An Approach to Selecting Putative RNA Motifs Using MDL Principle

The history of molecular biology is punctuated by a series of discoveries demonstrating the surprising breadth of biological roles of ribonucleic acid (RNA). An ensemble of evolutionary related RNA sequences believed to contain signals at sequence and structure level can be exploited to detect motifs common to all or a portion of those sequences. Finding these similar structural features can provide substantial information as to which parts of the sequence are functional. For several decades, free energy minimization has been the most popular method for structure prediction. However, limitations of the free energy models as well as time complexity have prompted us to look for alternative approaches. We therefore, investigate another paradigm, minimum description length (MDL) encoding, for evaluating the significance of consensus motifs. Here, we evaluate motifs generated by Seed using the description length as a selection criteria. MDL scoring method was tested on four data sets of varying complexity. We found that the scoring method produces competing structures in comparison to the ones predicted with lowest free energy. The top rank motifs have high measures of positive predicted value to known motifs.