Removal of AU Bias from Microarray mRNA Expression Data Enhances Computational Identification of Active MicroRNAs

Ran Elkon and Reuven Agami

PLOS Computational Biology, 2008, vol. 4, issue 10, 1-10

Abstract: Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3′-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3′-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3′-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3′-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions.Author Summary: MicroRNAs are a novel class of genes that encodes for short RNA molecules recognized to play key roles in the regulation of many biological networks. MicroRNAs, predicted to collectively target more than 30% of all human protein-coding genes, suppress gene expression by binding to regulatory elements usually embedded in the 3′-UTRs of their target mRNAs. Despite intensive efforts in recent years, biological functions carried out by microRNAs have been characterized for only a small number of these genes, making elucidation of their roles one of the greatest challenges of biology today. Bioinformatics analyses can significantly help meet this challenge. In particular, the integrated analysis of microarray mRNA expression data and 3′-UTR sequences holds great promise for systematic dissection of regulatory networks controlled by microRNAs. Applying such integrated analysis to numerous microarray datasets, we disclosed a major technical bias that hampers the identification of active microRNAs from mRNA expression profiles. We developed visualization and normalization schemes for detection and removal of the bias and demonstrate that their application to microarray data significantly enhances the identification of active microRNAs. Given the broad use of microarrays and the ever-growing interest in microRNAs, we anticipate that the methods we introduced will be widely adopted.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1000189

DOI: 10.1371/journal.pcbi.1000189

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