Filtration and Depth Annotation Improve Non-linear Projection for RNA Motif Discovery

This study presents a strategy for reducing the effects of noise on the location of RNA motifs in the context of a previously developed analysis pipeline. The pipeline was developed to search for novel RNA motifs incorporating both primary and secondary structure. The ability of the pipeline to detect motifs in the presence of a relatively large amount of sequence not containing a target motif is examined in three different experiments. The first demonstrates the impact of increasing the number of sequences without a particular motif in a synthetic data set. The second experiment looks at how well a known motif, the iron response element, clusters in biological data sets with various amounts of non-IRE motif containing sequence. The final experiment applies and analyzes the effects of a number-near-neighbors filter to winnow data and highlight the presence of the clusters representing motifs. The filter is found to help substantially