second one to be even more sensitive.…”

“…In this section, we consider two biological sequence oriented applications that have recently been proposed to use spaced seeds : SVM classifiers based on spaced string kernels (Onodera and Shibuya, 2013), and alignment-free distance estimators using spaced k-mers (Boden et al, 2013. We show that the coverage sensitivity can be used in both cases to improve the estimators, and thus also be applied to the selection of the best seed patterns on such domains.…”

“…Interestingly, it's only in the last year that the use of spaced seeds has been proposed (Boden et al, 2013, with recent applications for specific Next Generation Sequencing tasks, such as multi-clonal clusterization (Giraud et al, 2014). Here again, the lack of seed criteria used in the literature didn't help the selection of good seeds for these tasks.…”