SArKS: de novo discovery of gene expression regulatory motifs and domains by suffix array kernel smoothing

MotivationWe set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score--fold-change, test-statistic, p-value--comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies nonparametric kernel smoothing to uncover promoter motifs that correlate with elevated differential expression scores. SArKS detects motifs by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.\n\nResultsWe applied SArKS to published gene expression data representing distinct neocortical neuron classes in M. musculus and interneuron developmental states in H. sapiens. When benchmarked against several existing algorithms for correlative motif discovery using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power.\n\nAvailabilityhttps://github.com/denniscwylie/sarks.\n\nContactdenniswylie@austin.utexas.edu.\n\nSupplementary informationappended to document.