Next-Generation MicroRNA Expression Profiling Technology Methods and
The rapid pace of microRNA (miRNA) research continues to drive the advances of techniques for miRNA expression profiling, and innovative technologies that are more sensitive, specific, quantitative, and that are compatible with a wide range of biospecimen
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1. Introduction MicroRNAs (miRNAs) are endogenous RNAs with a length of around 22 nt. They are post-transcriptional regulators, which can bind to partially complementary sequences on target mRNAs, resulting mostly in translational repression and thereby silencing of the target protein. miRNAs are predicted to regulate the expression of the majority of all mammalian genes (1). Classically, diseases and genetic disorders are linked to a single genetic polymorphism, resulting, for instance, in dysregulation of a single mRNA. A systematic analysis of genetic disorders has since revealed that many more such diseases are of complex, multifactorial origin, meaning that they are likely associated with the effects of multiple genes and more so related with lifestyle and environmental factors (2). Here, we ask the question if similar multifactorial origins are also reflected on correlation patterns of associated miRNA level.
Jian-Bing Fan (ed.), Next-Generation MicroRNA Expression Profiling Technology: Methods and Protocols, Methods in Molecular Biology, vol. 822, DOI 10.1007/978-1-61779-427-8_17, © Springer Science+Business Media, LLC 2012
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A. Ruepp et al.
We provide a systematic resource denoted as PhenomiR, consisting of literature studies linking dysregulated miRNA profiles with diseases. With a total of 12,189 miRNA expression phenotype relation data points, collected from 628 different experiments, PhenomiR is the most comprehensive resource of its kind. Studies compiled in PhenomiR have, for example, found that miRNA clusters like miR17-92, miR-106b-93-25, and miR-222-221 have a severe impact on cellular processes and diseases, especially cancer (3, 4). Here, miRNA clusters are defined as miRNAs on close-by genomic locations, which have been shown to be often coexpressed (5). Given a maximum distance of 5 kb, about 34% of human miRNAs appear as miRNA clusters of at least two members. We have previously investigated the influence of differentially regulated miRNA clusters on diseases (6). Using the comprehensive dataset from our PhenomiR database, we asked whether the impact of miRNA clusters on diseases is only restricted to a few examples or if miRNA clusters systematically affect the pathobiology of diseases. We were able to show that dysregulated miRNA clusters are significantly overrepresented compared to singular miRNA gene products (not contained in an miRNA cluster). These clusters are tightly associated with many diseases. In the following, we summarize the content of the PhenomiR database and outline its use both via Web interface and through local installation. Moreover, we briefly review analyses of related miRNA profiles using the R language in Subheading 3. Since for more detailed analyses it may be useful to understand the core PhenomiR data structure and the concept of an entry before examining the following methods, we briefly describe these principles in Note 1.
2. Materials Methods presented in this chapter deal with both Web-based functionality and installing of the data for manipulati
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