Using Trawler_standalone to discover overrepresented motifs in DNA and RNA sequences derived from various experiments including chromatin immunoprecipitation.

Genome-wide location analysis has become a standard technology to unravel gene regulation networks. The accurate characterization of nucleotide signatures in sequences is key to uncovering the regulatory logic but remains a computational challenge. This protocol describes how to best characterize these signatures (motifs) using the new standalone version of Trawler, which was designed and optimized to analyze chromatin immunoprecipitation (ChIP) data sets. In particular, we describe the three main steps of Trawler_standalone (motif discovery, clustering and visualization) and discuss the appropriate parameters to be used in each step depending on the data set and the biological questions addressed. Compared to five other motif discovery programs, Trawler_standalone is in most cases the fastest algorithm to accurately predict the correct motifs especially for large data sets. Its running time ranges within few seconds to several minutes, depending on the size of the data set and the parameters used. This protocol is best suited for bioinformaticians seeking to use Trawler_standalone in a high-throughput manner.