Abstract
BackgroundPost-transcriptional regulation of gene expression by small RNAs and RNA binding proteins is of fundamental importance in development of complex organisms, and dysregulation of regulatory RNAs can influence onset, progression and potentially be target for treatment of many diseases. Post-transcriptional regulation by small RNAs is mediated through partial complementary binding to messenger RNAs leaving nucleotide signatures or motifs throughout the entire transcriptome. Computational methods for discovery and analysis of sequence motifs in high-throughput mRNA expression profiling experiments are becoming increasingly important tools for the identification of post-transcriptional regulatory motifs and the inference of the regulators and their targets.ResultscWords is a method designed for regulatory motif discovery in differential case–control mRNA expression datasets. We have improved the algorithms and statistical methods of cWords, resulting in at least a factor 100 speed gain over the previous implementation. On a benchmark dataset of 19 microRNA (miRNA) perturbation experiments cWords showed equal or better performance than two comparable methods, miReduce and Sylamer. We have developed rigorous motif clustering and visualization that accompany the cWords analysis for more intuitive and effective data interpretation. To demonstrate the versatility of cWords we show that it can also be used for identification of potential siRNA off-target binding. Moreover, cWords analysis of an experiment profiling mRNAs bound by Argonaute ribonucleoprotein particles discovered endogenous miRNA binding motifs.ConclusionscWords is an unbiased, flexible and easy-to-use tool designed for regulatory motif discovery in differential case–control mRNA expression datasets. cWords is based on rigorous statistical methods that demonstrate comparable or better performance than other existing methods. Rich visualization of results promotes intuitive and efficient interpretation of data. cWords is available as a stand-alone Open Source program at Github https://github.com/simras/cWords and as a web-service at: http://servers.binf.ku.dk/cwords/.
Highlights
Post-transcriptional regulation of gene expression by small Ribonucleic acid (RNA) and RNA binding proteins is of fundamental importance in development of complex organisms, and dysregulation of regulatory RNAs can influence onset, progression and potentially be target for treatment of many diseases
MiRNA target prediction methods may not detect non-canonical target motifs specific to the perturbed miRNA, and systematic analysis of miRNA perturbation experiments has shown that in addition to miRNA seed sites, other 3′Untranslated region (UTR) motifs, some corresponding to known binding sites of RNA binding proteins (RNA-BPs), can be predictive of the observed messenger RNA (mRNA) expression changes [7]
The rigorous statistical framework allows for simultaneous analysis of multiple word lengths, and words are clustered into motifs presented in plots providing both overview and in-depth information for interpretation
Summary
Post-transcriptional regulation of gene expression by small RNAs and RNA binding proteins is of fundamental importance in development of complex organisms, and dysregulation of regulatory RNAs can influence onset, progression and potentially be target for treatment of many diseases. Computational methods for discovery and analysis of sequence motifs in high-throughput mRNA expression profiling experiments are becoming increasingly important tools for the identification of post-transcriptional regulatory motifs and the inference of the regulators and their targets. MiRNA target prediction methods may not detect non-canonical target motifs specific to the perturbed miRNA, and systematic analysis of miRNA perturbation experiments has shown that in addition to miRNA seed sites, other 3′UTR motifs, some corresponding to known binding sites of RNA binding proteins (RNA-BPs), can be predictive of the observed mRNA expression changes [7]. There is a need for computational methods that allow for unbiased and systematic analysis of mRNA sequence motifs in miRNA perturbation experiments to confirm effective experimental perturbation and to explore regulatory sequence elements other than established miRNA binding sites
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