Dennis C Wylie1, Hans A Hofmann1,2,3,4, Boris V Zemelman2,4,5,6. 1. Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX, USA. 2. Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA. 3. Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA. 4. Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA. 5. Department of Neuroscience, University of Texas at Austin, Austin, TX, USA. 6. Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA.
Abstract
MOTIVATION: We 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 non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. RESULTS: We applied SArKS to published gene expression data representing distinct neocortical neuron classes in Mus musculus and interneuron developmental states in Homo sapiens. When benchmarked against several existing algorithms using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power. AVAILABILITY AND IMPLEMENTATION: https://github.com/denniscwylie/sarks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: We 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 non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. RESULTS: We applied SArKS to published gene expression data representing distinct neocortical neuron classes in Mus musculus and interneuron developmental states in Homo sapiens. When benchmarked against several existing algorithms using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power. AVAILABILITY AND IMPLEMENTATION: https://github.com/denniscwylie/sarks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Preeti Mehta; Lauren Kreeger; Dennis C Wylie; Jagruti J Pattadkal; Tara Lusignan; Matthew J Davis; Gergely F Turi; Wen-Ke Li; Matthew P Whitmire; Yuzhi Chen; Bridget L Kajs; Eyal Seidemann; Nicholas J Priebe; Attila Losonczy; Boris V Zemelman Journal: Cell Rep Date: 2019-03-05 Impact factor: 9.423