Identification of microRNA-mRNA modules using microarray data.

BACKGROUND: MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression and are involved in numerous cellular processes. Consequently, miRNAs are an important component of gene regulatory networks and an improved understanding of miRNAs will further our knowledge of these networks. There is a many-to-many relationship between miRNAs and mRNAs because a single miRNA targets multiple mRNAs and a single mRNA is targeted by multiple miRNAs. However, most of the current methods for the identification of regulatory miRNAs and their target mRNAs ignore this biological observation and focus on miRNA-mRNA pairs. RESULTS: We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest. CONCLUSIONS: Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.

Analysis of a splice array experiment elucidates roles of chromatin elongation factor Spt4-5 in splicing.

Splicing is an important process for regulation of gene expression in eukaryotes, and it has important functional links to other steps of gene expression. Two examples of these linkages include Ceg1, a component of the mRNA capping enzyme, and the chromatin elongation factors Spt4-5, both of which have recently been shown to play a role in the normal splicing of several genes in the yeast Saccharomyces cerevisiae. Using a genomic approach to characterize the roles of Spt4-5 in splicing, we used splicing-sensitive DNA microarrays to identify specific sets of genes that are mis-spliced in ceg1, spt4, and spt5 mutants. In the context of a complex, nested, experimental design featuring 22 dye-swap array hybridizations, comprising both biological and technical replicates, we applied five appropriate statistical models for assessing differential expression between wild-type and the mutants. To refine selection of differential expression genes, we then used a robust model-synthesizing approach, Differential Expression via Distance Synthesis, to integrate all five models. The resultant list of differentially expressed genes was then further analyzed with regard to select attributes: we found that highly transcribed genes with long introns were most sensitive to spt mutations. QPCR confirmation of differential expression was established for the limited number of genes evaluated. In this paper, we showcase splicing array technology, as well as powerful, yet general, statistical methodology for assessing differential expression, in the context of a real, complex experimental design. Our results suggest that the Spt4-Spt5 complex may help coordinate splicing with transcription under conditions that present kinetic challenges to spliceosome assembly or function.