Regulatory element identification in subsets of transcripts: comparison and integration of current computational methods.

Regulatory elements in mRNA play an often pivotal role in post-transcriptional regulation of gene expression. However, a systematic approach to efficiently identify putative regulatory elements from sets of post-transcriptionally coregulated genes is lacking, hampering studies of coregulation mechanisms. Although there are several analytical methods that can be used to detect conserved mRNA regulatory elements in a set of transcripts, there has been no systematic study of how well any of these methods perform individually or as a group. We therefore compared how well three algorithms, each based on a different principle (enumeration, optimization, or structure/sequence profiles), can identify elements in unaligned untranslated sequence regions. Two algorithms were originally designed to detect transcription factor binding sites, Weeder and BioProspector; and one was designed to detect RNA elements conserved in structure, RNAProfile. Three types of elements were examined: (1) elements conserved in both primary sequence and secondary structure; (2) elements conserved only in primary sequence; and (3) microRNA targets. Our results indicate that all methods can uniquely identify certain known RNA elements, and therefore, integrating the output from all algorithms leads to the most complete identification of elements. We therefore developed an approach to integrate results and guide selection of candidate elements from several algorithms presented as a web service (https://dbw.msi.umn.edu:8443/recit). These findings together with the approach for integration can be used to identify candidate elements from genome-wide post-transcriptional profiling data sets.