FastSK: fast sequence analysis with gapped string kernels.

Motivation: Gapped k-mer kernels with support vector machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly sized training sets. However, existing gkm-SVM algorithms suffer from slow kernel computation time, as they depend exponentially on the sub-sequence feature length, number of mismatch positions, and the task's alphabet size.

Results: In this work, we introduce a fast and scalable algorithm for calculating gapped k-mer string kernels.