De-novo motif search is a frequently applied bioinformatics procedure to identify and prioritize recurrent elements in sequences sets for biological investigation, such as the ones derived from high-throughput differential expression experiments. Several algorithms have been developed to perform motif search, employing widely different approaches and often giving divergent results. In order to maximize the power of these investigations and ultimately be able to draft solid biological hypotheses, there is the need for applying multiple tools on the same sequences and merge the obtained results. However, motif reporting formats and statistical evaluation methods currently make such an integration task difficult to perform and mostly restricted to specific scenarios.
Researchers from the University of Trento have developed the Dynamic Motif Integration Toolkit (DynaMIT), a flexible platform designed to implement this vision: it provides the means to execute multiple motif search tools, integrate their output and display the obtained results in many different ways. It is customizable and extendible in all its aspects, allowing for a truly personalized and fine-tuned usage experience.
DynaMIT workflow. The figure describes the three steps composing the toolkit workflow. First of all, motif search is performed by means of all tools specified in the configuration, and all results collected together; then, obtained motifs are thus integrated according to the user-selected integration strategy. Eventually, the various specified results visualization modes are executed to provide tabular and graphical displays of the obtained data.
Availability – DynaMIT is implemented as an open-source Python package, and can be used stand-alone or easily embedded in complex bioinformatic pipelines; it is freely available at http://cibioltg.bitbucket.org