Experimental identification of miRNA targets is not an easy task, especially using conventional tools. The principal challenge in target recognition of miRNAs is based on the small size of their targets (18-24 nucleotides (nts)). Also, every human miRNA has hundreds of targets with limited complementarity, unlike plant miRNAs. The affinity and specificity required for their recognition requires highly precise tools as the difference between a true target and a false positive might be a single base.
There has been an explosion in computational biology algorithms for human miRNA target prediction. We propose their identification and verification for human miRNAs using a combinatorial approach involving computational and molecular biology. We intend to make extensions to well-established tools and techniques to verify the miRNA targets predicted using the computational methods.
Prediction of miRNA targets provides an alternative approach to assign biological functions. This is simpler in plants due to their high complementarity and limited targets per miRNA but functional duplexes can be more variable in structure in humans [1]. Thus, we propose the use of more than one method to verify these targets.
For accurate and sensitive means to measure the expression levels of miRNAs without need for RNA size fractionation and/or RNA amplification, we intend to optimize RNA preparation protocols, as well as labeling and hybridization protocols.
A Harvard University researcher and pioneer of miRNA research, Gary Ruvkun has called miRNAs "the biological equivalent of dark matter, all around us but almost escaping detection." It has been well-established that miRNAs have a role in cancer development and tissue differentiation. They regulate almost one third of the genes in the human genome [2] although, it is still not known why miRNAs regulate some genes and not others. Some of their other functions include cell proliferation, apoptosis, oncogenesis and anti-viral defense. These previously considered “junk” RNA have implications for the treatment of cancer, diabetes and brain disorders.
The necessity to study these tiny pieces of mRNA also stems from the fact that they comprise 1% of the genes in animals and are highly conserved across the species. The understanding of miRNA function is very limited, which makes even target prediction an extremely challenging task.
Once miRNA targets are known, it might help understand complicated gene regulation, especially in gene networks. It has been shown that genes with higher cis-regulation complexity are more coordinately regulated by transacting factors at the transcriptional level and by miRNAs at the post-transcriptional level [3]. Thus, understanding the miRNA regulation pattern might fill gaps in the studies of gene networks and regulation.
References:
1. Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of microRNA–target recognition. PLoS Biol 3(3): e85.
2. Lewis BP, Burge CB, Bartel DP (2005) Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell 120: 15–20.
3. Cui Q, Yu Z, Pan Y, Purisima EO, Wang E; MicroRNAs preferentially target the genes with high transcriptional regulation complexity; Biochem Biophys Res Commun. 2006 Nov 27
There has been an explosion in computational biology algorithms for human miRNA target prediction. We propose their identification and verification for human miRNAs using a combinatorial approach involving computational and molecular biology. We intend to make extensions to well-established tools and techniques to verify the miRNA targets predicted using the computational methods.
Prediction of miRNA targets provides an alternative approach to assign biological functions. This is simpler in plants due to their high complementarity and limited targets per miRNA but functional duplexes can be more variable in structure in humans [1]. Thus, we propose the use of more than one method to verify these targets.
For accurate and sensitive means to measure the expression levels of miRNAs without need for RNA size fractionation and/or RNA amplification, we intend to optimize RNA preparation protocols, as well as labeling and hybridization protocols.
A Harvard University researcher and pioneer of miRNA research, Gary Ruvkun has called miRNAs "the biological equivalent of dark matter, all around us but almost escaping detection." It has been well-established that miRNAs have a role in cancer development and tissue differentiation. They regulate almost one third of the genes in the human genome [2] although, it is still not known why miRNAs regulate some genes and not others. Some of their other functions include cell proliferation, apoptosis, oncogenesis and anti-viral defense. These previously considered “junk” RNA have implications for the treatment of cancer, diabetes and brain disorders.
The necessity to study these tiny pieces of mRNA also stems from the fact that they comprise 1% of the genes in animals and are highly conserved across the species. The understanding of miRNA function is very limited, which makes even target prediction an extremely challenging task.
Once miRNA targets are known, it might help understand complicated gene regulation, especially in gene networks. It has been shown that genes with higher cis-regulation complexity are more coordinately regulated by transacting factors at the transcriptional level and by miRNAs at the post-transcriptional level [3]. Thus, understanding the miRNA regulation pattern might fill gaps in the studies of gene networks and regulation.
References:
1. Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of microRNA–target recognition. PLoS Biol 3(3): e85.
2. Lewis BP, Burge CB, Bartel DP (2005) Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell 120: 15–20.
3. Cui Q, Yu Z, Pan Y, Purisima EO, Wang E; MicroRNAs preferentially target the genes with high transcriptional regulation complexity; Biochem Biophys Res Commun. 2006 Nov 27
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