Abstract
BackgroundWhile progresses have been made in mapping transcriptional regulatory networks, posttranscriptional regulatory roles just begin to be uncovered, which has arrested much attention due to the discovery of miRNAs. Here we demonstrated a combinatorial approach to incorporate transcriptional and posttranscriptional regulatory sequences with gene expression profiles to determine their probabilistic dependencies.ResultsWe applied the proposed method to microarray time course gene expression profiles and could correctly predict expression patterns for more than 50% of 1,132 genes, based on the sequence motifs adopted in the network models, which was statistically significant. Our study suggested that the contribution of miRNA regulation towards gene expression in plants may be more restricted than that of transcription factors; however, miRNAs might confer additional layers of robustness on gene regulation networks. The programs written in C++ and PERL implementing methods in this work are available for download from our supplemental data web page.ConclusionIn this study we demonstrated a combinatorial approach to incorporate miRNA target motifs (miRNA-mediated posttranscriptional regulatory sites) and TFBSs (transcription factor binding sites) with gene expression profiles to reconstruct the regulatory networks. The proposed approach may facilitate the incorporation of diverse sources with limited prior knowledge.
Highlights
While progresses have been made in mapping transcriptional regulatory networks, posttranscriptional regulatory roles just begin to be uncovered, which has arrested much attention due to the discovery of miRNAs
We believe that sequences with the same number of mismatches to a miRNA might not have the same probability to be cleaved by the miRNA owing to the mechanism of RNA-induced silencing complex (RISC)
In spite of the variable miRNA sequences, the complementarities between miRNA-target duplex might follow some rules according to the RISC mechanisms, and we believed that the Hidden Markov model (HMM) could be used to find these hidden rules by learning from a training set of potential miRNA targets of only 19 mature miRNAs contained in miRBase 3.0, a three years old release, and in this way we assessed the ability of our method to extrapolate from a limited prior knowledge [23]
Summary
While progresses have been made in mapping transcriptional regulatory networks, posttranscriptional regulatory roles just begin to be uncovered, which has arrested much attention due to the discovery of miRNAs. Here we demonstrated a combinatorial approach to incorporate transcriptional and posttranscriptional regulatory sequences with gene expression profiles to determine their probabilistic dependencies. Numerous experimental and computational studies [2] have been done on locating transcriptional regulator DNA binding sequences and understanding their working mechanisms. These binding motifs can be used as building blocks of gene regulatory networks and several approaches were developed to identify how a set of cis-regulatory elements in a gene's promoter region governed its behavior and explained the observed expression profiles [3-5]. Posttranscriptional regulation through RNA-RNA interaction has arrested much attention due to the discovery of microRNAs (miRNAs)
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