Dataset Information

CWords - systematic microRNA regulatory motif discovery from mRNA expression data.

ABSTRACT: 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. 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.cWords 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.cWords 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/.

SUBMITTER: Rasmussen SH

PROVIDER: S-EPMC3682869 | biostudies-other | 2013 May

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

cWords - systematic microRNA regulatory motif discovery from mRNA expression data.

Rasmussen Simon H SH Jacobsen Anders A Krogh Anders A

Silence 20130520 1

<h4>Background</h4>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. Post-transcriptional regulation by small RNAs is mediated through partial complementary binding to messenger RNAs leaving nucleotide signatures or motifs throughout the entire transcriptome. Comput ...[more]

PMID: 23688306

Similar Datasets

Project description:BackgroundTraditional methods for computational motif discovery often suffer from poor performance. In particular, methods that search for sequence matches to known binding motifs tend to predict many non-functional binding sites because they fail to take into consideration the biological state of the cell. In recent years, genome-wide studies have generated a lot of data that has the potential to improve our ability to identify functional motifs and binding sites, such as information about chromatin accessibility and epigenetic states in different cell types. However, it is not always trivial to make use of this data in combination with existing motif discovery tools, especially for researchers who are not skilled in bioinformatics programming.ResultsHere we present MotifLab, a general workbench for analysing regulatory sequence regions and discovering transcription factor binding sites and cis-regulatory modules. MotifLab supports comprehensive motif discovery and analysis by allowing users to integrate several popular motif discovery tools as well as different kinds of additional information, including phylogenetic conservation, epigenetic marks, DNase hypersensitive sites, ChIP-Seq data, positional binding preferences of transcription factors, transcription factor interactions and gene expression. MotifLab offers several data-processing operations that can be used to create, manipulate and analyse data objects, and complete analysis workflows can be constructed and automatically executed within MotifLab, including graphical presentation of the results.ConclusionsWe have developed MotifLab as a flexible workbench for motif analysis in a genomic context. The flexibility and effectiveness of this workbench has been demonstrated on selected test cases, in particular two previously published benchmark data sets for single motifs and modules, and a realistic example of genes responding to treatment with forskolin. MotifLab is freely available at http://www.motiflab.org.

Project description:BackgroundGenes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.ResultsWe propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.ConclusionWe have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.

Project description:Abstract Background: The pathogenesis of human immunodeficiency virus 1 (HIV-1) infection is so complex that have not been clearly defined, despite intensive efforts have been made by many researchers. MicroRNA (miRNA) as regulation factor in various human diseases may influence the course of HIV-1 infection by targeting mRNAs. Thus, studies combining transcription of posttranscriptional miRNA regulation are required. Methods: With the purpose of identifying cascaded miRNA-mRNA regulatory relationships related to HIV infection in gene level, the parallel miRNA, and mRNA expression profiles were analyzed to select differential expressed miRNAs and mRNAs. Then, miRNA-mRNA interactions were predicted using 3 data sources and Pearson correlation coefficient was calculated based on the gene expression level for accuracy improvement. Furthermore, the calculation of the regulatory impact factors was conducted to reveal crucial regulators in HIV-1 infection. To give further insight into these transcription factor (TF) regulators, the differentially co-expression analysis was conducted to identify differentially co-expressed links and differential co-expressed genes and the co-expression gene modules were identified using a threshold-based hierarchical clustering method, then modules were combined into a miRNA-TF-mRNA network. Results: A total of 69,126 differentially co-expressed links and 626 differential co-expressed genes were identified. Functional enrichment analysis indicated that these co-expressed genes were significantly involved in immune response and apoptosis. Moreover, according to regulatory impact factors, 5 most influential TFs and miRNA in HIV-1 infection were identified and miRNA-TF-mRNA regulatory networks were built during the computing process. Conclusions: In our study, a set of integrated methods was generated to identify important regulators and miRNA-TF-mRNA interactions. Parallel profiling analysis of the miRNAs and mRNAs expression of HIV/acquired immunodeficiency syndrome (AIDS) patients after antiretroviral therapy indicated that some regulators have wide impact on gene regulation and that these regulatory elements may bear significant implications on the underlying molecular mechanism and pathogenesis of AIDS occurrence.

Dataset Information

CWords - systematic microRNA regulatory motif discovery from mRNA expression data.

Publications

cWords - systematic microRNA regulatory motif discovery from mRNA expression data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets