Biochemical and Biophysical Research Communications
Refining microRNA target predictions: Sorting the wheat from the chaff
Introduction
microRNAs (miRNAs) are short, ∼22 nucleotide long RNAs that reduce gene expression, usually by binding to the 3′ untranslated region of target mRNAs. miRNAs guide a protein complex called RNA-induced silencing complex (RISC) to specific mRNA target sites called microRNA responsive elements (mREs). miRNAs were first discovered in 1993 during an analysis of larval developmental timing in the worm Caenorhabditis Elegans, where a 22-nucleotide RNA regulated protein abundance of LIN-14 [1]. First regarded as a Nematode-specific RNA family, it was only in 2000 that another microRNA, let-7, was characterized and identified in other species. In 2002, Eric Lai compared the sequences of 11 microRNAs to the K box and Brd Box motifs that were known to mediate post-transcriptional regulation in Drosophila. He demonstrated that the first eight nucleotides, now called the seed region, of miRNAs, were perfectly complementary to these motifs and concluded that this complementarity may be essential in post-transcriptional regulation by microRNAs [2]. This simple bioinformatics analysis established one of the strongest predictive features used in target prediction to date and was the basis for numerous algorithms that enabled the explosion of miRNA functional characterization.
To date over 1000 miRNAs have been identified in Humans, hundreds of which are associated with major biological processes including cell proliferation and differentiation, development and disease. As such miRNAs are arguably one of the most important classes of functional RNAs. However, the rules governing miRNA target recognition are not fully understood and may vary for each miRNA–mRNA pair. Computational approaches that can test various models of miRNA binding and predict target sites are therefore essential to understanding the function of microRNAs.
In this review, we will outline the major concepts underlying the most popular target prediction algorithms, discuss the context in which these algorithms are optimal and provide simple techniques to refine them. A list of readily available miRNA target prediction algorithms and databases is given in Table 1.
Section snippets
Materials and methods
All the techniques and approaches discussed here are available online.
Results
An ouroboric relation has always existed between the computational prediction of miRNA targets and their experimental validation. Experimental approaches are often designed and interpreted by using computational predictions and these in turn have been created and refined based on experimental output. It is therefore difficult to obtain an independent experimental dataset to compare the efficiency of target prediction algorithms and until recently, experiments were not designed to discover novel
Discussion
Experimentalists using miRNA target prediction software for the first time will invariably observe two apparent shortfalls. The first is that target prediction algorithms produce large lists of candidates, many of which cannot currently be validated experimentally. These false positives are generally attributed to a poor modelling of the miRNA–mRNA interaction. This may not always be the case. Recent studies have demonstrated that miRNAs, their guide, and their effector proteins are tightly
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