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

Abstract

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.

A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers

“…Details can be found in the Supplemental Methods. The Weeder de novo motif detection algoirthm (Pavesi et al 2006) was then used to identify over-represented miRNA-binding sites in the 39 UTR of putatively miRNA coregulated genes (Linhart et al 2008;Fan et al 2009). …”

“…There are two commonly used strategies to identify the miRNA regulator(s) responsible for the observed coexpression of a set of genes: (1) Enrichment of predicted 39 UTR binding sites for a known miRNA (Kertesz et al 2007;Betel et al 2008Betel et al , 2010Friedman et al 2009); or (2) de novo identification of a 39 UTR motif that is complementary to a seed sequence of a miRNA in miRBase (Linhart et al 2008;Fan et al 2009;Goodarzi et al 2009;Kozomara and Griffiths-Jones 2011). Algorithms utilizing the first strategy incorporate some combination of seed complementarity, cross-species conservation, and thermodynamic properties of the binding site.…”

“…We have recently developed a novel algorithm miRvestigator to accurately associate 39 UTR motifs to complementary miRNA seed sequences (Plaisier et al 2011). However, for this algorithm to be effective it has to be coupled to a second algorithm Weeder (Pavesi et al 2006) that can accurately detect de novo cis-regulatory motifs that are conserved within the 39 UTRs of the coexpressed genes (Linhart et al 2008;Fan et al 2009). miRvestigator converts relative conservation of nucleotides at each position of a cis-regulatory motif discovered by Weeder into a profile hidden Markov model (HMM).…”

“…The second approach performs the de novo discovery of conserved putative miRNA-binding sites within the 39 UTRs of coexpressed genes. Weeder is one such algorithm that accurately discovers conserved cis-regulatory elements in 39 UTRs (Linhart et al 2008;Fan et al 2009). The information of conserved cis-regulatory sequences can then be utilized for pattern matching to seed sequences of known miRNAs in miRBase.…”

Known and novel post-transcriptional regulatory sequences are conserved across plant families

“…Again, because of incomplete sequence information or lineage specific loss, we used the MEME option that detects zero or one motif in all sequences (''zoops''), where motifs could be 6-30 nt long. A second-order Markov model based on all A. thaliana 59 UTRs (or 39 UTRs, depending on the region being searched) was used as a background model in all MEME searches (Fan et al 2009). To correct for multiple tests, we divided an E-value cutoff of 0.05 by the number of orthologous subgroups compared: 91,331 for 59 UTR and 73,893 for 39 UTR.…”