Abstract
Many DNA-binding proteins interact with partner proteins. Recently, based on the high-throughput consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX) method, many such protein pairs have been found to bind DNA with flexible spacing between their individual binding motifs. Most existing motif representations were not designed to capture such flexibly spaced regions. In order to computationally discover more co-binding events without prior knowledge about the identities of the co-binding proteins, a new representation is needed. We propose a new class of sequence patterns that flexibly model such variable regions and corresponding algorithms that identify co-bound sequences using these patterns. Based on both simulated and CAP-SELEX data, features derived from our sequence patterns lead to better classification performance than patterns that do not explicitly model the variable regions. We also show that even for standard ChIP-seq data, this new class of sequence patterns can help discover co-bound events in a subset of sequences in an unsupervised manner. The open-source software is available at https://github.com/kevingroup/glk-SVM.
Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control