SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps
Fig 1
SeqGL identifies binding profiles in genome-wide regulatory maps.
SeqGL uses sparse group lasso to identify the most important k-mer groups that discriminate between ChIP-seq/DNase-seq peaks and flanks. Hierarchical clustering of k-mer counts across peak and flank sequences reveals a block structure that defines k-mer groups. A representative heatmap of k-mer frequencies for a subset of peaks and flanks is shown. Sparse group lasso regression sets some groups uniformly to zero; groups with non-zero weights define group signals that may represent binding sequence signals for individual TFs. Significant hits for each group signal are identified, and sequence windows around these hits are extracted. HOMER is then applied to the windows to associate these group signals with motifs for visualization and identification.